Tokushima University / Educator and Researcher Directory --- Haga, Akihiro

Personal Web Page

https://physicistinmedicine.wixsite.com/research

Field of Study

○	Medical Physics

Subject of Study

○	Medical imaging Physics (Medical Image Reconstruction, Image feature extraction, Atomics and Nuclear Physics, Radiation Physics, Machine Learning)

Book / Paper

Book:

1.	Akihiro Haga : Introduction of Radiomics, Oct. 2021.
2.	Motohiro Ishida, Shinki Oyabu, Tetsushi Ueta, Hidekazu Kakei, Kazuhide Kanenishi, Hiroki Tanioka, Shin-ichi Nakayama and Akihiro Haga : 情報科学入門統計・データサイエンス・AI, 技術評論社, Mar. 2021.
3.	Akihiro Haga : 放射線治療AIと外科治療AI, Ohmsha, Ltd., Tokyo, Apr. 2020.
4.	Akihiro Haga : Radiation Biology, 東京大学出版, Tokyo, Mar. 2020.
5.	Hitoshi Ikushima and Akihiro Haga : やさしくわかる放射線治療学(担当:編集，放射線治療物理), 秀潤社, Oct. 2018.
6.	Akihiro Haga : Image-Based Computer-Assisted Radiation Therapy, --- Chapter 9: x-ray based patient positioning ---, Springer, Jan. 2017.
7.	Akihiro Haga : 外部放射線治療におけるQuality Assurance (QA)システムガイドライン, KANEHARA & CO., LTD., Tokyo, Apr. 2016.
8.	Akihiro Haga : Stereotactic Body Radiation Therapy, --- Chapter 18: Others: Four-dimensional Cone-Beam CT during SBRT ---, Springer, Oct. 2015.
9.	Akihiro Haga : 初学者のための原子力・量子・核融合の事典, 丸善出版, Tokyo, Oct. 2014.
10.	Akihiro Haga : 医学物理の理工学下巻, 養賢堂, Tokyo, Aug. 2012.
11.	Akihiro Haga : 医学物理の理工学上巻, 養賢堂, Tokyo, Aug. 2012.
12.	Akihiro Haga : 強度変調放射線治療における物理・技術的ガイドラインの詳細, 株式会社中外医学社, Tokyo, Nov. 2010.

Academic Paper (Judged Full Paper):

1.	Arce P, Archer W J, Arsini L, Bagulya A., Bolst D, Brown M C J, Caccia B, Chacon A, Cirrone A P G, es-Giraldo A Cort M, Cutajar D, Cuttone G, Dondero P, Dotti A, Faddegon B, Fattori S, Fedon C, Guatelli S and Akihiro Haga : Results of a Geant4 benchmarking study for bio-medical applications, performed with the G4Med system, developed by the Geant4 Medical Simulation Benchmarking Group, Medical Physics, 2024.
2.	Yoshihide Sato, Dousatsu Sakata, David Bolst, Edward Simpson, Chacon Andrew, Safavi-Naeini Mitra, Guatelli Susanna and Akihiro Haga : Validation of LightIon Quantum Molecular Dynamics (LIQMD) model for hadron therapy, Physica Medica, Vol.128, No.104850, 2024.
3.	Akita Keitaro, Kusunose Kenya, Akihiro Haga, Shimomura Taisei, Kosaka Yoshitaka, Ishiyama Katsunori, Hasegawa Kohei, Fifer A. Michael, Maurer S. Mathew and Shimada J. Yuichi : Deep learning of echocardiography distinguishes between presence and absence of late gadolinium enhancement on cardiac magnetic resonance in patients with hypertrophic cardiomyopathy, Echo Research & Practice, Vol.11, No.23, 1-10, 2024.
4.	Atsushi TAKEYA, Keiichiro Watanabe and Akihiro Haga : Fine structural human phantom in dentistry and instance tooth segmentation, Scientific Reports, Vol.14, No.12630, 2024. (Summary) In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtual cone-beam computed tomography (CBCT) system, we generated over 170,000 training datasets. These datasets were produced by varying the elemental densities and tooth sizes within the human phantom, as well as varying the X-ray spectrum, noise intensity, and projection cutoff intensity in the virtual CBCT system. The deep-learning (DL) based tooth segmentation model was trained using the generated datasets. The results demonstrate an agreement with manual contouring when applied to clinical CBCT data. Specifically, the Dice similarity coefficient exceeded 0.87, indicating the robust performance of the developed segmentation model even when virtual imaging was used. The present results show the practical utility of virtual imaging techniques in dentistry and highlight the potential of medical computer vision for enhancing precision and efficiency in dental imaging processes. (Keyword) Humans / Tooth / Phantoms, Imaging / Cone-Beam Computed Tomography / Dentistry / Image Processing, Computer-Assisted / Deep Learning (Link to Publication Site) ● Publication site (DOI): 10.1038/s41598-024-63319-x (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 38824210 ● Summary page in Scopus @ Elsevier: 2-s2.0-85195003006 (DOI: 10.1038/s41598-024-63319-x, PubMed: 38824210, Elsevier: Scopus)
5.	Ping Ye, Wei Zhao, Taisei Shimomura, Kai-Wen Li, Akihiro Haga and Li-Sheng Geng : Pixel-by-pixel correction of beam hardening artifacts by bowtie filter in fan-beam CT, Physics in Medicine and Biology, Vol.69, No.9, 2024. (Summary) Beam hardening (BH) artifacts in computed tomography (CT) images originate from the polychromatic nature of x-ray photons. In a CT system with a bowtie filter, residual BH artifacts remain when polynomial fits are used. These artifacts lead to worse visuals, reduced contrast, and inaccurate CT numbers. This work proposes a pixel-by-pixel correction (PPC) method to reduce the residual BH artifacts caused by a bowtie filter.. The energy spectrum for each pixel at the detector after the photons pass through the bowtie filter was calculated. Then, the spectrum was filtered through a series of water slabs with different thicknesses. The polychromatic projection corresponding to the thickness of the water slab for each detector pixel could be obtained. Next, we carried out a water slab experiment with a mono energy= 69 keV to get the monochromatic projection. The polychromatic and monochromatic projections were then fitted with a 2nd-order polynomial. The proposed method was evaluated on digital phantoms in a virtual CT system and phantoms in a real CT machine.. In the case of a virtual CT system, the standard deviation of the line profile was reduced by 23.8%, 37.3%, and 14.3%, respectively, in the water phantom with different shapes. The difference of the linear attenuation coefficients (LAC) in the central and peripheral areas of an image was reduced from 0.010 to 0.003cm-1and 0.007cm-1to 0 in the biological tissue phantom and human phantom, respectively. The method was also validated using CT projection data obtained from Activion16 (Canon Medical Systems, Japan). The difference in the LAC in the central and peripheral areas can be reduced by a factor of two.. The proposed PPC method can successfully remove the cupping artifacts in both virtual and authentic CT images. The scanned object's shapes and materials do not affect the technique. (Keyword) Artifacts / Phantoms, Imaging / Tomography, X-Ray Computed / Image Processing, Computer-Assisted / Humans (Link to Publication Site) ● Publication site (DOI): 10.1088/1361-6560/ad40fa (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 38640915 ● Search Scopus @ Elsevier (PMID): 38640915 ● Search Scopus @ Elsevier (DOI): 10.1088/1361-6560/ad40fa (DOI: 10.1088/1361-6560/ad40fa, PubMed: 38640915)
6.	Ohshiro Ryuji, Yuki Kanazawa, Akihiro Haga and Masafumi Harada : Development of numerical phantom converting from electron microscopic analysis to multi-component water fraction for MRI simulator., Proceedings of ISMRM, Vol.33, 2038, 2024.
7.	Shun Kitano, Yuki Kanazawa, Masafumi Harada, Yo Taniguchi, Hiroaki Hayashi, Yuki Matsumoto, Kosuke Ito, Yoshitaka Bito and Akihiro Haga : Conversion Map from Quantitative Parameter Mapping to Myelin Water Fraction: Comparison with R1·R2* and Myelin Water Fraction in White Matter., Magma, 2024. (Summary) To clarify the relationship between myelin water fraction (MWF) and R⋅R and to develop a method to calculate MWF directly from parameters derived from QPM, i.e., MWF converted from QPM (MWF). Subjects were 12 healthy volunteers. On a 3 T MR scanner, dataset was acquired using spoiled gradient-echo sequence for QPM. MWF and R⋅R maps were derived from the multi-gradient-echo (mGRE) dataset. Volume-of-interest (VOI) analysis using the JHU-white matter (WM) atlas was performed. All the data in the 48 WM regions measured VOI were plotted, and quadratic polynomial approximations of each region were derived from the relationship between R·R and the two-pool model-MWF. The R·R map was converted to MWF map. MWF atlas template was generated using converted to MWF from R·R per WM region. The mean MWF and R·R values for the 48 WM regions were 11.96 ± 6.63%, and 19.94 ± 4.59 s, respectively. A non-linear relationship in 48 regions of the WM between MWF and R·R values was observed by quadratic polynomial approximation (R ≥ 0.963, P < 0.0001). MWF map improved image quality compared to the mGRE-MWF map. Myelin water atlas template derived from MWF may be generated with combined multiple WM regions. (Link to Publication Site) ● Publication site (DOI): 10.1007/s10334-024-01155-w (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 38581455 ● Search Scopus @ Elsevier (PMID): 38581455 ● Search Scopus @ Elsevier (DOI): 10.1007/s10334-024-01155-w (DOI: 10.1007/s10334-024-01155-w, PubMed: 38581455)
8.	Natsuki Ikemitsu, Yuki Kanazawa, Akihiro Haga, Hiroaki Hayashi, Yuki Matsumoto and Masafumi Harada : Determination of Alzheimer's disease based on morphology and atrophy using machine learning combined with automated segmentation, Acta Radiologica, Vol.65, No.4, 359-366, 2024. (Summary) To evaluate the degree of cerebral atrophy for Alzheimer's disease (AD), voxel-based morphometry has been performed with magnetic resonance imaging. Detailed morphological changes in a specific tissue area having the most evidence of atrophy were not considered by the machine-learning technique. To develop a machine-learning system that can capture morphology features for determination of atrophy of brain tissue in early-stage AD and classification of healthy participants or patients. Three-dimensional T1-weighted (3D-T1W) data were obtained from AD Neuroimaging Initiative (200 healthy controls and 200 patients with early-stage AD). Automated segmentation of 3D-T1W data was performed. Deep learning (DL) and support vector machine (SVM) were trained using 66-segmented volume values as input and AD diagnosis as output. DL was performed using 66 volume values or gray matter (GM) and white matter (WM) volume values. SVM learning was performed using 66 volume values and six regions with high variable importance. 3D convolutional neural network (3D-CNN) was trained using the segmented images. Accuracy and area under curve (AUC) were obtained. Variable importance was evaluated from logistic regression analysis. DL for GM and WM volume values, accuracy 0.6; SVM for all volume values, accuracy 0.82 and AUC 0.81; DL for all volume values, accuracy 0.82 and AUC 0.8; 3D-CNN using segmental images of the whole brain, accuracy 0.5 and AUC 0.51. SVM using volume values of six regions, accuracy 0.82; image-based 3D-CNN, highest accuracy 0.69. Our results show that atrophic features are more considerable than morphological features in the early detection of AD. (Keyword) Alzheimer's disease / magnetic resonance imaging / convolutional neural network / deep learning / support vector machine (Link to Publication Site) ● Publication site (DOI): 10.1177/02841851231218384 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 38196180 ● Search Scopus @ Elsevier (PMID): 38196180 ● Search Scopus @ Elsevier (DOI): 10.1177/02841851231218384 (DOI: 10.1177/02841851231218384, PubMed: 38196180)
9.	Daiki Chiba, Yuki Kanazawa, Tosiaki Miyati, Masafumi Harada, Mitsuharu Miyoshi, Hiroaki Hayashi and Akihiro Haga : Simplified assessment for chemical exchanged saturation transfer (CEST) imaging: local offset frequency and CEST effect, Radiological Physics and Technology, Vol.17, No.1, 93-102, 2024. (Summary) The aim of this study is to develop a novel phantom for the evaluation of clinical CEST imaging settings, e.g., B0 and B1 field inhomogeneities, CEST contrast, and post-processing. We made a phantom composed of two slice sections: a grid section for local offset frequency evaluation and a sample section for CEST effect evaluation using different concentrations of an egg white albumin solution. On a 3 Tesla MR scanner, a phantom study was performed using CEST imaging; the mean B1 amplitudes were set at 1.2 and 1.9 µT, and CEST images with and without B0 corrections were acquired. Next, region of interest (ROI) analysis was performed for each slice. Then, CEST images with and without B0 corrections were compared at each B1 amplitude. The B0 corrected Z-spectrums at each local region in the grid section showed a shifting of the curve bottom to 0 ppm. Z-spectrum at B1 = 1.9 µT showed a broader curve shape than that at 1.2 µT. Moreover, MTRasym values at 3.5 ppm for each albumin sample at B1 = 1.9 µT were about two times higher than those at 1.2 µT. Our phantom enabled us to evaluate and optimize B0 inhomogeneity and the CEST effect at the B1 amplitude. (Keyword) Magnetic Resonance Imaging / Phantoms, Imaging / Albumins (Link to Publication Site) ● Publication site (DOI): 10.1007/s12194-023-00752-z (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 37897684 ● Summary page in Scopus @ Elsevier: 2-s2.0-85174963843 (DOI: 10.1007/s12194-023-00752-z, PubMed: 37897684, Elsevier: Scopus)
10.	Akinari Kasai, Jinsei Miyoshi, Yasushi Sato, Koichi Okamoto, Hiroshi Miyamoto, Takashi Kawanaka, Chisato Tonoiso, Masafumi Harada, Masakazu Goto, Takahiro Yoshida, Akihiro Haga and Tetsuji Takayama : A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma., Scientific Reports, Vol.14, No.1, 2039, 2024. (Summary) No clinically relevant biomarker has been identified for predicting the response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT). Herein, we established a CT-based radiomics model with artificial intelligence (AI) to predict the response and prognosis of CRT in ESCC. A total of 44 ESCC patients (stage I-IV) were enrolled in this study; training (n = 27) and validation (n = 17) cohorts. First, we extracted a total of 476 radiomics features from three-dimensional CT images of cancer lesions in training cohort, selected 110 features associated with the CRT response by ROC analysis (AUC ≥ 0.7) and identified 12 independent features, excluding correlated features by Pearson's correlation analysis (r ≥ 0.7). Based on the 12 features, we constructed 5 prediction models of different machine learning algorithms (Random Forest (RF), Ridge Regression, Naive Bayes, Support Vector Machine, and Artificial Neural Network models). Among those, the RF model showed the highest AUC in the training cohort (0.99 [95%CI 0.86-1.00]) as well as in the validation cohort (0.92 [95%CI 0.71-0.99]) to predict the CRT response. Additionally, Kaplan-Meyer analysis of the validation cohort and all the patient data showed significantly longer progression-free and overall survival in the high-prediction score group compared with the low-prediction score group in the RF model. Univariate and multivariate analyses revealed that the radiomics prediction score and lymph node metastasis were independent prognostic biomarkers for CRT of ESCC. In conclusion, we have developed a CT-based radiomics model using AI, which may have the potential to predict the CRT response as well as the prognosis for ESCC patients with non-invasiveness and cost-effectiveness. (Keyword) Humans / Esophageal Squamous Cell Carcinoma / Artificial Intelligence / Bayes Theorem / Esophageal Neoplasms / Radiomics / Prognosis / Chemoradiotherapy / Tomography, X-Ray Computed (Tokushima University Institutional Repository) ● Metadata: 119113 (Link to Publication Site) ● Publication site (DOI): 10.1038/s41598-024-52418-4 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 38263395 ● Search Scopus @ Elsevier (PMID): 38263395 ● Search Scopus @ Elsevier (DOI): 10.1038/s41598-024-52418-4 (Tokushima University Institutional Repository: 119113, DOI: 10.1038/s41598-024-52418-4, PubMed: 38263395)
11.	Akihiro Haga : Quantum annealing-based computed tomography using variational approach for a real-number image reconstruction, Physics in Medicine and Biology, Vol.69, No.4, 2024. (Summary) Despite recent advancements in quantum computing, the limited number of available qubits has hindered progress in CT reconstruction. This study investigates the feasibility of utilizing quantum annealing-based computed tomography (QACT) with current quantum bit levels.. The QACT algorithm aims to precisely solve quadratic unconstrained binary optimization problems. Furthermore, a novel approach is proposed to reconstruct images by approximating real numbers using the variational method. This approach allows for accurate CT image reconstruction using a small number of qubits. The study examines the impact of projection data quantity and noise on various image sizes ranging from 4 × 4 to 24 × 24 pixels. The reconstructed results are compared against conventional reconstruction algorithms, namely maximum likelihood expectation maximization (MLEM) and filtered back projection (FBP).. By employing the variational approach and utilizing two qubits for each pixel of the image, accurate reconstruction was achieved with an adequate number of projections. Under conditions of abundant projections and lower noise levels, the image quality in QACT algorithm outperformed that of MLEM and FBP algorithms. However, in situations with limited projection data and in the presence of noise, the image quality in QACT was inferior to that in MLEM.. This study developed the QACT reconstruction algorithm using the variational approach for real-number reconstruction. Remarkably, only 2 qubits were required for each pixel representation, demonstrating their sufficiency for accurate reconstruction. (Keyword) Computing Methodologies / Phantoms, Imaging / Quantum Theory / Tomography, X-Ray Computed / Algorithms / Image Processing, Computer-Assisted (Link to Publication Site) ● Publication site (DOI): 10.1088/1361-6560/ad2155 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 38252994 ● Summary page in Scopus @ Elsevier: 2-s2.0-85184668221 (DOI: 10.1088/1361-6560/ad2155, PubMed: 38252994, Elsevier: Scopus)
12.	Yuki Kanazawa, Natsuki Ikemitsu, Yuki Kinjyo, Masafumi Harada, Hiroaki Hayashi, Yo Taniguchi, Kosuke Ito, Yoshitaka Bito, Yuki Matsumoto and Akihiro Haga : Differences of white matter structure for diffusion kurtosis imaging using voxel-based morphometry and connectivity analysis, BJR Open, Vol.6, No.1, 1-7, 2024. (Summary) In a clinical study, diffusion kurtosis imaging (DKI) has been used to visualize and distinguish white matter (WM) structures' details. The purpose of our study is to evaluate and compare the diffusion tensor imaging (DTI) and DKI parameter values to obtain WM structure differences of healthy subjects. Thirteen healthy volunteers (mean age, 25.2 years) were examined in this study. On a 3-T MRI system, diffusion dataset for DKI was acquired using an echo-planner imaging sequence, and T-weghted (Tw) images were acquired. Imaging analysis was performed using Functional MRI of the brain Software Library (FSL). First, registration analysis was performed using the Tw of each subject to MNI152. Second, DTI (eg, fractional anisotropy [FA] and each diffusivity) and DKI (eg, mean kurtosis [MK], radial kurtosis [RK], and axial kurtosis [AK]) datasets were applied to above computed spline coefficients and affine matrices. Each DTI and DKI parameter value for WM areas was compared. Finally, tract-based spatial statistics (TBSS) analysis was performed using each parameter. The relationship between FA and kurtosis parameters (MK, RK, and AK) for WM areas had a strong positive correlation (FA-MK, = 0.93; FA-RK, = 0.89) and a strong negative correlation (FA-AK, = 0.92). When comparing a TBSS connection, we found that this could be observed more clearly in MK than in RK and FA. WM analysis with DKI enable us to obtain more detailed information for connectivity between nerve structures. Quantitative indices of neurological diseases were determined using segmenting WM regions using voxel-based morphometry processing of DKI images. (Link to Publication Site) ● Publication site (DOI): 10.1093/bjro/tzad003 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 38352183 ● Search Scopus @ Elsevier (PMID): 38352183 ● Search Scopus @ Elsevier (DOI): 10.1093/bjro/tzad003 (DOI: 10.1093/bjro/tzad003, PubMed: 38352183)
13.	Taisei Shimomura, Daiyu Fujiwara, Yuki Inoue, Atsushi Takeya, Takeshi Ohta, Toshikazu Imae, Yuki Nozawa, Kanabu Nawa, Keiichi Nakagawa and Akihiro Haga : Virtual cone-beam computed tomography simulator with human phantom library and its application to the elemental material decomposition, Physica Medica, Vol.113, 102648, 2023. (Summary) The purpose of this study is to develop a virtual CBCT simulator with a head and neck (HN) human phantom library and to demonstrate the feasibility of elemental material decomposition (EMD) for quantitative CBCT imaging using this virtual simulator. The library of 36 HN human phantoms were developed by extending the ICRP 110 adult phantoms based on human age, height, and weight statistics. To create the CBCT database for the library, a virtual CBCT simulator that simulated the direct and scattered X-ray on a flat panel detector using ray-tracing and deep-learning (DL) models was used. Gaussian distributed noise was also included on the flat panel detector, which was evaluated using a real CBCT system. The usefulness of the virtual CBCT system was demonstrated through the application of the developed DL-based EMD model for case involving virtual phantom and real patient. The virtual simulator could generate various virtual CBCT images based on the human phantom library, and the prediction of the EMD could be successfully performed by preparing the CBCT database from the proposed virtual system, even for a real patient. The CBCT image degradation owing to the scattered X-ray and the statistical noise affected the prediction accuracy, although these effects were minimal. Furthermore, the elemental distribution using the real CBCT image was also predictable. This study demonstrated the potential of using computer vision for medical data preparation and analysis, which could have important implications for improving patient outcomes, especially in adaptive radiation therapy. (Keyword) Adult / Humans / Phantoms, Imaging / Cone-Beam Computed Tomography / Databases, Factual / Head / Neck (Tokushima University Institutional Repository) ● Metadata: 119336 (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ejmp.2023.102648 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 37672845 ● Summary page in Scopus @ Elsevier: 2-s2.0-85170075973 (Tokushima University Institutional Repository: 119336, DOI: 10.1016/j.ejmp.2023.102648, PubMed: 37672845, Elsevier: Scopus)
14.	Natsumi Yamaguchi, Yoshitaka Kosaka, Akihiro Haga, Masataka Sata and Kenya Kusunose : Artificial intelligence-assisted interpretation of systolic function by echocardiogram, Open Heart, Vol.10, No.2, e002287., 2023. (Summary) Precise and reliable echocardiographic assessment of left ventricular ejection fraction (LVEF) is needed for clinical decision-making. Recently, artificial intelligence (AI) models have been developed to estimate LVEF accurately. The aim of this study was to evaluate whether an AI model could estimate an expert read of LVEF and reduce the interinstitutional variability of level 1 readers with the AI-LVEF displayed on the echocardiographic screen. This prospective, multicentre echocardiographic study was conducted by five cardiologists of level 1 echocardiographic skill (minimum level of competency to interpret images) from different hospitals. Protocol 1: Visual LVEFs for the 48 cases were measured without input from the AI-LVEF. Protocol 2: the 48 cases were again shown to all readers with inclusion of AI-LVEF data. To assess the concordance and accuracy with or without AI-LVEF, each visual LVEF measurement was compared with an average of the estimates by five expert readers as a reference. A good correlation was found between AI-LVEF and reference LVEF (r=0.90, p<0.001) from the expert readers. For the classification LVEF, the area under the curve was 0.95 on heart failure with preserved EF and 0.96 on heart failure reduced EF. For the precision, the SD was reduced from 6.1±2.3 to 2.5±0.9 (p<0.001) with AI-LVEF. For the accuracy, the root-mean squared error was improved from 7.5±3.1 to 5.6±3.2 (p=0.004) with AI-LVEF. AI can assist with the interpretation of systolic function on an echocardiogram for level 1 readers from different institutions. (Keyword) Humans / Ventricular Function, Left / Stroke Volume / Artificial Intelligence / Prospective Studies / Echocardiography / Heart Failure (Tokushima University Institutional Repository) ● Metadata: 118454 (Link to Publication Site) ● Publication site (DOI): 10.1136/openhrt-2023-002287 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 37460267 ● Summary page in Scopus @ Elsevier: 2-s2.0-85165601733 (Tokushima University Institutional Repository: 118454, DOI: 10.1136/openhrt-2023-002287, PubMed: 37460267, Elsevier: Scopus)
15.	Takayuki Higuchi and Akihiro Haga : X-ray energy spectrum estimation based on a virtual computed tomography system, Biomedical Physics & Engineering Express, 025002, 2023. (Tokushima University Institutional Repository) ● Metadata: 117913 (Link to Publication Site) ● Publication site (DOI): 10.1088/2057-1976/acb158 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1088/2057-1976/acb158 (Tokushima University Institutional Repository: 117913, DOI: 10.1088/2057-1976/acb158)
16.	Yuki Kanazawa, KITANO Shun, Masafumi Harada, Yo Taniguchi, Yuki Matsumoto, Hiroaki Hayashi, Kosuke Ito, Yoshitaka Bito and Akihiro Haga : Myelin Water Atlas Template Derived from Quantitative Parameter Mapping, Proceedings of ISMRM, Vol.32, No.2630, 2023.
17.	Yuki Kanazawa, Masafumi Harada, Yo Taniguchi, Hiroaki Hayashi, Takashi Abe, Maki Ohtomo, Yuki Matsumoto, Masaharu Ono, Kosuke Ito, Yoshitaka Bito and Akihiro Haga : Myelin-weighted imaging derived from quantitative parameter mapping, European Journal of Radiology, Vol.156, No.110525, 1-9, 2022. (Summary) We developed a novel method which is applicable to visualize contrast according to myelin components in the human brain using relaxation time derived from quantitative parameter mapping magnetic resonance imaging (QPM-MRI). Using healthy volunteer data (n = 10), we verified that our method demonstrated that the myelin-weighted contrast increased proportionally by products R and R, i.e., QPM-myelin-weighted image, in which modified T-weighted/T-weighted (Tw/Tw) ratio mapping method was applied. We compared measurement values in white matter (WM) and gray matter (GM) regions of the Tw/Tw ratio and R·R product maps of healthy volunteers. Linear regression analysis between each value. Mann Whitney U test between WM and GM signals in each myelin map. In addition, Additionally, QPM-myelin-weighted image was applied to a 32-year-old female MS patient. Linear regression analysis showed a highly significant correlation between conventional Tw/Tw ratios and R·R* products derived from QPM (R = 0.73, P < 0.0001). Moreover, there is a significant difference between WM and GM structures in each myelin images (both, P < 0.0001). Additionally, in a clinical case, MS lesions enabled observation of not only MS plaques but also heterogeneous myelin signal loss associated with demyelination more clearly than Tw image and conventional Tw/Tw ratio image. Our myelin-weighted imaging technique using QPM may be useful for myelin visualization and is expected to become independent of measurement conditions due to having quantitative characteristics of QPM itself. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ejrad.2022.110525 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 36166986 ● Search Scopus @ Elsevier (PMID): 36166986 ● Search Scopus @ Elsevier (DOI): 10.1016/j.ejrad.2022.110525 (DOI: 10.1016/j.ejrad.2022.110525, PubMed: 36166986)
18.	Yoshihide Sato, Dousatsu Sakata, David Bolst, Edward Simpson, Susanna Guatelli and Akihiro Haga : Development of a more accurate Geant4 quantum molecular dynamics model for hadron therapy, Physics in Medicine and Biology, Vol.67, No.15, 225001, 2022. (Summary) Objective: Although in heavy-ion therapy, the quantum molecular dynamics (QMD) model is one of the most fundamental physics models providing an accurate daughter- ion production yield in the final state, there are still non-negligible differences with the experimental results. The aim of this study is to improve fragment production in water phantoms by developing a more accurate QMD model in Geant4.Approach: A QMD model was developed by implementing modern Skyrme interaction parameter sets, as well as by incorporating with an ad hoc α-cluster model in the initial nuclear state. Two adjusting parameters were selected that can significantly affect the fragment productions in the QMD model: the radius to discriminate a cluster to which nucleons belong after the nucleus-nucleus reaction, denoted by R, and the squared standard deviation of the Gaussian packet, denoted by L. Squared Mahalanobiss distance of fragment yields and angular distributions with 1, 2, and the higher atomic number for the produced fragments were employed as objective functions, and multi- objective optimization (MOO), which make it possible to compare quantitatively the simulated production yields with the reference experimental data, was performed. Main results: The MOO analysis showed that the QMD model with modern Skyrme parameters coupled with the proposed α-cluster model, denoted as SkMα, can drastically improve light fragments yields in water. In addition, the proposed model reproduced the kinetic energy distribution of the fragments accurately. The optimized L in SkMα was confirmed to be realistic by the charge radii analysis in the ground state formation.Significance: The proposed framework using MOO was demonstrated to be very useful in judging the superiority of the proposed nuclear model. The optimized QMD model is expected to improve the accuracy of heavy-ion therapy dosimetry. (Keyword) Phantoms, Imaging (Tokushima University Institutional Repository) ● Metadata: 117596 (Link to Publication Site) ● Publication site (DOI): 10.1088/1361-6560/ac9a9a (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 36240757 ● Search Scopus @ Elsevier (PMID): 36240757 ● Search Scopus @ Elsevier (DOI): 10.1088/1361-6560/ac9a9a (Tokushima University Institutional Repository: 117596, DOI: 10.1088/1361-6560/ac9a9a, PubMed: 36240757)
19.	Seto Hiroe, Oyama Asuka, Kitora Shuji, Toki Hiroshi, Yamamoto Ryohei, Akihiro Haga, Shinzawa Maki, Yamakawa Miyae, Fukui Sakiko and Moriyama Toshiki : Gradient Boosting Decision Tree Becomes More Reliable Than Logistic Regression in Predicting Probability for Diabetes With Big Data, Scientific Reports, 2022. (Summary) We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree (GBDT) and logistic regression (LR) models using data obtained from the Kokuho-database of the Osaka prefecture, Japan. To develop the models, we focused on 16 predictors from health checkup data from April 2013 to December 2014. A total of 277,651 eligible participants were studied. The prediction models were developed using a light gradient boosting machine (LightGBM), which is an effective GBDT implementation algorithm, and LR. Their reliabilities were measured based on expected calibration error (ECE), negative log-likelihood (Logloss), and reliability diagrams. Similarly, their classification accuracies were measured in the area under the curve (AUC). We further analyzed their reliabilities while changing the sample size for training. Among the 277,651 participants, 15,900 (7978 males and 7922 females) were newly diagnosed with diabetes within 3 years. LightGBM (LR) achieved an ECE of 0.0018 ± 0.00033 (0.0048 ± 0.00058), a Logloss of 0.167 ± 0.00062 (0.172 ± 0.00090), and an AUC of 0.844 ± 0.0025 (0.826 ± 0.0035). From sample size analysis, the reliability of LightGBM became higher than LR when the sample size increased more than [Formula: see text]. Thus, we confirmed that GBDT provides a more reliable model than that of LR in the development of diabetes prediction models using big data. ML could potentially produce a highly reliable diabetes prediction model, a helpful tool for improving lifestyle and preventing diabetes. (Keyword) Big Data / Decision Trees / Diabetes Mellitus / Female / Humans / Logistic Models / Male / Reproducibility of Results (Tokushima University Institutional Repository) ● Metadata: 117852 (Link to Publication Site) ● Publication site (DOI): 10.1038/s41598-022-20149-z (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 36220875 ● Search Scopus @ Elsevier (PMID): 36220875 ● Search Scopus @ Elsevier (DOI): 10.1038/s41598-022-20149-z (Tokushima University Institutional Repository: 117852, DOI: 10.1038/s41598-022-20149-z, PubMed: 36220875)
20.	Daiyu Fujiwara, Taisei Shimomura, Wei Zhao, Kai-wen Li, Akihiro Haga and Li-sheng Geng : Virtual computed-tomography system for deep-learning-based material decomposition, Physics in Medicine and Biology, Vol.67, No.15, 155008, 2022. (Summary) Objective: Material decomposition (MD) evaluates the elemental composition of human tissues and organs via computed tomography (CT) and is indispensable in correlating anatomical images with functional ones. A major issue in MD is inaccurate elemental information about the real human body. To overcome this problem, we developed a virtual CT system model, by which various reconstructed images can be generated based on ICRP110 human phantoms with information about six major elements (H, C, N, O, P, and Ca).Approach: We generated CT datasets labelled with accurate elemental information using the proposed generative CT model and trained a deep learning (DL)-based model to estimate the material distribution with the ICRP110 based human phantom as well as the digital SheppLogan phantom. The accuracy in quad-, dual-, and single-energy CT cases was investigated. The influence of beam-hardening artefacts, noise, and spectrum variations were analysed with testing datasets including elemental density and anatomical shape variations.Main results: The results indicated that this DL approach can realise precise MD, even with single-energy CT images. Moreover, noise, beam-hardening artefacts, and spectrum variations were shown to have minimal impact on the MD. Significance: Present results suggest that the difficulty to prepare a large CT database can be solved by introducing the virtual CT system and the proposed technique can be applied to clinical radiodiagnosis and radiotherapy. (Keyword) Artifacts / Deep Learning / Humans / Image Processing, Computer-Assisted / Phantoms, Imaging / Tomography, X-Ray Computed (Tokushima University Institutional Repository) ● Metadata: 117595 (Link to Publication Site) ● Publication site (DOI): 10.1088/1361-6560/ac7bcd (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 35738247 ● Search Scopus @ Elsevier (PMID): 35738247 ● Search Scopus @ Elsevier (DOI): 10.1088/1361-6560/ac7bcd (Tokushima University Institutional Repository: 117595, DOI: 10.1088/1361-6560/ac7bcd, PubMed: 35738247)
21.	Natsuki Ikemitsu, Yuki Kanazawa, Masafumi Harada, Yuki Matsumoto, Hiroaki Hayashi, Kosuke Ito, Yo Taniguchi, Yoshitaka Bito and Akihiro Haga : Determination of white matter structure index for voxel basedmorphometry and connectivity analysis., European Congress of Radiology (EPOS), 10, 2022.
22.	Nagomi Fukuda, Yuki Kanazawa, Hiroaki Hayashi, Yuki Matsumoto, Masafumi Harada, Motoharu Sasaki and Akihiro Haga : Development of a B1 correction method without additional scanning VFA T1 map., European Congress of Radiology (EPOS), 10, 2022.
23.	Sho Ozaki, Shizuo Kaji, Kanabu Nawa, Toshikazu Imae, Atsushi Aoki, Takahiro Nakamoto, Takeshi Ohta, Yuki Nozawa, Hideomi Yamashita, Akihiro Haga and Keiichi Nakagawa : Training of deep cross-modality conversion models with a small dataset, and their application in megavoltage CT to kilovoltage CT conversion, Medical Physics, Vol.49, No.5, 2022. (Summary) In recent years, deep learning-based image processing has emerged as a valuable tool for medical imaging owing to its high performance. However, the quality of deep learning-based methods heavily relies on the amount of training data; the high cost of acquiring a large data set is a limitation to their utilization in medical fields. Herein, based on deep learning, we developed a computed tomography (CT) modality conversion method requiring only a few unsupervised images. The proposed method is based on cycle-consistency generative adversarial network (CycleGAN) with several extensions tailored for CT images, which aims at preserving the structure in the processed images and reducing the amount of training data. This method was applied to realize the conversion of megavoltage computed tomography (MVCT) to kilovoltage computed tomography (kVCT) images. Training was conducted using several data sets acquired from patients with head and neck cancer. The size of the data sets ranged from 16 slices (two patients) to 2745 slices (137 patients) for MVCT and 2824 slices (98 patients) for kVCT. The required size of the training data was found to be as small as a few hundred slices. By statistical and visual evaluations, the quality improvement and structure preservation of the MVCT images converted by the proposed model were investigated. As a clinical benefit, it was observed by medical doctors that the converted images enhanced the precision of contouring. We developed an MVCT to kVCT conversion model based on deep learning, which can be trained using only a few hundred unpaired images. The stability of the model against changes in data size was demonstrated. This study promotes the reliable use of deep learning in clinical medicine by partially answering commonly asked questions, such as "Is our data sufficient?" and "How much data should we acquire?" (Link to Publication Site) ● Publication site (DOI): 10.1002/mp.15626 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 35315529 ● Search Scopus @ Elsevier (PMID): 35315529 ● Search Scopus @ Elsevier (DOI): 10.1002/mp.15626 (DOI: 10.1002/mp.15626, PubMed: 35315529)
24.	Nagomi Fukuda, Yuki Kanazawa, Hiroaki Hayashi, Yuki Matsumoto, Masafumi Harada, Motoharu Sasaki and Akihiro Haga : Development of self-calibrating B1 correction for three-dimensional variable flip angle T1 mapping, Proceedings of ISMRM, Vol.31, No.3218, 2022.
25.	Shun Kitano, Yuki Kanazawa, Masafumi Harada, Yo Taniguchi, Yuki Matsumoto, Hiroaki Hayashi, Kosuke Ito, Yoshitaka Bito and Akihiro Haga : Conversion map from quantitative parameter mapping to myelin water fraction, Proceedings of ISMRM, Vol.31, No.3052, 2022.
26.	Natsuki Ikemitsu, Yuki Kanazawa, Akihiro Haga, Hiroaki Hayashi, Yuki Matsumoto and Masafumi Harada : Determination of Alzheimer's disease based on morphology and atrophy using machine learning combined with automated segmentation., Proceedings of ISMRM, Vol.31, No.3263, 2022.
27.	Takaharu Kudoh, Akihiro Haga, Keiko Kudoh, Akira Takahashi, Motoharu Sasaki, Yasusei Kudo, Hitoshi Ikushima and Youji Miyamoto : Radiomics analysis of [18F]-fluoro-2-deoxyglucose positron emission tomography for the prediction of cervical lymph node metastasis in tongue squamous cell carcinoma, Oral Radiology, 2022. (Summary) This study aimed to create a predictive model for cervical lymph node metastasis (CLNM) in patients with tongue squamous cell carcinoma (SCC) based on radiomics features detected by [F]-fluoro-2-deoxyglucose (F-FDG) positron emission tomography (PET). A total of 40 patients with tongue SCC who underwent F-FDG PET imaging during their first medical examination were enrolled. During the follow-up period (mean 28 months), 20 patients had CLNM, including six with late CLNM, whereas the remaining 20 patients did not have CLNM. Radiomics features were extracted from F-FDG PET images of all patients irrespective of metal artifact, and clinicopathological factors were obtained from the medical records. Late CLNM was defined as the CLNM that occurred after major treatment. The least absolute shrinkage and selection operator (LASSO) model was used for radiomics feature selection and sequential data fitting. The receiver operating characteristic curve analysis was used to assess the predictive performance of the F-FDG PET-based model and clinicopathological factors model (CFM) for CLNM. Six radiomics features were selected from LASSO analysis. The average values of the area under the curve (AUC), accuracy, sensitivity, and specificity of radiomics analysis for predicting CLNM from F-FDG PET images were 0.79, 0.68, 0.65, and 0.70, respectively. In contrast, those of the CFM were 0.54, 0.60, 0.60, and 0.60, respectively. The F-FDG PET-based model showed significantly higher AUC than that of the CFM. The F-FDG PET-based model has better potential for diagnosing CLNM and predicting late CLNM in patients with tongue SCC than the CFM. (Tokushima University Institutional Repository) ● Metadata: 116770 (Link to Publication Site) ● Publication site (DOI): 10.1007/s11282-022-00600-7 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 35254609 ● Summary page in Scopus @ Elsevier: 2-s2.0-85125757133 (Tokushima University Institutional Repository: 116770, DOI: 10.1007/s11282-022-00600-7, PubMed: 35254609, Elsevier: Scopus)
28.	Hitoshi Ikushima, Akihiro Haga, Ken Ando, Shingo Kato, Yuko Kaneyasu, Takashi Uno, Noriyuki Okonogi, Kenji Yoshida, Takuro Ariga, Fimiaki Isohashi, Yoko Harima, Ayae Kanemoto, Noriko Ii, Masaru Wakatsuki and Tatsuya Ohno : Prediction of out-of-field recurrence after chemoradiotherapy for cervical cancer using a combination model of clinical parameters and magnetic resonance imaging radiomics: a multi-institutional study of the Japanese Radiation Oncology Study Group, Journal of Radiation Research, 2021. (Keyword) Radiomics / radiation therapy / Cervical cancer / Out of field recurrence (Tokushima University Institutional Repository) ● Metadata: 118065 (Link to Publication Site) ● Publication site (DOI): 10.1093/jrr/rrab104 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1093/jrr/rrab104 (Tokushima University Institutional Repository: 118065, DOI: 10.1093/jrr/rrab104)
29.	Masahide Tominaga, Yukari Nagayasu, Motoharu Sasaki, Furuta Takuya, Hiroaki Hayashi, Masataka Oita, Yuichi Nishiyama and Akihiro Haga : Influence of distant scatterer on air kerma measurement in the evaluation of diagnostic X-rays using Monte Carlo simulation, Radiological Physics and Technology, Vol.14, No.4, 381-389, 2021. (Summary) The evaluation of the entrance surface dose (ESD) ensures safe radiation doses for X-ray imaging patients. The air kerma free-in-air value used to estimate ESD may be affected by those X-rays that scatter from the scatterer placed behind the chamber at the time of measurement, thereby leading to assessment errors. Therefore, the influence of scattered radiation on air kerma measurements was investigated. Monte Carlo simulations were performed for various detector-to-scatterer distances and scatterer materials. The simulation results were compared with actual measurements to confirm the simulation accuracy. The source-chamber distance was set to 50 and 100 cm for the experimental measurements and simulation, respectively, and the chamber-scatterer distance was varied. The Monte Carlo simulation results reproduced the actual measurements with an accuracy of 3.5%. The effect of backscattering varied with the tube voltage and irradiation field size. The effect was observed in the order of prominence for the following scatterer materials: water-equivalent phantom, acrylic, concrete, lead, and iron. Furthermore, this effect decreased exponentially with increasing chamber-scatterer distance. For a field size of 10 × 10 cm, the finite-distance backscatter factor decreased with an increasing chamber-scatterer distance for all materials. The cause of backscattering in diagnostic X-ray energy regions differs depending on the scatterer material, as well as the photon energy and field size. Backscattering decreases exponentially as the distance between the detector and scatterer increases. (Keyword) Air kerma / Backscattering / Dosimetry / Monte Carlo simulation (Link to Publication Site) ● Publication site (DOI): 10.1007/s12194-021-00641-3 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 34716568 ● Search Scopus @ Elsevier (PMID): 34716568 ● Search Scopus @ Elsevier (DOI): 10.1007/s12194-021-00641-3 (DOI: 10.1007/s12194-021-00641-3, PubMed: 34716568)
30.	Kai-Wen Li, Daiyu Fujiwara, Akihiro Haga, Huisheng Liu and Li-Sheng Geng : Physical density estimations of single- and dual-energy CT using material-based forward projection algorithm: a simulation study., The British Journal of Radiology, 2021. (Summary) Objectives:This study aims to evaluate the accuracy of physical density prediction in single-energy CT (SECT) and dual-energy CT (DECT) by adapting a fully simulation-based method using a material-based forward projection algorithm (MBFPA).Methods:We used biological tissues referenced in ICRU Report 44 and tissue substitutes to prepare three different types of phantoms for calibrating the Hounsfield unit (HU)-to-density curves. Sinograms were first virtually generated by the MBFPA with four representative energy spectra (i.e. 80 kVp, 100 kVp, 120 kVp, and 6 MVp) and then reconstructed to form realistic CT images by adding statistical noise. The HU-to-density curves in each spectrum and their pairwise combinations were derived from the CT images. The accuracy of these curves was validated using the ICRP110 human phantoms.Results:The relative mean square errors (RMSEs) of the physical density by the HU-to-density curves calibrated with kV SECT nearly presented no phantom size dependence. The kVkV DECT calibrated curves were also comparable with those from the kV SECT. The phantom size effect became notable when the MV X-ray beams were employed for both SECT and DECT due to beam-hardening effects. The RMSEs were decreased using the biological tissue phantom.Conclusion:Simulation-based density prediction can be useful in the theoretical analysis of SECT and DECT calibrations. The results of this study indicated that the accuracy of SECT calibration is comparable with that of DECT using biological tissues. The size and shape of the calibration phantom could affect the accuracy, especially for MV CT calibrations.Advances in knowledge:The present study is based on a full simulation environment, which accommodates various situations such as SECT, kVkV DECT, and even kVMV DECT. In this paper, we presented the advances pertaining to the accuracy of the physical density prediction when applied to SECT and DECT in the MV X-ray energy range. To the best of our knowledge, this study is the first to validate the physical density estimation both in SECT and DECT using human-type phantoms. (Link to Publication Site) ● Publication site (DOI): 10.1259/bjr.20201236 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 34541866 ● Search Scopus @ Elsevier (PMID): 34541866 ● Search Scopus @ Elsevier (DOI): 10.1259/bjr.20201236 (DOI: 10.1259/bjr.20201236, PubMed: 34541866)
31.	Yohsuke Kanoh, Hitoshi Ikushima, Motoharu Sasaki and Akihiro Haga : Automatic Contour Segmentation of Cervical Cancer using Artificial Intelligence, Journal of Radiation Research, Vol.62, No.5, 934-944, 2021. (Summary) In cervical cancer treatment, radiation therapy is selected based on the degree of tumor progression, and radiation oncologists are required to delineate tumor contours. To reduce the burden on radiation oncologists, an automatic segmentation of the tumor contours would prove useful. To the best of our knowledge, automatic tumor contour segmentation has rarely been applied to cervical cancer treatment. In this study, diffusion-weighted images (DWI) of 98 patients with cervical cancer were acquired. We trained an automatic tumor contour segmentation model using 2D U-Net and 3D U-Net to investigate the possibility of applying such a model to clinical practice. A total of 98 cases were employed for the training, and they were then predicted by swapping the training and test images. To predict tumor contours, six prediction images were obtained after six training sessions for one case. The six images were then summed and binarized to output a final image through automatic contour segmentation. For the evaluation, the Dice similarity coefficient (DSC) and Hausdorff distance (HD) was applied to analyze the difference between tumor contour delineation by radiation oncologists and the output image. The DSC ranged from 0.13 to 0.93 (median 0.83, mean 0.77). The cases with DSC <0.65 included tumors with a maximum diameter < 40 mm and heterogeneous intracavitary concentration due to necrosis. The HD ranged from 2.7 to 9.6 mm (median 4.7 mm). Thus, the study confirmed that the tumor contours of cervical cancer can be automatically segmented with high accuracy. (Keyword) cervical cancer / automatic tumor contour segmentation / radiation therapy / diffusion-weighted imaging / Dice similarity coefficient (Tokushima University Institutional Repository) ● Metadata: 116749 (Link to Publication Site) ● Publication site (DOI): 10.1093/jrr/rrab070 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 34401914 ● Search Scopus @ Elsevier (PMID): 34401914 ● Search Scopus @ Elsevier (DOI): 10.1093/jrr/rrab070 (Tokushima University Institutional Repository: 116749, DOI: 10.1093/jrr/rrab070, PubMed: 34401914)
32.	Sae X. Morita, Kenya Kusunose, Akihiro Haga, Masataka Sata, Kohei Hasegawa, Yoshihiko Raita, Muredach P. Reilly, Michael A. Fifer, Mathew S. Maurer and Yuichi J. Shimada : Deep Learning Analysis of Echocardiographic Images to Predict Positive Genotype in Patients With Hypertrophic Cardiomyopathy., Frontiers in Cardiovascular Medicine, Vol.8, 669860, 2021. (Summary) Genetic testing provides valuable insights into family screening strategies, diagnosis, and prognosis in patients with hypertrophic cardiomyopathy (HCM). On the other hand, genetic testing carries socio-economical and psychological burdens. It is therefore important to identify patients with HCM who are more likely to have positive genotype. However, conventional prediction models based on clinical and echocardiographic parameters offer only modest accuracy and are subject to intra- and inter-observer variability. We therefore hypothesized that deep convolutional neural network (DCNN, a type of deep learning) analysis of echocardiographic images improves the predictive accuracy of positive genotype in patients with HCM. In each case, we obtained parasternal short- and long-axis as well as apical 2-, 3-, 4-, and 5-chamber views. We employed DCNN algorithm to predict positive genotype based on the input echocardiographic images. We performed 5-fold cross-validations. We used 2 reference models-the Mayo HCM Genotype Predictor score (Mayo score) and the Toronto HCM Genotype score (Toronto score). We compared the area under the receiver-operating-characteristic curve (AUC) between a combined model using the reference model plus DCNN-derived probability and the reference model. We calculated the -value by performing 1,000 bootstrapping. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, we examined the net reclassification improvement. We included 99 adults with HCM who underwent genetic testing. Overall, 45 patients (45%) had positive genotype. The new model combining Mayo score and DCNN-derived probability significantly outperformed Mayo score (AUC 0.86 [95% CI 0.79-0.93] vs. 0.72 [0.61-0.82]; < 0.001). Similarly, the new model combining Toronto score and DCNN-derived probability exhibited a higher AUC compared to Toronto score alone (AUC 0.84 [0.76-0.92] vs. 0.75 [0.65-0.85]; = 0.03). An improvement in the sensitivity, specificity, PPV, and NPV was also achieved, along with significant net reclassification improvement. In conclusion, compared to the conventional models, our new model combining the conventional and DCNN-derived models demonstrated superior accuracy to predict positive genotype in patients with HCM. (Link to Publication Site) ● Publication site (DOI): 10.3389/fcvm.2021.669860 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 34513940 ● Search Scopus @ Elsevier (PMID): 34513940 ● Search Scopus @ Elsevier (DOI): 10.3389/fcvm.2021.669860 (DOI: 10.3389/fcvm.2021.669860, PubMed: 34513940)
33.	Kai-Wen Li, Daiyu Fujiwara, Akihiro Haga, Huisheng Liu and Li-Sheng Geng : kV-kV and kV-MV DECT based estimation of proton stopping power ratio - a simulation study, Physica Medica, Vol.89, 182-192, 2021. (Summary) This study aims to estimate the proton stopping power ratio (SPR) by using 80-120 kV and 120 kV-6 MV dual-energy CT (DECT) in a fully simulation-based approach for proton therapy dose calculations. Based on a virtual CT system, a two-step approach is applied to obtain the reference attenuation coefficient for image reconstruction. The effective atomic number (EAN) and electron density ratio (EDR) are estimated from two CT scans. The SPR is estimated using a calibration based on known materials to obtain a piecewise linear relationship between the EAN and the logarithm of the mean excitation energy, lnI. The calibration phantoms are constructed from reference tissue materials taken from ICRU Report 44. Our approach is evaluated through using the ICRP110 human phantom. The respective influences of noise and beam hardening effects are studied. With the beam hardening correction applied, the results of 120 kV-6 MV DECT are comparable to those of 80-120 kV DECT in predicting the EAN, but the former demonstrated a clear improvement in predicting the EDR and SPR. The 120 kV-6 MV DECT is able to predict the SPR within an accuracy of 10% for lung tissue and 5% for pelvis tissue, thereby outperforming the 80-120 kV DECT. The 120 kV-6 MV DECT is less sensitive to noise but more susceptible to beam hardening effects. By applying beam hardening correction, the 120 kV-6 MV DECT can predict the SPR more accurately than the 80-120 kV DECT. To utilize our DECT approach most effectively, high-quality reconstructed images are required. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ejmp.2021.07.038 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 34390901 ● Summary page in Scopus @ Elsevier: 2-s2.0-85112373523 (DOI: 10.1016/j.ejmp.2021.07.038, PubMed: 34390901, Elsevier: Scopus)
34.	Hasegawa Yu, Akihiro Haga, Sakata Dousatsu, Yuki Kanazawa, Masahide Tominaga, Motoharu Sasaki, Imae Toshikazu and Nakagawa Keiichi : Estimation of X-ray Energy Spectrum of Cone-Beam Computed Tomography Scanner Using Percentage Depth Dose Measurements and Machine Learning Approach, Journal of the Physical Society of Japan, Vol.90, 074801-1-074801-7, 2021. (Link to Publication Site) ● Publication site (DOI): 10.7566/JPSJ.90.074801 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.7566/JPSJ.90.074801 (DOI: 10.7566/JPSJ.90.074801)
35.	Naoki Maeda, Yuki Kanazawa, Masafumi Harada, Yo Taniguchi, Yuki Matsumoto, Hiroaki Hayashi, Kosuke Ito, Yoshitaka Bito and Akihiro Haga : Derivation of Water Exchange Constants between Components using Quantitative Parameter Mapping (QPM), Proceedings of ISMRM, Vol.29, No.3069, 2021.
36.	Taisei Shimomura and Akihiro Haga : Computed tomography image representation using the Legendre polynomial and spherical harmonics functions, Radiological Physics and Technology, Vol.14, 113-121, 2021. (Summary) The representation of computed tomography (CT) images using the Legendre polynomial (LPF) and spherical harmonics (SHF) functions was investigated. We selected 100 two-dimensional (2D) CT images of 10 lung cancer patients and 33 three-dimensional (3D) CT images of head and neck cancer patients. The reproducibility of these special functions was evaluated in terms of the normalized cross-correlation (NCC). For the 2D images, the NCC was 0.990 ± 0.002 (1sd) with an LPF of order 70, whereas for the 3D images, the NCC was 0.971 ± 0.004 (1sd) with an SHF of degree 70. The results showed that the LPF was more efficient than the Fourier series. As the thoracic and head areas are cylindrical and spherical, respectively, expansions with the LPF and SHF achieved an efficient representation of the human body. CT image representation with analytical functions can be potentially beneficial, such as in X-ray scattering estimation. (Link to Publication Site) ● Publication site (DOI): 10.1007/s12194-020-00604-0 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 33428117 ● Summary page in Scopus @ Elsevier: 2-s2.0-85099278446 (DOI: 10.1007/s12194-020-00604-0, PubMed: 33428117, Elsevier: Scopus)
37.	Jun'ichi Kotoku, Asuka Oyama, Kanako Kitazumi, Hiroshi Toki, Akihiro Haga, Ryohei Yamamoto, Maki Shinzawa, Miyae Yamakawa, Sakiko Fukui, Keiichi Yamamoto and Toshiki Moriyama : Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups, PLoS ONE, Vol.15(12), No.12, e0243229, 2020. (Summary) Causal relations among many statistical variables have been assessed using a Linear non-Gaussian Acyclic Model (LiNGAM). Using access to large amounts of health checkup data from Osaka prefecture obtained during the six fiscal years of years 2012-2017, we applied the DirectLiNGAM algorithm as a trial to extract causal relations among health indices for age groups and genders. Results show that LiNGAM yields interesting and reasonable results, suggesting causal relations and correlation among the statistical indices used for these analyses. (Keyword) Adult / Age Factors / Aged / Aged, 80 and over / Algorithms / Big Data / Female / Humans / Japan / Male / Medical Informatics / Metabolic Syndrome / Middle Aged / Normal Distribution / Sex Factors (Tokushima University Institutional Repository) ● Metadata: 116467 (Link to Publication Site) ● Publication site (DOI): 10.1371/journal.pone.0243229 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 33362207 ● Summary page in Scopus @ Elsevier: 2-s2.0-85099017483 (Tokushima University Institutional Repository: 116467, DOI: 10.1371/journal.pone.0243229, PubMed: 33362207, Elsevier: Scopus)
38.	Toshikazu Imae, Akihiro Haga, Yuichi Watanabe, Shigeharu Takenaka, Takashi Shiraki, Kanabu Nawa, Mami Ogita, Wataru Takahashi, Hideomi Yamashita, Keiichi Nakagawa and Osamu Abe : Retrospective dose reconstruction of prostate stereotactic body radiotherapy using conebeam CT and a log file during VMAT delivery with flatteningfilterfree mode, Radiological Physics and Technology, 2020. (Summary) This study aimed to reconstruct the dose distribution of single fraction of stereotactic body radiotherapy for patients with prostate cancer using cone-beam computed tomography (CBCT) and a log file during volumetric-modulated arc therapy (VMAT) delivery with flattening-filter-free (FFF) mode. Twenty patients with clinically localized prostate cancer were treated with FFF-VMAT, and projection images for in-treatment CBCT (iCBCT) imaging were concomitantly acquired with a log file. A D dose of 36.25 Gy in five fractions was prescribed to each planning target volume (PTV) on each treatment planning CT (pCT). Deformed pCT (dCT) was obtained from the iCBCT using a hybrid deformable image registration algorithm. Dose distributions on the dCT were calculated using Pinnacle v9.10 by converting the log file data to Pinnacle data format using an in-house software. Dose warping was performed by referring to deformation vector fields calculated from pCT and dCT. Reconstructed dose distribution was compared with that of the original plan. Dose differences between the original and reconstructed dose distributions were within 3% at the isocenter and observed in PTV and organ-at-risk (OAR) regions. Differences in OAR regions were relatively larger than those in the PTV, presumably because OARs were more deformed than the PTV. Therefore, our method can be used successfully to reconstruct the dose distributions of one fraction using iCBCT and a log file during FFF-VMAT delivery. (Link to Publication Site) ● Publication site (DOI): 10.1007/s12194-020-00574-3 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 32656744 ● Summary page in Scopus @ Elsevier: 2-s2.0-85087754517 (DOI: 10.1007/s12194-020-00574-3, PubMed: 32656744, Elsevier: Scopus)
39.	Kenya Kusunose, Takashi Abe, Akihiro Haga, Daiju Fukuda, Hirotsugu Yamada, Masafumi Harada and Masataka Sata : A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images, JACC. Cardiovascular Imaging, Vol.13, No.2, 374-381, 2020. (Summary) This study investigated whether a deep convolutional neural network (DCNN) could provide improved detection of regional wall motion abnormalities (RWMAs) and differentiate among groups of coronary infarction territories from conventional 2-dimensional echocardiographic images compared with that of cardiologists, sonographers, and resident readers. An effective intervention for reduction of misreading of RWMAs is needed. The hypothesis was that a DCNN trained using echocardiographic images would provide improved detection of RWMAs in the clinical setting. A total of 300 patients with a history of myocardial infarction were enrolled. From this cohort, 3 groups of 100 patients each had infarctions of the left anterior descending (LAD) artery, the left circumflex (LCX) branch, and the right coronary artery (RCA). A total of 100 age-matched control patients with normal wall motion were selected from a database. Each case contained cardiac ultrasonographs from short-axis views at end-diastolic, mid-systolic, and end-systolic phases. After the DCNN underwent 100 steps of training, diagnostic accuracies were calculated from the test set. Independently, 10 versions of the same model were trained, and ensemble predictions were performed using those versions. For detection of the presence of WMAs, the area under the receiver-operating characteristic curve (AUC) produced by the deep learning algorithm was similar to that produced by the cardiologists and sonographer readers (0.99 vs. 0.98, respectively; p = 0.15) and significantly higher than the AUC result of the resident readers (0.99 vs. 0.90, respectively; p = 0.002). For detection of territories of WMAs, the AUC by the deep learning algorithm was similar to the AUC by the cardiologist and sonographer readers (0.97 vs. 0.95, respectively; p = 0.61) and significantly higher than the AUC by resident readers (0.97 vs. 0.83, respectively; p = 0.003). From a validation group at an independent site (n = 40), the AUC by the deep learning algorithm was 0.90. The present results support the possibility of using DCNN for automated diagnosis of RWMAs in the field of echocardiography. (Tokushima University Institutional Repository) ● Metadata: 114196 (Link to Publication Site) ● Publication site (DOI): 10.1016/j.jcmg.2019.02.024 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 31103590 ● Summary page in Scopus @ Elsevier: 2-s2.0-85070068493 (Tokushima University Institutional Repository: 114196, DOI: 10.1016/j.jcmg.2019.02.024, PubMed: 31103590, Elsevier: Scopus)
40.	Kenya Kusunose, Akihiro Haga, Mizuki Inoue, Daiju Fukuda, Hirotsugu Yamada and Masataka Sata : Clinically Feasible and Accurate View Classification of Echocardiographic Images Using Deep Learning, Biomolecules, Vol.10, No.5, E665, 2020. (Summary) A proper echocardiographic study requires several video clips recorded from different acquisition angles for observation of the complex cardiac anatomy. However, these video clips are not necessarily labeled in a database. Identification of the acquired view becomes the first step of analyzing an echocardiogram. Currently, there is no consensus whether the mislabeled samples can be used to create a feasible clinical prediction model of ejection fraction (EF). The aim of this study was to test two types of input methods for the classification of images, and to test the accuracy of the prediction model for EF in a learning database containing mislabeled images that were not checked by observers. We enrolled 340 patients with five standard views (long axis, short axis, 3-chamber view, 4-chamber view and 2-chamber view) and 10 images in a cycle, used for training a convolutional neural network to classify views (total 17,000 labeled images). All DICOM images were rigidly registered and rescaled into a reference image to fit the size of echocardiographic images. We employed 5-fold cross validation to examine model performance. We tested models trained by two types of data, averaged images and 10 selected images. Our best model (from 10 selected images) classified video views with 98.1% overall test accuracy in the independent cohort. In our view classification model, 1.9% of the images were mislabeled. To determine if this 98.1% accuracy was acceptable for creating the clinical prediction model using echocardiographic data, we tested the prediction model for EF using learning data with a 1.9% error rate. The accuracy of the prediction model for EF was warranted, even with training data containing 1.9% mislabeled images. The CNN algorithm can classify images into five standard views in a clinical setting. Our results suggest that this approach may provide a clinically feasible accuracy level of view classification for the analysis of echocardiographic data. (Tokushima University Institutional Repository) ● Metadata: 115024 (Link to Publication Site) ● Publication site (DOI): 10.3390/biom10050665 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 32344829 ● Summary page in Scopus @ Elsevier: 2-s2.0-85083901945 (Tokushima University Institutional Repository: 115024, DOI: 10.3390/biom10050665, PubMed: 32344829, Elsevier: Scopus)
41.	Kenya Kusunose, Akihiro Haga, Natsumi Yamaguchi, Takashi Abe, Daiju Fukuda, Hirotsugu Yamada, Masafumi Harada and Masataka Sata : Deep Learning for Assessment of Left Ventricular Ejection Fraction from Echocardiographic Images, Journal of the American Society of Echocardiography, Vol.33, No.5, 632-635, 2020. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.echo.2020.01.009 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 32111541 ● Summary page in Scopus @ Elsevier: 2-s2.0-85080044447 (DOI: 10.1016/j.echo.2020.01.009, PubMed: 32111541, Elsevier: Scopus)
42.	Akihiro Haga and Kenya Kusunose : Echo radiomics, Medical Imaging Technology, Vol.38, No.1, 21-26, 2020. (Summary) <p>Recent progress in image analysis methods including a deep learning open a new phase in the analysis using echo-cardiographic images. Indeed, a disease-existence diagnosis, disease-difference diagnosis, and functional diagnosis via the computational analysis with echocardiographic images are nowadays the active research field. In this paper, we introduce the research of cardiac conditions using two-dimensional echo imaging, especially, a quantitative evaluation of left ventricular ejection fraction (LVEF). Although the previous LVEF evaluation is needed through an observer dependent process that requires manual tracing, we show here that the direct evaluation from echocardiographic images using deep neural network with various view modes gives precise prediction in LVEF. In addition, the pre-processing used in our research, including automated classification of imaging plane, is introduced.</p> (Keyword) Ultra-sound image / Echocardiographic analysis / Left ventricular ejection fraction / Deep learning (Link to Publication Site) ● Publication site (DOI): 10.11409/mit.38.21 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390565134827158784 ● Search Scopus @ Elsevier (DOI): 10.11409/mit.38.21 (DOI: 10.11409/mit.38.21, CiNii: 1390565134827158784)
43.	Ozaki Sho, Akihiro Haga, Chao Edward, Maurer Calvin, Nawa Kanabu, Ohta Takeshi, Nakamoto Takahiro, Nozawa Yuki, Magome Taiki, Nakano Masahiro and Nakagawa Keiichi : Fast Statistical Iterative Reconstruction for Mega-voltage Computed Tomography, The Journal of Medical Investigation : JMI, Vol.67, No.1,2, 30-39, 2020. (Tokushima University Institutional Repository) ● Metadata: 114750 (Link to Publication Site) ● Publication site (DOI): 10.2152/jmi.67.30 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 32378615 ● Search Scopus @ Elsevier (PMID): 32378615 ● Search Scopus @ Elsevier (DOI): 10.2152/jmi.67.30 (Tokushima University Institutional Repository: 114750, DOI: 10.2152/jmi.67.30, PubMed: 32378615)
44.	Ryosuke Takenaka, Akihiro Haga, Kanabu Nawa, Yamashita Hideomi and Keiichi Nakagawa : Improvement of the robustness to set up error by a virtual bolus in total scalp irradiation with Helical TomoTherapy, Radiological Physics and Technology, Vol.12, No.4, 433-437, 2019. (Summary) Intensity-modulated radiation therapy has recently been used for total scalp irradiation. In inverse planning, the treatment planning system increases the fluence of tangential beam near the skin surface to counter the build-up region. Consequently, the dose to the skin surface increases even with small setup errors. Replacing the electron density of the surrounding air of some thickness with a virtual bolus during optimization could suppress the extremely high fluence near the skin. We confirmed the usefulness of a virtual bolus in total scalp irradiation. For each patient, two beams were planned, one with and the other without a virtual bolus. The dose distribution was calculated using computed tomography images that were shifted to simulate setup errors. The hot spot dose was suppressed in the plans using a virtual bolus. In conclusion, using a virtual bolus improved the robustness to setup errors. (Keyword) Humans / Radiotherapy Planning, Computer-Assisted / Radiotherapy Setup Errors / Radiotherapy, Intensity-Modulated / Scalp / User-Computer Interface (Link to Publication Site) ● Publication site (DOI): 10.1007/s12194-019-00539-1 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 31642033 ● Search Scopus @ Elsevier (PMID): 31642033 ● Search Scopus @ Elsevier (DOI): 10.1007/s12194-019-00539-1 (DOI: 10.1007/s12194-019-00539-1, PubMed: 31642033)
45.	Wataru Takahashi, Kanabu Nawa, Akihiro Haga, Hideomi Yamashita, Toshikazu Imae, Mami Ogita, Kae Okuma, Osamu Abe and Keiichi Nakagawa : Acceptable fetal dose using flattening filter-free volumetric arc therapy (FFF VMAT) in postoperative chemoradiotherapy of tongue cancer during pregnancy,, Clinical and Translational Radiation Oncology, Vol.20, 9-12, 2019. (Summary) Optimizing irradiation protocols for pregnant women is challenging, because there are few cases and a dearth of fetal dosimetry data. We cared for a 36-year-old pregnant woman with tongue cancer. Prior to treatment, we compared three intensity-modulated radiation therapy (IMRT) techniques, including helical tomotherapy, volumetric arc therapy (VMAT), and flattening-filter free VMAT (FFF-VMAT) using treatment planning software. FFF-VMAT achieved the minimum fetal exposure and was selected as the optimal modality. We prescribed 66 Gy to the involved nodes, 60 Gy to the tumor bed and ipsilateral neck, and 54 Gy to the contralateral neck over 33 fractions. To confirm the out-of-field exposure per fraction, surface doses and the rectal dose were measured during FFF-VMAT delivery. Postoperative chemoradiotherapy was delivered using IMRT and a cisplatin regimen. Without any shielding, the total fetal dose was 0.03 Gy, within the limits established by the ICRP. A healthy girl was born vaginally at 37 weeks' gestation. (Tokushima University Institutional Repository) ● Metadata: 115062 (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ctro.2019.10.002 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 31709307 ● Search Scopus @ Elsevier (PMID): 31709307 ● Search Scopus @ Elsevier (DOI): 10.1016/j.ctro.2019.10.002 (Tokushima University Institutional Repository: 115062, DOI: 10.1016/j.ctro.2019.10.002, PubMed: 31709307)
46.	Satoshi Takahashi, Wataru Takahashi, Shota Tanaka, Akihiro Haga, Takahiro Nakamoto, Yuichi Suzuki, Taijun Hana, Akitake Mukasa, Shunsaku Takayanagi, Yosuke Kitagawa, Takahide Nejo, Masashi Nomura, Keiichi Nakagawa and Nobuhito Saito : Radiomics Analysis for Glioma Malignancy Evaluation Using Diffusion Kurtosis and Tensor Imaging, International Journal of Radiation OncologyBiologyPhysics, Vol.105, No.4, 784-791, 2019. (Summary) A noninvasive diagnostic method to predict the degree of malignancy accurately would be of great help in glioma management. This study aimed to create a highly accurate machine learning model to perform glioma grading. Preoperative magnetic resonance imaging acquired for cases of glioma operated on at our institution from October 2014 through January 2018 were obtained retrospectively. Six types of magnetic resonance imaging sequences (T-weighted image, diffusion-weighted image, apparent diffusion coefficient [ADC], fractional anisotropy, and mean kurtosis [MK]) were chosen for analysis; 476 features were extracted semiautomatically for each sequence (2856 features in total). Recursive feature elimination was used to select significant features for a machine learning model that distinguishes glioblastoma from lower-grade glioma (grades 2 and 3). Fifty-five data sets from 54 cases were obtained (14 grade 2 gliomas, 12 grade 3 gliomas, and 29 glioblastomas), of which 44 and 11 data sets were used for machine learning and independent testing, respectively. We detected 504 features with significant differences (false discovery rate <0.05) between glioblastoma and lower-grade glioma. The most accurate machine learning model was created using 6 features extracted from the ADC and MK images. In the logistic regression, the area under the curve was 0.90 ± 0.05, and the accuracy of the test data set was 0.91 (10 out of 11); using a support vector machine, they were 0.93 ± 0.03 and 0.91 (10 out of 11), respectively (kernel, radial basis function; c = 1.0). Our machine learning model accurately predicted glioma tumor grade. The ADC and MK sequences produced particularly useful features. (Keyword) Adolescent / Adult / Aged / Aged, 80 and over / Anisotropy / Astrocytoma / Brain Neoplasms / Datasets as Topic / Diagnosis, Differential / Diffusion Magnetic Resonance Imaging / Diffusion Tensor Imaging / Female / Glioblastoma / Glioma / Humans / Machine Learning / Male / Middle Aged / Neoplasm Grading / Oligodendroglioma / Retrospective Studies / Young Adult (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ijrobp.2019.07.011 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 31344432 ● Summary page in Scopus @ Elsevier: 2-s2.0-85071992633 (DOI: 10.1016/j.ijrobp.2019.07.011, PubMed: 31344432, Elsevier: Scopus)
47.	Takuya Mizutani, Taiki Magome, Hiroshi Igaki, Akihiro Haga, Kanabu Nawa, Noriyasu Sekiya and Keiichi Nakagawa : Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy, Journal of Radiation Research, Vol.60, No.6, 818-824, 2019. (Summary) The purpose of this study was to predict the survival time of patients with malignant glioma after radiotherapy with high accuracy by considering additional clinical factors and optimize the prescription dose and treatment duration for individual patient by using a machine learning model. A total of 35 patients with malignant glioma were included in this study. The candidate features included 12 clinical features and 192 dose-volume histogram (DVH) features. The appropriate input features and parameters of the support vector machine (SVM) were selected using the genetic algorithm based on Akaike's information criterion, i.e. clinical, DVH, and both clinical and DVH features. The prediction accuracy of the SVM models was evaluated through a leave-one-out cross-validation test with residual error, which was defined as the absolute difference between the actual and predicted survival times after radiotherapy. Moreover, the influences of various values of prescription dose and treatment duration on the predicted survival time were evaluated. The prediction accuracy was significantly improved with the combined use of clinical and DVH features compared with the separate use of both features (P < 0.01, Wilcoxon signed rank test). Mean ± standard deviation of the leave-one-out cross-validation using the combined clinical and DVH features, only clinical features and only DVH features were 104.7 ± 96.5, 144.2 ± 126.1 and 204.5 ± 186.0 days, respectively. The prediction accuracy could be improved with the combination of clinical and DVH features, and our results show the potential to optimize the treatment strategy for individual patients based on a machine learning model. (Keyword) Adolescent / Adult / Aged / Aged, 80 and over / Child / Dose-Response Relationship, Radiation / Female / Glioma / Humans / Machine Learning / Male / Middle Aged / Models, Theoretical / Support Vector Machine / Survival Analysis / Time Factors / Young Adult (Tokushima University Institutional Repository) ● Metadata: 115534 (Link to Publication Site) ● Publication site (DOI): 10.1093/jrr/rrz066 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 31665445 ● Summary page in Scopus @ Elsevier: 2-s2.0-85075814383 (Tokushima University Institutional Repository: 115534, DOI: 10.1093/jrr/rrz066, PubMed: 31665445, Elsevier: Scopus)
48.	Takahiro Nakamoto, Wataru Takahashi, Akihiro Haga, Satoshi Takahashi, Shigeru Kiryu, Kanabu Nawa, Takeshi Ohta, Sho Ozaki, Yuki Nozawa, Shota Tanaka, Akitake Mukasa and Keiichi Nakagawa : Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis, Scientific Reports, Vol.9, No.1, 19411, 2019. (Summary) We conducted a feasibility study to predict malignant glioma grades via radiomic analysis using contrast-enhanced T1-weighted magnetic resonance images (CE-T1WIs) and T2-weighted magnetic resonance images (T2WIs). We proposed a framework and applied it to CE-T1WIs and T2WIs (with tumor region data) acquired preoperatively from 157 patients with malignant glioma (grade III: 55, grade IV: 102) as the primary dataset and 67 patients with malignant glioma (grade III: 22, grade IV: 45) as the validation dataset. Radiomic features such as size/shape, intensity, histogram, and texture features were extracted from the tumor regions on the CE-T1WIs and T2WIs. The Wilcoxon-Mann-Whitney (WMW) test and least absolute shrinkage and selection operator logistic regression (LASSO-LR) were employed to select the radiomic features. Various machine learning (ML) algorithms were used to construct prediction models for the malignant glioma grades using the selected radiomic features. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the prediction models in the primary dataset. The selected radiomic features for all folds in the LOOCV of the primary dataset were used to perform an independent validation. As evaluation indices, accuracies, sensitivities, specificities, and values for the area under receiver operating characteristic curve (or simply the area under the curve (AUC)) for all prediction models were calculated. The mean AUC value for all prediction models constructed by the ML algorithms in the LOOCV of the primary dataset was 0.902 ± 0.024 (95% CI (confidence interval), 0.873-0.932). In the independent validation, the mean AUC value for all prediction models was 0.747 ± 0.034 (95% CI, 0.705-0.790). The results of this study suggest that the malignant glioma grades could be sufficiently and easily predicted by preparing the CE-T1WIs, T2WIs, and tumor delineations for each patient. Our proposed framework may be an effective tool for preoperatively grading malignant gliomas. (Tokushima University Institutional Repository) ● Metadata: 114951 (Link to Publication Site) ● Publication site (DOI): 10.1038/s41598-019-55922-0 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 31857632 ● Search Scopus @ Elsevier (PMID): 31857632 ● Search Scopus @ Elsevier (DOI): 10.1038/s41598-019-55922-0 (Tokushima University Institutional Repository: 114951, DOI: 10.1038/s41598-019-55922-0, PubMed: 31857632)
49.	Kenya Kusunose, Akihiro Haga, Takashi Abe and Masataka Sata : Utilization of Artificial Intelligence in Echocardiography, Circulation Journal, Vol.83, No.8, 1623-1629, 2019. (Summary) Echocardiography has a central role in the diagnosis and management of cardiovascular disease. Precise and reliable echocardiographic assessment is required for clinical decision-making. Even if the development of new technologies (3-dimentional echocardiography, speckle-tracking, semi-automated analysis, etc.), the final decision on analysis is strongly dependent on operator experience. Diagnostic errors are a major unresolved problem. Moreover, not only can cardiologists differ from one another in image interpretation, but also the same observer may come to different findings when a reading is repeated. Daily high workloads in clinical practice may lead to this error, and all cardiologists require precise perception in this field. Artificial intelligence (AI) has the potential to improve analysis and interpretation of medical images to a new stage compared with previous algorithms. From our comprehensive review, we believe AI has the potential to improve accuracy of diagnosis, clinical management, and patient care. Although there are several concerns about the required large dataset and "black box" algorithm, AI can provide satisfactory results in this field. In the future, it will be necessary for cardiologists to adapt their daily practice to incorporate AI in this new stage of echocardiography. (Tokushima University Institutional Repository) ● Metadata: 114194 (Link to Publication Site) ● Publication site (DOI): 10.1253/circj.CJ-19-0420 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 31257314 ● Summary page in Scopus @ Elsevier: 2-s2.0-85070102118 (Tokushima University Institutional Repository: 114194, DOI: 10.1253/circj.CJ-19-0420, PubMed: 31257314, Elsevier: Scopus)
50.	Yuki Kanazawa, Chiba Daiki, Masafumi Harada, Miyati Tosiaki, Miyoshi Mitsuharu, Hiroaki Hayashi, Yuki Matsumoto, Takashi Abe and Akihiro Haga : Thermal sensitive pH imaging using CEST, Proceedings of the 27th Annual Meeting of ISMRM, No.3991, 2019.
51.	Yuki Kanazawa, Masafumi Harada, Taniguchi Yo, Hiroaki Hayashi, Takashi Abe, Maki Ohtomo, Yuki Matsumoto, Ono Masafumi, Bito Yoshitaka and Akihiro Haga : Myelin imaging derived from quantitative parameter mapping, Proceedings of the 27th Annual Meeting of ISMRM, No.3313, 2019.
52.	Akihiro Haga, Takahashi Wataru, Aoki Shuri, Nawa Kanabu and Yamashita Hideomi : Standardization of imaging features for radiomics analysis, The Journal of Medical Investigation : JMI, Vol.66, 35-37, 2018. (Summary) Radiomics has the potential to provide tumor characteristics with noninvasive and repeatable way. The purpose of this paper is to evaluate the standardization effect of imaging features for radiomics analysis. For this purpose, we prepared two CT databases; one includes 40 non-small cell lung cancer (NSCLC) patients for whom tumor biopsies was performed before stereotactic body radiation therapy in The University of Tokyo Hospital, and the other includes 29 early-stage NSCLC datasets from the Cancer Imaging Archive. The former was used as the training data, whereas the later was used as the test data in the evaluation of the prediction model. In total, 476 imaging features were extracted from each data. Then, both training and test data were standardized as the min-max normalization, the z-score normalization, and the whitening from the principle component analysis. All of standardization strategies improved the accuracy for the histology prediction. The area under the receiver observed characteristics curve was 0.725, 0.789, and 0.785 in above standardizations, respectively. Radiomics analysis has shown that robust features have a high prognostic power in predicting early-stage NSCLC histology subtypes. The performance was able to be improved by standardizing the data in the feature space. (Keyword) Radiomics / Histology prediction / Quantitative imaging / Standardization / machine learning (Tokushima University Institutional Repository) ● Metadata: 113316 (Link to Publication Site) ● Publication site (DOI): 10.2152/jmi.66.35 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 31064950 ● Summary page in Scopus @ Elsevier: 2-s2.0-85065783999 (Tokushima University Institutional Repository: 113316, DOI: 10.2152/jmi.66.35, PubMed: 31064950, Elsevier: Scopus)
53.	Ritu Bhusal Chhatkuli, Kazuyuki Demachi, Mitsuru Uesaka, Keiichi Nakagawa and Akihiro Haga : Development of a markerless tumor-tracking algorithm using prior four-dimensional cone-beam computed tomography, Journal of Radiation Research, Vol.59, No.6, 1-7, 2018. (Summary) Respiratory motion management is a huge challenge in radiation therapy. Respiratory motion induces temporal anatomic changes that distort the tumor volume and its position. In this study, a markerless tumor-tracking algorithm was investigated by performing phase recognition during stereotactic body radiation therapy (SBRT) using four-dimensional cone-beam computer tomography (4D-CBCT) obtained at patient registration, and in-treatment cone-beam projection images. The data for 20 treatment sessions (five lung cancer patients) were selected for this study. Three of the patients were treated with conventional flattening filter (FF) beams, and the other two were treated with flattening filter-free (FFF) beams. Prior to treatment, 4D-CBCT was acquired to create the template projection images for 10 phases. In-treatment images were obtained at near real time during treatment. Template-based phase recognition was performed for 4D-CBCT re-projected templates using prior 4D-CBCT based phase recognition algorithm and was compared with the results generated by the Amsterdam Shroud (AS) technique. Visual verification technique was used for the verification of the phase recognition and AS technique at certain tumor-visible angles. Offline template matching analysis using the cross-correlation indicated that phase recognition performed using the prior 4D-CBCT and visual verification matched up to 97.5% in the case of FFF, and 95% in the case of FF, whereas the AS technique matched 83.5% with visual verification for FFF and 93% for FF. Markerless tumor tracking based on phase recognition using prior 4D-CBCT has been developed successfully. This is the first study that reports on the use of prior 4D-CBCT based on normalized cross-correlation technique for phase recognition. (Tokushima University Institutional Repository) ● Metadata: 115536 (Link to Publication Site) ● Publication site (DOI): 10.1093/jrr/rry085 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 30407560 ● Search Scopus @ Elsevier (PMID): 30407560 ● Search Scopus @ Elsevier (DOI): 10.1093/jrr/rry085 (Tokushima University Institutional Repository: 115536, DOI: 10.1093/jrr/rry085, PubMed: 30407560)
54.	Kida Satoshi, Nakamoto Takahiro, Nakano Masahiro, Nawa Kanabu, Akihiro Haga, Kotoku Junichi, Yamashita Hideomi and Nakagawa Keiichi : Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network, Curēus, Vol.10, No.4, e2548, 2018. (Summary) Introduction Cone beam computed tomography (CBCT) plays an important role in image-guided radiation therapy (IGRT), while having disadvantages of severe shading artifact caused by the reconstruction using scatter contaminated and truncated projections. The purpose of this study is to develop a deep convolutional neural network (DCNN) method for improving CBCT image quality. Methods CBCT and planning computed tomography (pCT) image pairs from 20 prostate cancer patients were selected. Subsequently, each pCT volume was pre-aligned to the corresponding CBCT volume by image registration, thereby leading to registered pCT data (pCT). Next, a 39-layer DCNN model was trained to learn a direct mapping from the CBCT to the corresponding pCTimages. The trained model was applied to a new CBCT data set to obtain improved CBCT (i-CBCT) images. The resulting i-CBCT images were compared to pCT using the spatial non-uniformity (SNU), the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM). Results The image quality of the i-CBCT has shown a substantial improvement on spatial uniformity compared to that of the original CBCT, and a significant improvement on the PSNR and the SSIM compared to that of the original CBCT and the enhanced CBCT by the existing pCT-based correction method. Conclusion We have developed a DCNN method for improving CBCT image quality. The proposed method may be directly applicable to CBCT images acquired by any commercial CBCT scanner. (Keyword) Cone Beam CT / Planning CT / Deep learning / Convolutional Neural Network (Link to Publication Site) ● Publication site (DOI): 10.7759/cureus.2548 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 29963342 ● Search Scopus @ Elsevier (PMID): 29963342 ● Search Scopus @ Elsevier (DOI): 10.7759/cureus.2548 (DOI: 10.7759/cureus.2548, PubMed: 29963342)
55.	Shuri Aoki, Hideomi Yamashita, Akihiro Haga, Takeshi Ota, Wataru Takahashi, Sho Ozaki, Kanabu Nawa, Toshikazu Imae, Osamu Abe and Keiichi Nakagawa : Stereotactic body radiotherapy for centrallylocated lung tumors with 56 Gy in seven fractions: A retrospective study, Oncology Letters, Vol.16, No.4, 4498-4506, 2018. (Summary) Stereotactic body radiotherapy (SBRT) for centrally-located lung tumors remains a challenge because of the increased risk of treatment-related adverse events (AEs), and uncertainty around prescribing the optimal dose. The present study reported the results of central tumor SBRT with 56 Gy in 7 fractions (fr) at the University of Tokyo Hospital. A total of 35 cases that underwent SBRT with or without volumetric-modulated arc therapy consisting of 56 Gy/7 fr for central lung lesions between 2010 and 2016 at the University of Tokyo Hospital were reveiwed. A central lesion was defined as a tumor within 2 cm of the proximal bronchial tree (RTOG 0236 definition) or within 2 cm in all directions of any critical mediastinal structure. Local control (LC), overall survival (OS), and AEs were investigated. The Kaplan-Meier method was used to estimate LC and OS. AEs were scored per the Common Terminology Criteria for Adverse Events Version 4.0. Thirty-five patients with 36 central lung lesions were included. Fifteen lesions were primary non-small cell lung cancer (NSCLC), 13 were recurrences of NSCLC, and 8 had oligo-recurrences from other primaries. Median tumor diameter was 29 mm. Eighteen patients had had prior surgery. At a median follow-up of 13.1 months for all patients and 18.3 months in surviving patients, 22 patients had died, ten due to primary disease (4 NSCLC), while three were treatment-related. The 1- and 2-year OS were 57.3 and 40.4%, respectively, and median OS was 15.7 months. Local recurrence occurred in only two lesions. 1- and 2-year LC rates were both 96%. Nine patients experienced grade ≥3 toxicity, representing 26% of the cohort. Two of these were grade 5, one pneumonitis and one hemoptysis. Considering the background of the subject, tumor control of our central SBRT is promising, especially in primary NSCLC. However, the safety of SBRT to central lung cancer remains controversial. (Tokushima University Institutional Repository) ● Metadata: 115666 (Link to Publication Site) ● Publication site (DOI): 10.3892/ol.2018.9188 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 30214585 ● Search Scopus @ Elsevier (PMID): 30214585 ● Search Scopus @ Elsevier (DOI): 10.3892/ol.2018.9188 (Tokushima University Institutional Repository: 115666, DOI: 10.3892/ol.2018.9188, PubMed: 30214585)
56.	Aoki Shuri, Yamashita Hideomi, Akihiro Haga, Nawa Kanabu, Image Toshikazu, Takahashi Wataru, Abe Osamu and Nakagawa Keiichi : Flattening filter-free technique in volumetric modulated arc therapy for lung stereotactic body radiotherapy: A clinical comparison with the flattening filter technique, Oncology Letters, Vol.15, No.3, 3928-3936, 2018. (Summary) The present study sought to evaluate the impact of the flattening filter-free (FFF) technique in volumetric modulated arc therapy for lung stereotactic body radiotherapy. Its clinical safety and availability were compared with the flattening filter (FF) method. The cases of 65 patients who underwent lung volumetric modulated arc therapy-stereotactic body radiotherapy (VMAT-SBRT) using FF or FFF techniques were reviewed. A total of 55 Gy/4 fractions (fr) was prescribed for peripheral lesions or 56 Gy/7 fr for central lesions. The total monitor units (MU), treatment time, dose to tumors, dose to organs at risk, tumor control (local control rate, overall survival, progression-free survival) and adverse events between cases treated with FF and cases treated with the FFF technique were compared. A total of 35 patients were treated with conventional FF techniques prior to November 2014 and 30 patients were treated with FFF techniques after this date. It was revealed that the beam-on time was significantly shortened by the FFF technique (P<0.01). Other factors were similar for FFF and FF plans in respect to conformity (P=0.95), homogeneity (P=0.20) and other dosimetric values, including total MU and planning target volume/internal target volume coverage. The median follow-up period was 18 months (range, 2-35). One-year local control rates were 97.1 and 90.0% in the FF group and FFF groups, respectively (P=0.33). Grade 3 pneumonitis was observed in 5.8% of FF patients and 3.4% of FFF patients (P=1.00). No other adverse events ≥grade 3 were observed. The results of the study suggest that VMAT-SBRT using the FFF technique shortens the treatment time for lung SBRT while maintaining a high local control rate with low toxicity. (Link to Publication Site) ● Publication site (DOI): 10.3892/ol.2018.7809 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 29563993 ● Summary page in Scopus @ Elsevier: 2-s2.0-85040823404 (DOI: 10.3892/ol.2018.7809, PubMed: 29563993, Elsevier: Scopus)
57.	Akihiro Haga, Takahashi Wataru, Aoki Shuri, Nawa Kanabu and Yamashita Hideomi : Classification of early stage non-small cell lung cancers on computed tomographic images into histological types using radiomic features: interobserver delineation variability analysis, Radiological Physics and Technology, Vol.11, No.1, 1-9, 2017. (Summary) Radiomics, which involves the extraction of large numbers of quantitative features from medical images, has attracted attention in cancer research. In radiomics analysis, tumor segmentation is a crucial step. In this study, we evaluated the potential application of radiomics for predicting the histology of early stage non-small cell lung cancer (NSCLC) by analyzing interobserver variability in tumor delineation. Forty patient datasets were included in this study, 21 involving adenocarcinomas and 19 involving squamous cell carcinomas. All patients underwent stereotactic body radiotherapy treatment. In total, 476 features were extracted from each dataset, representing treatment planning, computed tomography images, and gross tumor volume (GTV). The definition of GTV can significantly affect the histology prediction. Therefore, in the present study, the effect of interobserver tumor delineation variability on radiomic features was evaluated by preparing 4 volumes of interest (VOIs) for each patient, as follows: the original GTV (which was delineated at treatment planning); two GTVs delineated retrospectively by radiation oncologists; and a semi-automatic GTV contoured by a medical physicist. Radiomic features extracted from each VOI were then analyzed using a naïve Bayesian model. Area-under-the-curve (AUC) analysis showed that interobserver variability in delineation is a significant factor in radiomics performance. Nevertheless, with 8 selected features, AUC values averaged over the VOIs were high (0.725 ± 0.070). The present study indicated that radiomics has potential for predicting early stage NSCLC histology despite variability in delineation. The high prediction accuracy implies that noninvasive histology evaluation by radiomics is a promising clinical application. (Keyword) Radiomics / Histology / Non-small-cell lung cancer (NSCLC) / Prediction / Machine learning (Link to Publication Site) ● Publication site (DOI): 10.1007/s12194-017-0433-2 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 29209915 ● Search Scopus @ Elsevier (PMID): 29209915 ● Search Scopus @ Elsevier (DOI): 10.1007/s12194-017-0433-2 (DOI: 10.1007/s12194-017-0433-2, PubMed: 29209915)
58.	Masahiro Nakano, Akihiro Haga, Jun'ichi Kotoku, Taiki Magome, Yoshitaka Masutani, Shouhei Hanaoka, Satoshi Kida and Keiichi Nakagawa : Cone-beam CT reconstruction for non-periodic organ motion using time-ordered chain graph model, Radiation Oncology, Vol.12:145, 1-4, 2017. (Tokushima University Institutional Repository) ● Metadata: 114537 (Link to Publication Site) ● Publication site (DOI): 10.1186/s13014-017-0879-8 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85028883938 (Tokushima University Institutional Repository: 114537, DOI: 10.1186/s13014-017-0879-8, Elsevier: Scopus)
59.	Dousatsu Sakata, Akihiro Haga, Satoshi Kida, Toshikazu Imae, Shigeharu Takenaka and Keiichi Nakagawa : Effective atomic number estimation using kV-MV dual-energy source in LINAC, Physica Medica, Vol.39, 9-15, 2017. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ejmp.2017.06.010 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85023159954 (DOI: 10.1016/j.ejmp.2017.06.010, Elsevier: Scopus)
60.	Kanabu Nawa, Akihiro Haga, Akihiro Nomoto, Raniel A Sarmiento, Kenshiro Shiraishi, Hideomi Yamashita and Keiichi Nakagawa : Evaluation of a commercial automatic treatment planning system for prostate cancers, Medical Dosimetry, Vol.42, No.3, 203-209, 2017. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.meddos.2017.03.004 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85019549956 (DOI: 10.1016/j.meddos.2017.03.004, Elsevier: Scopus)
61.	Keiichi Nakagawa, Kanabu Nawa, Masatoshi Hashimoto, Shuri Aoki, Yoshihiro Kaneko, Hideomi Yamashita and Akihiro Haga : A Half-Arc Multiple Deep-Inspiration Breath-Hold Volumetric Modulated Arc Therapy for a Lung Tumor with 10 MV Flattening-Filter-Free Beams and an Image Sensor Measuring a Distance Map to Thorax Surface: An Initial Clinical Experience, International Journal of Medical Physics, Clinical Engineering and Radiation Oncology, Vol.6, No.1, 31-35, 2017. (Link to Publication Site) ● Publication site (DOI): 10.4236/ijmpcero.2017.61004 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.4236/ijmpcero.2017.61004 (DOI: 10.4236/ijmpcero.2017.61004)
62.	Ryosuke Takenaka, Akihiro Haga, Hideomi Yamashita and Keiichi Nakagawa : Adequate target volume in total-body irradiation by intensity-modulated radiation therapy using helical tomotherapy: a simulation study, Journal of Radiation Research, Vol.58, No.2, 210-216, 2016. (Summary) Recently, intensity-modulated radiation therapy (IMRT) has been used for total-body irradiation (TBI). Since the planning target volume (PTV) for TBI includes the surrounding air, a dose prescription to the PTV provides high fluence to the body surface. Thus with just a small set-up error, the body might be exposed to a high-fluence beam. This study aims to assess which target volume should be prescribed the dose, such as a clinical target volume (CTV) with a margin, or a CTV that excludes the surface area of the skin. Three treatment plans were created for each patient: the 5-mm clipped plan (Plan A), the 0-mm margin plan (Plan B) and the 5-mm margin plan (Plan C). The CTV was the whole body. PTVs were the CTV with the exception of 5 mm from the skin surface in Plan A, equal to the CTV in Plan B, and the CTV with a 5 mm margin in Plan C. The prescribed dose was 12 Gy in six fractions. To assess the influence of the set-up error, dose distributions were simulated on computed tomography (CT) images shifted 2 pixels (= 4.296 mm), 5 pixels (= 10.74 mm) and 10 pixels (= 21.48 mm) in the lateral direction from the original CT. With a set-up error of 10.74 mm, V110% was 8.8%, 11.1% and 23.3% in Plans A, B and C, respectively. The prescription to the PTV containing the surrounding air can be paradoxically vulnerable to a high-dose as a consequence of a small set-up error. (Keyword) Adult / Child / Computer Simulation / Female / Humans / Male / Middle Aged / Radiotherapy Dosage / Radiotherapy, Intensity-Modulated / Whole-Body Irradiation (Link to Publication Site) ● Publication site (DOI): 10.1093/jrr/rrw115 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 27974508 ● Summary page in Scopus @ Elsevier: 2-s2.0-85027444549 (DOI: 10.1093/jrr/rrw115, PubMed: 27974508, Elsevier: Scopus)
63.	Taiki Magome, Akihiro Haga, Yutaka Takahashi, Keiichi Nakagawa, Kathryn Dusenbery and Susanta Hui : A rapid imaging method for total body or marrow irradiation in helical tomotherapy, International Journal of Radiation OncologyBiologyPhysics, Vol.96, No.3, 688-695, 2016. (Summary) Megavoltage computed tomographic (MVCT) imaging has been widely used for the 3-dimensional (3-D) setup of patients treated with helical tomotherapy (HT). One drawback of MVCT is its very long imaging time, the result of slow couch speeds of approximately 1 mm/s, which can be difficult for the patient to tolerate. We sought to develop an MVCT imaging method allowing faster couch speeds and to assess its accuracy for image guidance for HT. Three cadavers were scanned 4 times with couch speeds of 1, 2, 3, and 4 mm/s. The resulting MVCT images were reconstructed using an iterative reconstruction (IR) algorithm with a penalty term of total variation and with a conventional filtered back projection (FBP) algorithm. The MVCT images were registered with kilovoltage CT images, and the registration errors from the 2 reconstruction algorithms were compared. This fast MVCT imaging was tested in 3 cases of total marrow irradiation as a clinical trial. The 3-D registration errors of the MVCT images reconstructed with the IR algorithm were smaller than the errors of images reconstructed with the FBP algorithm at fast couch speeds (2, 3, 4 mm/s). The scan time and imaging dose at a speed of 4 mm/s were reduced to 30% of those from a conventional coarse mode scan. For the patient imaging, faster MVCT (3 mm/s couch speed) scanning reduced the imaging time and still generated images useful for anatomic registration. Fast MVCT with the IR algorithm is clinically feasible for large 3-D target localization, which may reduce the overall time for the treatment procedure. This technique may also be useful for calculating daily dose distributions or organ motion analyses in HT treatment over a wide area. Automated integration of this imaging is at least needed to further assess its clinical benefits. (Keyword) Adult / Bone Marrow / Cadaver / Dose-Response Relationship, Radiation / Female / Humans / Male / Middle Aged / Radiographic Image Enhancement / Radiography, Dual-Energy Scanned Projection / Radiotherapy Dosage / Radiotherapy, Image-Guided / Reproducibility of Results / Sensitivity and Specificity / Tomography, X-Ray Computed / Whole Body Imaging / Whole-Body Irradiation (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ijrobp.2016.06.2458 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 27681766 ● Summary page in Scopus @ Elsevier: 2-s2.0-84990830637 (DOI: 10.1016/j.ijrobp.2016.06.2458, PubMed: 27681766, Elsevier: Scopus)
64.	Taiki Magome, Jerry Froelich, Yutaka Takahashi, Luke Arentsen, Shernan Holtan, Keenan Brown, Akihiro Haga, Keiichi Nakagawa, Jennifer Holter, Sebastian Giebel, Jeffrey Wong, Kathryn Dusenbery, Guy Storme and Susanta Hui : Evaluation of Functional Marrow Irradiation Based on Skeletal Marrow Composition Obtained Using Dual-Energy Computed Tomography, International Journal of Radiation OncologyBiologyPhysics, Vol.96, No.3, 679-687, 2016. (Summary) To develop an imaging method to characterize and map marrow composition in the entire skeletal system, and to simulate differential targeted marrow irradiation based on marrow composition. Whole-body dual energy computed tomography (DECT) images of cadavers and leukemia patients were acquired, segmented to separate bone marrow components, namely, bone, red marrow (RM), and yellow marrow (YM). DECT-derived marrow fat fraction was validated using histology of lumbar vertebrae obtained from cadavers. The fractions of RM (RMF = RM/total marrow) and YMF were calculated in each skeletal region to assess the correlation of marrow composition with sites and ages. Treatment planning was simulated to target irradiation differentially at a higher dose (18 Gy) to either RM or YM and a lower dose (12 Gy) to the rest of the skeleton. A significant correlation between fat fractions obtained from DECT and cadaver histology samples was observed (r=0.861, P<.0001, Pearson). The RMF decreased in the head, neck, and chest was significantly inversely correlated with age but did not show any significant age-related changes in the abdomen and pelvis regions. Conformity of radiation to targets (RM, YM) was significantly dependent on skeletal sites. The radiation exposure was significantly reduced (P<.05, t test) to organs at risk (OARs) in RM and YM irradiation compared with standard total marrow irradiation (TMI). Whole-body DECT offers a new imaging technique to visualize and measure skeletal-wide marrow composition. The DECT-based treatment planning offers volumetric and site-specific precise radiation dosimetry of RM and YM, which varies with aging. Our proposed method could be used as a functional compartment of TMI for further targeted radiation to specific bone marrow environment, dose escalation, reduction of doses to OARs, or a combination of these factors. (Keyword) Adult / Bone Marrow / Cadaver / Dose-Response Relationship, Radiation / Female / Humans / Leukemia / Male / Middle Aged / Radiography, Dual-Energy Scanned Projection / Radiotherapy Dosage / Radiotherapy, Image-Guided / Reproducibility of Results / Sensitivity and Specificity / Tomography, X-Ray Computed / Whole Body Imaging / Whole-Body Irradiation (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ijrobp.2016.06.2459 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 27681765 ● Summary page in Scopus @ Elsevier: 2-s2.0-84990855026 (DOI: 10.1016/j.ijrobp.2016.06.2459, PubMed: 27681765, Elsevier: Scopus)
65.	今江禄一, Akihiro Haga, 早乙女直也, 木田智士, 中野正寛, 竹中重治, 竹内幸浩, 白木尚, 矢野敬一, 山下英臣, 中川恵一 and 大友邦 : Evaluation of a Post-analysis Method for Cumulative Dose Distribution in Stereotactic Body Radiotherapy, Japanese Journal of Radiological Technology, Vol.72, No.3, 251-260, 2016. (Summary) Purpose: The purpose of this study was to evaluate a post-analysis method for cumulative dose distribution in stereotactic body radiotherapy (SBRT) using volumetric modulated arc therapy (VMAT) . Method: VMAT is capable of acquiring respiratory signals derived from projection images and machine parameters based on machine logs during VMAT delivery. Dose distributions were reconstructed from the respiratory signals and machine parameters in the condition where respiratory signals were without division, divided into 4 and 10 phases. The dose distribution of each respiratory phase was calculated on the planned four-dimensional CT (4DCT). Summation of the dose distributions was carried out using deformable image registration (DIR), and cumulative dose distributions were compared with those of the corresponding plans. Results and discussion: Without division, dose differences between cumulative distribution and plan were not significant. In the condition where respiratory signals were divided, dose differences were observed over dose in cranial region and under dose in caudal region of planning target volume (PTV). Differences between 4 and 10 phases were not significant. Conclusion: The present method was feasible for evaluating cumulative dose distribution in VMAT-SBRT using 4DCT and DIR. (Keyword) lung / cumulative dose distribution / stereotactic body radiotherapy (SBRT) / volumetric modulated arc therapy (VMAT) / deformable image registration (DIR) (Link to Publication Site) ● Publication site (DOI): 10.6009/jjrt.2016_JSRT_72.3.251 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001206361099776 ● Summary page in Scopus @ Elsevier: 2-s2.0-84973470909 (DOI: 10.6009/jjrt.2016_JSRT_72.3.251, CiNii: 1390001206361099776, Elsevier: Scopus)
66.	Hideomi Yamashita, Akihiro Haga, Ryousuke Takenaka, Tomoki Kiritoshi, Kae Okuma, Kuni Ohtomo and Keiichi Nakagawa : Efficacy and feasibility of ambulatory treatment-based monthly nedaplatin plus S-1 in definitive or salvage concurrent chemoradiotherapy for early, advanced, and relapsed esophageal cancer, Radiation Oncology, Vol.11:4, 1-7, 2016. (Summary) Standard chemoradiotherapy (CRT) using cisplatin (CDDP) and 5-fluorouracil (5-FU) is an optional treatment for patients with stage II-III esophageal cancer. However, there are some demerits in this regimen because CDDP administration requires a large transfusion volume and 5-FU must be continuously infused over 24 h. Therefore, hospitalization is unavoidable. We collected retrospectively the data of definitive CRT with nedaplatin and S-1 as carried out in our institution. Patients with early and advanced esophageal cancer and relapsed esophageal cancer after radical surgery were included. Nedaplatin 80 mg/m(2) was given on days 1 and 29, and S-1 80 mg/m(2) on days 1-14 and 29-42. No prophylactic treatment with granulocyte colony stimulating factor was administered. Patients received two courses of concurrent radiotherapy of more than 50 Gy with or without two additional courses as adjuvant therapy every 4 weeks. Between August 2011 and June 2015, 89 patients (age range, 44-86 years; K-PS 90-100, 81 %; squamous cell carcinoma histology, 97 %; definitive/salvage CRT, 75/25 %) were collected. Twenty-one (24 %) patients completed four cycles, and 94 % received two or more cycles. Grade 4 leukopenia, thrombocytopenia, and anemia occurred in 12, 7, and 10 % of the patients, respectively. Five patients developed febrile neutropenia. Grade 3 non-hematological toxicity included infection in 12 %, mucositis/esophagitis in 3 %, kidney in 3 %, and fatigue in 3 %. Sixty-four patients (72 %) received the prescribed full dose and full cycles of chemotherapy. A complete response was achieved in 76 patients (85 %). The 3-year overall survival rate was 54.4 % in definitive CRT and 39.8 % in salvage CRT, respectively. Sixty-two subjects (70 %) received treatment as outpatients. Nedaplatin and S-1 in combination with radiotherapy is feasible, and toxicity is tolerable. This treatment method has the potential to shorten hospitalization without impairing the efficacy of CRT. (Keyword) Adult / Aged / Aged, 80 and over / Ambulatory Care / Antineoplastic Combined Chemotherapy Protocols / Carcinoma, Squamous Cell / Chemoradiotherapy / Cisplatin / Drug Administration Schedule / Drug Combinations / Esophageal Neoplasms / Female / Fluorouracil / Hospitalization / Humans / Kaplan-Meier Estimate / Male / Middle Aged / Organoplatinum Compounds / Oxonic Acid / Retrospective Studies / Salvage Therapy / Tegafur (Tokushima University Institutional Repository) ● Metadata: 114536 (Link to Publication Site) ● Publication site (DOI): 10.1186/s13014-016-0587-9 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 26781439 ● Summary page in Scopus @ Elsevier: 2-s2.0-84954349388 (Tokushima University Institutional Repository: 114536, DOI: 10.1186/s13014-016-0587-9, PubMed: 26781439, Elsevier: Scopus)
67.	Ritu Bhusal Chhatkuli, Kazuyuki Demachi, Naoki Miyamoto, Mitsuru Uesaka and Akihiro Haga : Dynamic Image Prediction Using Principal Component and Multi-Channel Singular Spectral Analysis: A Feasibility Study, Open Journal of Medical Imaging, Vol.5, No.03, 133-142, 2015. (Link to Publication Site) ● Publication site (DOI): 10.4236/ojmi.2015.53017 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.4236/ojmi.2015.53017 (DOI: 10.4236/ojmi.2015.53017)
68.	Hiroshi Igaki, Taiki Magome, Madoka Sakuramachi, Akihiro Nomoto, Akira Sakumi, Mayuka Kitaguchi, Akihiro Haga, Jun Itami and Keiichi Nakagawa : Patterns of Recurrence in Malignant Glioma Patients: Association with Subventricular Zone and Radiotherapy Dose, Jacobs Journal of Radiation Oncology, Vol.2, No.3, 020, 2015.
69.	木田智士, 増谷佳孝, 中野正寛, 今江禄一, 中川恵一 and Akihiro Haga : Improvement of Kilo-voltage Cone Beam CT Image Quality with Iterative Optimized Scatter Correction Algorithm, Medical Imaging Technology, Vol.33, No.3, 133-141, 2015. (Summary) In this study, we developed the iterative optimized scatter correction algorithm, which incorporates scatter correction and statistical iterative reconstruction method for improvement of CBCT image quality. For scatter estimation, scatter measurement with beam blockers and scatter simulation based on Klein-Nishina formula were combined complementally. Statistical image reconstruction (Convex method) and scatter estimation were treated parallelly and incorporated into the iterative optimized scatter correction algorithm. The validation of our proposed method was performed by analysis of the attenuation coefficients of cylindrical water phantom. (Keyword) kV CBCT / kV CBCT / Improvement of image quality / Scatter correction / Iterative reconstruction (Link to Publication Site) ● Publication site (DOI): 10.11409/mit.33.133 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001204652369792 ● Search Scopus @ Elsevier (DOI): 10.11409/mit.33.133 (DOI: 10.11409/mit.33.133, CiNii: 1390001204652369792)
70.	Hideomi Yamashita, Ryousuke Takenaka, Akira Sakumi, Akihiro Haga, Kuni Otomo and Keiichi Nakagawa : Analysis of motion of the rectum during preoperative intensity modulated radiation therapy for rectal cancer using cone-beam computed tomography, Radiation Oncology, Vol.10:2, 1-7, 2015. (Summary) The purpose of the present study was to quantify the inter-fractional motion of the rectum and the rectal and bladder volumes using CBCT scans taken during chemoradiation therapy (CRT) for rectal cancer. Also, assessment was made for a better margin for simultaneous integrated boost - intensity modulated radiation therapy (SIB-IMRT) for rectal cancer. There were 32 patients in this study undergoing preoperative CRT for rectal cancer. Each rectum and bladder was contoured on all planning CTs and CBCTs (day 1, 7, 13, 19, 25). The target volume was configured by adding margins (0, 3, 5, 7, 10, and 15 mm) to the rectum on planning CT. The respective percentage of rectal volume that exceeds the target volume was calculated for each of these margins. The percentage of bladder volume that exceeds the bladder volume in the planning CT and motion of the center of gravity of rectum were also analyzed. Planning CTs and series of each 5 CBCTs for 32 patients were analyzed in this study. The rectal volume tended to shrink week after week. The mean values (± SD) in the 32 series per patient of the percentage of rectum on the CBCTs exceeding target volume in which the margins of 0, 3, 5, 7, 10, and 15 mm were added to the rectum on planning CT were 20.7 ± 12.5%, 7.2 ± 8.3%, 3.9 ± 5.9%, 2.1 ± 3.9%, 0.7 ± 1.8%, and 0.1 ± 0.3%, respectively. No association was seen between the percentage of changes of bladder volume and motion of rectal centroid. In this study, we estimated the motion of the rectum using planning CT and CBCT. Ten to fifteen mm is a sufficient margin for the rectum during SIB-IMRT for rectal cancer in the supine position. (Keyword) Adult / Aged / Aged, 80 and over / Chemoradiotherapy / Cone-Beam Computed Tomography / Female / Humans / Male / Middle Aged / Movement / Neoplasm Staging / Preoperative Care / Radiotherapy Planning, Computer-Assisted / Radiotherapy, Intensity-Modulated / Rectal Neoplasms (Link to Publication Site) ● Publication site (DOI): 10.1186/s13014-014-0311-6 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 25566869 ● Summary page in Scopus @ Elsevier: 2-s2.0-84938869340 (DOI: 10.1186/s13014-014-0311-6, PubMed: 25566869, Elsevier: Scopus)
71.	Hideomi Yamashita, Akihiro Haga, Wataru Takahashi, Ryousuke Takenaka, Toshikazu Imae, Shigeharu Takenaka and Keiichi Nakagawa : Volumetric modulated arc therapy for lung stereotactic radiation therapy can achieve high local control rates, Radiation Oncology, Vol.9:243, 1-6, 2014. (Summary) The aim of this study was to report the outcome of primary or metastatic lung cancer patients undergoing volumetric modulated arc therapy for stereotactic body radiation therapy (VMAT-SBRT). From October 2010 to December 2013, consecutive 67 lung cancer patients received single-arc VMAT-SBRT using an Elekta-synergy system. All patients were treated with an abdominal compressor. The gross tumor volumes were contoured on 10 respiratory phases computed tomography (CT) datasets from 4-dimensional (4D) CT and merged into internal target volumes (ITVs). The planning target volume (PTV) margin was isotropically taken as 5 mm. Treatment was performed with a D95 prescription of 50 Gy (43 cases) or 55 Gy (12 cases) in 4 fractions for peripheral tumor or 56 Gy in 7 fractions (12 cases) for central tumor. Among the 67 patients, the median age was 73 years (range, 59-95 years). Of the patients, male was 72% and female 28%. The median Karnofsky performance status was 90-100% in 39 cases (58%) and 80-90% in 20 cases (30%). The median follow-up was 267 days (range, 40-1162 days). Tissue diagnosis was performed in 41 patients (61%). There were T1 primary lung tumor in 42 patients (T1a in 28 patients, T1b in 14 patients), T2 in 6 patients, three T3 in 3 patients, and metastatic lung tumor in 16 patients. The median mean lung dose was 6.87 Gy (range, 2.5-15 Gy). Six patients (9%) developed radiation pneumonitis required by steroid administration. Actuarial local control rate were 100% and 100% at 1 year, 92% and 75% at 2 years, and 92% and 75% at 3 years in primary and metastatic lung cancer, respectively (p =0.59). Overall survival rate was 83% and 84% at 1 year, 76% and 53% at 2 years, and 46% and 20% at 3 years in primary and metastatic lung cancer, respectively (p =0.12). Use of VMAT-based delivery of SBRT in primary in metastatic lung tumors demonstrates high local control rates and low risk of normal tissue complications. (Keyword) Aged / Aged, 80 and over / Carcinoma, Non-Small-Cell Lung / Female / Humans / Lung Neoplasms / Male / Middle Aged / Neoplasm Recurrence, Local / Radiosurgery / Radiotherapy Dosage / Radiotherapy Planning, Computer-Assisted / Radiotherapy, Image-Guided / Radiotherapy, Intensity-Modulated / Treatment Outcome / Tumor Burden (Link to Publication Site) ● Publication site (DOI): 10.1186/s13014-014-0243-1 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 25385062 ● Summary page in Scopus @ Elsevier: 2-s2.0-84930515116 (DOI: 10.1186/s13014-014-0243-1, PubMed: 25385062, Elsevier: Scopus)
72.	今江禄一, Akihiro Haga, 早乙女直也, 木田智士, 中野正寛, 竹中重治, 竹内幸浩, 白木尚, 矢野敬一, 山下英臣, 中川恵一 and 大友邦 : Dose Reconstruction Using Respiratory Signals and Machine Parameters during Treatment in Stereotactic Body Radiotherapy, Japanese Journal of Radiological Technology, Vol.70, No.11, 1225-1234, 2014. (Summary) <i>Purpose</i>: Volumetric modulated arc therapy (VMAT) is a rotational intensity-modulated radiotherapy (IMRT) technique capable of acquiring projection images during treatment. The purpose of this study was to reconstruct the dose distribution from respiratory signals and machine parameters acquired during stereotactic body radiotherapy (SBRT). <i>Methods</i>: The treatment plans created for VMAT-SBRT included the constraint of 1 mm/degree in multileaf collimator (MLC) for a moving phantom and three patients with lung tumors. The respiratory signals were derived from projection images acquired during VMAT delivery, while the machine parameters were derived from machine logs. The respiratory signals and machine parameters were then linked along with the gantry angle. With this data, the dose distribution of each respiratory phase was calculated on the planned four-dimensional CT (4D CT). The doses at the isocenter, the point of max dose and the centroid of the target were compared with those of the corresponding plans. <i>Results and discussion</i>: In the phantom study, the maximum dose difference between the plan and "in-treatment" results was -0.4% at the centroid of the target. In the patient study, the difference was -1.8 ± 0.4% at the centroid of the target. Dose differences of the evaluated points between 4 and 10 phases were not significant. <i>Conclusion</i>: The present method successfully reconstructed the dose distribution using the respiratory signals and machine parameters acquired during treatment. This is a feasible method for verifying the actual dose for a moving target. (Keyword) stereotactic body radiotherapy / dose distribution / respiratory signal / machine parameter / during treatment / stereotactic body radiotherapy / dose distribution / respiratory signal / machine parameter / during treatment (Link to Publication Site) ● Publication site (DOI): 10.6009/jjrt.2014_JSRT_70.11.1225 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001206362475264 ● Search Scopus @ Elsevier (DOI): 10.6009/jjrt.2014_JSRT_70.11.1225 (DOI: 10.6009/jjrt.2014_JSRT_70.11.1225, CiNii: 1390001206362475264)
73.	Akihiro Haga, Keiichi Nakagawa, Calvin Maurer, Ken Ruchala, Edward Chao, Dylan Casey, Satoshi Kida, Dousatsu Sakata, Masahiro Nakano, Taiki Magome and Yoshitaka Masutani : Reconstruction of the treatment area by use of sinogram in helical tomotherapy, Radiation Oncology, Vol.9:252, 1-6, 2014. (Link to Publication Site) ● Publication site (DOI): 10.1186/s13014-014-0252-0 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84964697755 (DOI: 10.1186/s13014-014-0252-0, Elsevier: Scopus)
74.	Hideomi Yamashita, Wataru Takahashi, Akihiro Haga, Satoshi Kida, Naoya Saotome and Keiichi Nakagawa : Stereotactic Body Radiotherapy for Small Lung Tumors in the University of Tokyo Hospital, BioMed Research International, Vol.2014, No.Article ID 136513, 2014. (Summary) Our work on stereotactic body radiation therapy (SBRT) for primary and metastatic lung tumors will be described. The eligibility criteria for SBRT, our previous SBRT method, the definition of target volume, heterogeneity correction, the position adjustment using four-dimensional cone-beam computed tomography (4D CBCT) immediately before SBRT, volumetric modulated arc therapy (VMAT) method for SBRT, verifying of tumor position within internal target volume (ITV) using in-treatment 4D-CBCT during VMAT-SBRT, shortening of treatment time using flattening-filter-free (FFF) techniques, delivery of 4D dose calculation for lung-VMAT patients using in-treatment CBCT and LINAC log data with agility multileaf collimator, and SBRT method for centrally located lung tumors in our institution will be shown. In our institution, these efforts have been made with the goal of raising the local control rate and decreasing adverse effects after SBRT. (Keyword) Cone-Beam Computed Tomography / Dose-Response Relationship, Radiation / Hospitals, University / Humans / Image Processing, Computer-Assisted / Japan / Lung / Lung Neoplasms / Phantoms, Imaging / Pressure / Radiosurgery / Radiotherapy Dosage / Radiotherapy, Intensity-Modulated (Link to Publication Site) ● Publication site (DOI): 10.1155/2014/136513 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 25110653 ● Summary page in Scopus @ Elsevier: 2-s2.0-84904699538 (DOI: 10.1155/2014/136513, PubMed: 25110653, Elsevier: Scopus)
75.	Kenshiro Shiraishi, Masahiko Futaguchi, Akihiro Haga, Akira Sakumi, Katsutake Sasaki, Kentaro Yamamoto, Hiroshi Igaki, Kuni Ohtomo, Kiyoshi Yoda and Keiichi Nakagawa : Validation of Planning Target Volume Margins by Analyzing Intrafractional Localization Errors for 14 Prostate Cancer Patients Based on Three-Dimensional Cross-Correlation between the Prostate Images of Planning CT and Intrafraction Cone-Beam CT during Volumetric Modulated Arc Therapy, BioMed Research International, Vol.2014, No.Article ID 960928, 2014. (Summary) Time-averaged intreatment prostate localization errors were calculated, for the first time, by three-dimensional prostate image cross-correlation between planning CT and intrafraction kilovoltage cone-beam CT (CBCT) during volumetric modulated arc therapy (VMAT). The intrafraction CBCT volume was reconstructed by an inhouse software after acquiring cine-mode projection images during VMAT delivery. Subsequently, the margin between a clinical target volume and a planning target volume (PTV) was obtained by applying the van Herk and variant formulas using the calculated localization errors. The resulting PTV margins were approximately 2 mm in lateral direction and 4 mm in craniocaudal and anteroposterior directions, which are consistent with the margin prescription employed in our facility. (Keyword) Aged / Aged, 80 and over / Cone-Beam Computed Tomography / Dose Fractionation / Humans / Imaging, Three-Dimensional / Male / Middle Aged / Prostatic Neoplasms / Radiometry / Radiotherapy Dosage / Radiotherapy Planning, Computer-Assisted / Radiotherapy, Image-Guided / Radiotherapy, Intensity-Modulated / Reproducibility of Results / Sensitivity and Specificity / Statistics as Topic / Tumor Burden (Link to Publication Site) ● Publication site (DOI): 10.1155/2014/960928 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 24977167 ● Summary page in Scopus @ Elsevier: 2-s2.0-84902211901 (DOI: 10.1155/2014/960928, PubMed: 24977167, Elsevier: Scopus)
76.	今江禄一, Akihiro Haga, 早乙女直也, 竹中重治, 岡野由香里, 佐々木克剛, 根津誠, 三枝茂輝, 白木尚, 矢野敬一, 中川恵一 and 大友邦 : Winston-Lutz Test and Acquisition of Flexmap Using Rotational Irradiation, Japanese Journal of Radiological Technology, Vol.70, No.4, 359-368, 2014. (Summary) <i>Purpose</i>: IGRT (image guided radiation therapy) is a useful technique for implementing precisely targeted radiation therapy. Quality assurance and quality control (QA/QC) medical linear accelerators with a portal imaging system (electronic portal imaging device: EPID) are the key to ensuring safe IGRT. The Winston-Lutz test (WLT) provides an evaluation of the MV isocenter, which is the intersection of radiation, collimator, and couch isocenters. A flexmap can indicate a displacement of EPID from the beam center axis as a function of gantry angles which can be removed from the images. The purpose of this study was to establish a novel method for simultaneously carrying out WLT and acquiring a flexmap using rotational irradiation. We also observed long-term changes in flexmaps over a period of five months. <i>Method</i>: We employed rotational irradiation with a rectangular field (30×30 mm). First, the displacement of EPID from the beam center axis, indicated by the ball bearing (BB) center, was evaluated using an in-house program. The location of the BB center was then modified according to WLT. Second, a second irradiation was used to acquire a flexmap. We performed this examination regularly and evaluated long-term changes in the flexmap. <i>Results and discussion</i>: It proved feasible to perform WLT and flexmap measurements using our proposed methods. The precision of WLT using rotational irradiation was 0.1 mm. In flexmap analysis, the maximum displacement from the mean value for each angle was 0.4 mm over five months. <i>Conclusion</i>: We have successfully established a novel method of simultaneously carrying out WLT and flexmap acquisition using rotational irradiation. Maximum displacement from the mean in each angle was 0.4 mm over five months. (Keyword) Winston-Lutz test / flexmap / rotational irradiation / quality assurance (QA) / electronic portal imaging device (EPID) / Winston-Lutz test / flexmap / rotational irradiation / quality assurance (QA) / electronic portal imaging device (EPID) (Link to Publication Site) ● Publication site (DOI): 10.6009/jjrt.2014_JSRT_70.4.359 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282681339710848 ● Summary page in Scopus @ Elsevier: 2-s2.0-84902548926 (DOI: 10.6009/jjrt.2014_JSRT_70.4.359, CiNii: 1390282681339710848, Elsevier: Scopus)
77.	Akihiro Haga, Taiki Magome, Shigeharu Takenaka, Toshikazu Imae, Akira Sakumi, Akihiro Nomoto, Hiroshi Igaki, Kenshiro Shiraishi, Hideomi Yamashita, Kuni Ohtomo and Keiichi Nakagawa : Independent Absorbed Dose Calculation Using the Monte Carlo Algorithm in Volumetric Modulated Arc Therapy, Radiation Oncology, Vol.9:75, 1-9, 2014. (Summary) To report the result of independent absorbed-dose calculations based on a Monte Carlo (MC) algorithm in volumetric modulated arc therapy (VMAT) for various treatment sites. All treatment plans were created by the superposition/convolution (SC) algorithm of SmartArc (Pinnacle V9.2, Philips). The beam information was converted into the format of the Monaco V3.3 (Elekta), which uses the X-ray voxel-based MC (XVMC) algorithm. The dose distribution was independently recalculated in the Monaco. The dose for the planning target volume (PTV) and the organ at risk (OAR) were analyzed via comparisons with those of the treatment plan.Before performing an independent absorbed-dose calculation, the validation was conducted via irradiation from 3 different gantry angles with a 10- × 10-cm2 field. For the independent absorbed-dose calculation, 15 patients with cancer (prostate, 5; lung, 5; head and neck, 3; rectal, 1; and esophageal, 1) who were treated with single-arc VMAT were selected. To classify the cause of the dose difference between the Pinnacle and Monaco TPSs, their calculations were also compared with the measurement data. In validation, the dose in Pinnacle agreed with that in Monaco within 1.5%. The agreement in VMAT calculations between Pinnacle and Monaco using phantoms was exceptional; at the isocenter, the difference was less than 1.5% for all the patients. For independent absorbed-dose calculations, the agreement was also extremely good. For the mean dose for the PTV in particular, the agreement was within 2.0% in all the patients; specifically, no large difference was observed for high-dose regions. Conversely, a significant difference was observed in the mean dose for the OAR. For patients with prostate cancer, the mean rectal dose calculated in Monaco was significantly smaller than that calculated in Pinnacle. There was no remarkable difference between the SC and XVMC calculations in the high-dose regions. The difference observed in the low-dose regions may have arisen from various causes such as the intrinsic dose deviation in the MC calculation, modeling accuracy, and CT-to-density table used in each planning system It is useful to perform independent absorbed-dose calculations with the MC algorithm in intensity-modulated radiation therapy commissioning. (Keyword) Algorithms / Head and Neck Neoplasms / Humans / Intestinal Neoplasms / Lung Neoplasms / Male / Monte Carlo Method / Phantoms, Imaging / Prostatic Neoplasms / Radiometry / Radiotherapy Dosage / Radiotherapy Planning, Computer-Assisted / Radiotherapy, Intensity-Modulated / Retrospective Studies / Validation Studies as Topic (Link to Publication Site) ● Publication site (DOI): 10.1186/1748-717X-9-75 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 24625221 ● Summary page in Scopus @ Elsevier: 2-s2.0-84899154771 (DOI: 10.1186/1748-717X-9-75, PubMed: 24625221, Elsevier: Scopus)
78.	Keiishi Nakagawa, Akihiro Haga, Akira Sakumi, Hideomi Yamashita, Hiroshi Igaki, Takashi Shiraki, Kuni Ohtomo, Yoshio Iwai and Kiyoshi Yoda : Impact of flattening-filter-free techniques on delivery time for lung stereotactic volumetric modulated arc therapy and image quality of concurrent kilovoltage cone-beam computed tomography: a preliminary phantom study, Journal of Radiation Research, Vol.55, No.1, 200-202, 2013. (Keyword) Cone-Beam Computed Tomography / Dose Fractionation / Humans / Lung Neoplasms / Phantoms, Imaging / Pilot Projects / Radiographic Image Interpretation, Computer-Assisted / Radiosurgery / Radiotherapy, Image-Guided / Radiotherapy, Intensity-Modulated / Reproducibility of Results / Sensitivity and Specificity (Link to Publication Site) ● Publication site (DOI): 10.1093/jrr/rrt105 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 23979078 ● Summary page in Scopus @ Elsevier: 2-s2.0-84892684770 (DOI: 10.1093/jrr/rrt105, PubMed: 23979078, Elsevier: Scopus)
79.	Wataru Takahashi, Hideomi Yamashita, Satoshi Kida, Yoshitaka Masutani, Akira Sakumi, Kuni Ohtomo, Keiichi Nakagawa and Akihiro Haga : Verification of Planning Target Volume Settings in Volumetric Modulated Arc Therapy for Stereotactic Body Radiation Therapy by Using In-Treatment 4-Dimensional Cone Beam Computed Tomography, International Journal of Radiation OncologyBiologyPhysics, Vol.86, No.3, 426-431, 2013. (Summary) To evaluate setup error and tumor motion during beam delivery by using 4-dimensional cone beam computed tomography (4D CBCT) and to assess the adequacy of the planning target volume (PTV) margin for lung cancer patients undergoing volumetric modulated arc therapy for stereotactic body radiation therapy (VMAT-SBRT). Fifteen lung cancer patients treated by single-arc VMAT-SBRT were selected in this analysis. All patients were treated with an abdominal compressor. The gross tumor volumes were contoured on maximum inspiration and maximum expiration CT datasets from 4D CT respiratory sorting and merged into internal target volumes (ITVs). The PTV margin was isotropically taken as 5 mm. Registration was automatically performed using "pre-3D" CBCT. Treatment was performed with a D95 prescription of 50 Gy delivered in 4 fractions. The 4D tumor locations during beam delivery were determined using in-treatment 4D CBCT images acquired in each fraction. Then, the discrepancy between the actual tumor location and the ITV was evaluated in the lateral, vertical, and longitudinal directions. Overall, 55 4D CBCT sets during VMAT-SBRT were successfully obtained. The amplitude of tumor motion was less than 10 mm in all directions. The average displacements between ITV and actual tumor location during treatment were 0.41 ± 0.93 mm, 0.15 ± 0.58 mm, and 0.60 ± 0.99 mm for the craniocaudal, left-right, and anteroposterior directions, respectively. The discrepancy in each phase did not exceed 5 mm in any direction. With in-treatment 4D CBCT, we confirmed the required PTV margins when the registration for moving target was performed using pre-3D CBCT. In-treatment 4D CBCT is a direct method for quantitatively assessing the intrafractional location of a moving target. (Keyword) Aged / Aged, 80 and over / Algorithms / Cone-Beam Computed Tomography / Dose Fractionation / Female / Four-Dimensional Computed Tomography / Humans / Lung Neoplasms / Male / Middle Aged / Movement / Radiosurgery / Radiotherapy Planning, Computer-Assisted / Radiotherapy Setup Errors / Radiotherapy, Image-Guided / Radiotherapy, Intensity-Modulated / respiration / Tumor Burden (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ijrobp.2013.02.019 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 23562767 ● Summary page in Scopus @ Elsevier: 2-s2.0-84880625785 (DOI: 10.1016/j.ijrobp.2013.02.019, PubMed: 23562767, Elsevier: Scopus)
80.	Akihiro Haga, Akira Sakumi, Yukari Okano, Saori Itoh, Naoya Saotome, Satoshi Kida, Hiroshi Igaki, Kenshiro Shiraishi, Hideomi Yamashita, Kuni Ohtomo and Keiichi Nakagawa : Dose verification of volumetric modulated arc therapy (VMAT) by use of in-treatment linac parameters, Radiological Physics and Technology, Vol.6, No.2, 335-342, 2013. (Summary) Linac parameters such as the multi-leaf collimator (MLC) position and jaw position, cumulative monitor units (MUs), and the corresponding gantry angle were recorded during the clinical delivery of volumetric modulated arc therapy for prostate, lung, and head/neck cancer patients. Then, linac parameters were converted into the beam-data format used in the treatment planning system, and the dose distribution was reconstructed. The dose-volume histogram and the dose difference (DD) were compared with the corresponding values in the treatment plan. A reproducible error of in-treatment linac parameters was observed when a sudden change of beam intensity or MLC/jaw speed occurred. The maximum cumulative MU error was more than 4 MU for lung cancer cases, and the maximum MLC position exceeded 5 mm for prostate and head/neck cancer patients. However, these errors were quickly compensated for at the next control point. All treatments analyzed in the present study were delivered within 0.4% accuracy at the planning target volume. The cumulative dose agreed with that of the plan within 3% of the prescribed dose. The 1% DD was 93.9, 99.9, and 93.4% of the prescription dose for prostate, lung, and head/neck cancer patients, respectively. (Keyword) Computer Simulation / Head and Neck Neoplasms / Humans / Lung Neoplasms / Male / Prostatic Neoplasms / Quality Assurance, Health Care / Radiotherapy Dosage / Radiotherapy Planning, Computer-Assisted / Radiotherapy, Intensity-Modulated / software (Link to Publication Site) ● Publication site (DOI): 10.1007/s12194-013-0205-6 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 23479401 ● Summary page in Scopus @ Elsevier: 2-s2.0-84880286583 (DOI: 10.1007/s12194-013-0205-6, PubMed: 23479401, Elsevier: Scopus)
81.	Motohiro Kawashima, Shuichi Ozawa, Akihiro Haga, Akira Sakumi, Chie Kurokawa, Satoru Sugimoto, Kumiko Karasawa, Keiichi Nakagawa and Keisuke Sasai : Comparison of total MU and segment areas in VMAT and step-and-shoot IMRT plans, Radiological Physics and Technology, Vol.6, No.1, 14-20, 2013. (Summary) We compared treatment plans for volumetric intensity-modulated arc therapy (VMAT) and step-and-shoot intensity-modulated radiation therapy (IMRT) in terms of their monitor unit (MU) and segment area at each control point to investigate the difference between the two methods. We investigated three sites: prostate (three cases), head and neck (three cases), and pleura (two cases). We used the total MU and the MU weighted average of segment area (MWSA) in each plan to compare VMAT and IMRT plans. VMAT plans tended to have a larger MWSA and a lower total MU than did IMRT plans in all sites, although there was little difference between dose indices in either irradiation technique. We conclude that VMAT is a better treatment technique due to its higher MU efficiency caused by the larger segment area. (Keyword) Humans / Male / Neoplasms / Radiotherapy Dosage / Radiotherapy Planning, Computer-Assisted / Radiotherapy, Intensity-Modulated (Link to Publication Site) ● Publication site (DOI): 10.1007/s12194-012-0164-3 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 22752600 ● Summary page in Scopus @ Elsevier: 2-s2.0-84873155811 (DOI: 10.1007/s12194-012-0164-3, PubMed: 22752600, Elsevier: Scopus)
82.	今江禄一, Akihiro Haga, 木田智士, 早乙女直也, 白木尚, 矢野敬一, 中川恵一 and 篠原広行 : Motion Analysis of Target during Stereotactic Radiotherapy of Lung Tumors, Medical Imaging Technology, Vol.30, No.5, 262-267, 2012. (Summary) Volumetric modulated arc therapy (VMAT) is a rotational intensity-modulated radiotherapy (IMRT) technique capable of acquiring projection images for cone-beam computed tomography (CBCT). CBCT during treatment is expected to evaluate the actual tumor position and dose distribution. The goal of our research is to establish a new method of verification during treatment in stereotactic body radiotherapy. In this study, we evaluated the movement of the target "in treatment" using kilo volts X-ray beams for diagnosis (kV-CBCT) and mega volts X-ray beams for treatment (MV-CBCT). One patient with lung tumor underwent CT scans for radiotherapy planning and was treated by VMAT while acquiring projection images for kV and MV-CBCT. The datasets were reconstructed from the projection images using in-house programs. The position of the target in kV and MV-CBCT were detected using radiotherapy planning system and the movement of the targets between kV and MV-CBCT was similar. We have successfully evaluated the movement of the target "in treatment" using kV and MV-CBCT. (Keyword) Chest / Stereotactic radiotherapy / Cone beam computed tomography (CBCT) / During treatment / Motion analysis (Link to Publication Site) ● Publication site (DOI): 10.11409/mit.30.262 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282679629104896 ● Search Scopus @ Elsevier (DOI): 10.11409/mit.30.262 (DOI: 10.11409/mit.30.262, CiNii: 1390282679629104896)
83.	Hideomi Yamashita, Kae Ohkuma, Wataru Takhashi, Akira Sakumi, Akihiro Haga, Kenji Ino, Masaaki Akahane, Kuni Ohtomo and Keiichi Nakagawa : Four-dimensional measurement of the displacement of metal clips or postoperative surgical staples during 320-multislice computed tomography scanning of gastric cancer, Radiation Oncology, Vol.7, 137, 2012. (Summary) To investigate the respiratory motion of metal clips or surgical staples placed in the gastric wall for planning of radiation therapy in gastric cancer patients. This study examined 15 metal markers in the gastric walls of 12 patients with gastric cancer treated with external-beam photon RT. Motion assessment was analyzed in 41 respiratory phases covering 20 s acquired with computed tomography (CT) in the RT position using 320-multislice CT. The intra-fraction displacement was assessed in the cranio-caudal (CC), antero-posterior (AP), and right-left (RL) directions. Motion in the CC direction showed a very strong correlation (R2 > 0.7) with the respiratory curve in all 15 markers. The mean (+/- SD) intra-fractional gastric motion (maximum range of displacement) was 12.5 (+/- 3.4) mm in the CC, 8.3 (+/- 2.2) mm in the AP, and 5.5 (+/- 3.0) mm in the RL direction. No significant differences in magnitude of motion were detected in the following: a) among the upper (n = 6), middle (n = 4), and lower (n = 5) stomach regions; b) between metal clips (n = 5) and surgical staples (n = 10); and c) between full (n = 9) and empty (n = 6) stomachs. Motion in primary gastric tumor was evaluated with 320-multislice CT. According to this study, the 95th percentile values from the cumulative distributions of the RL, AP, and CC direction were 6.3 mm, 9.0 mm, and 13.6 mm, respectively. (Keyword) Aged / Aged, 80 and over / Combined Modality Therapy / Female / Follow-Up Studies / Four-Dimensional Computed Tomography / Humans / Male / Metals / Middle Aged / Movement / Multidetector Computed Tomography / Neoplasm Staging / Postoperative Period / Prognosis / Radiotherapy Planning, Computer-Assisted / Respiration / Stomach Neoplasms / Surgical Stapling (Link to Publication Site) ● Publication site (DOI): 10.1186/1748-717X-7-137 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 22883343 ● Summary page in Scopus @ Elsevier: 2-s2.0-84864822876 (DOI: 10.1186/1748-717X-7-137, PubMed: 22883343, Elsevier: Scopus)
84.	Keiichi Nakagawa, Akihiro Haga, Satoshi Kida, Yoshitaka Masutani, Hideomi Yamashita, Wataru Takahashi, Akira Sakumi, Naoya Saotome, Takashi Shiraki, Kuni Ohtomo, Yoshio Iwai and Yoda Kiyoshi : 4D registration and 4D verification of lung tumor position for stereotactic volumetric modulated arc therapy using respiratory- correlated cone-beam CT, Journal of Radiation Research, Vol.54, No.1, 152-156, 2012. (Summary) We propose a clinical workflow of stereotactic volumetric modulated arc therapy (VMAT) for a lung tumor from planning to tumor position verification using 4D planning computed tomography (CT) and 4D cone-beam CT (CBCT). A 4D CT scanner, an Anzai belt and a BodyFix were employed to obtain 10-phase respiratory-correlated CT data for a lung patient under constrained breathing conditions. A planning target volume (PTV) was defined by adding a 5-mm margin to an internal target volume created from 10 clinical target volumes, each of which was delineated on each of the 10-phase planning CT data. A single-arc VMAT plan was created with a D(95) prescription dose of 50 Gy in four fractions on the maximum exhalation phase CT images. The PTV contours were exported to a kilovoltage CBCT X-ray Volume Imaging (XVI) equipped with a linear accelerator (linac). Immediately before treatment, 10-phase 4D CBCT images were reconstructed leading to animated lung tumor imaging. Initial bone matching was performed between frame-averaged 4D planning CT and frame-averaged 4D CBCT datasets. Subsequently, the imported PTV contours and the animated moving tumor were simultaneously displayed on the XVI monitor, and a manual 4D registration was interactively performed on the monitor until the moving tumor was symmetrically positioned inside the PTV. A VMAT beam was delivered to the patient and during the delivery further 4D CBCT projection data were acquired to verify the tumor position. The entire process was repeated for each fraction. It was confirmed that the moving tumor was positioned inside the PTV during the VMAT delivery. (Keyword) Four-Dimensional Computed Tomography / Humans / Lung Neoplasms / Radiosurgery / Radiotherapy, Image-Guided / Radiotherapy, Intensity-Modulated / Reproducibility of Results / Respiratory-Gated Imaging Techniques / Sensitivity and Specificity / Subtraction Technique (Link to Publication Site) ● Publication site (DOI): 10.1093/jrr/rrs058 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 22843380 ● Summary page in Scopus @ Elsevier: 2-s2.0-84872010349 (DOI: 10.1093/jrr/rrs058, PubMed: 22843380, Elsevier: Scopus)
85.	Hideomi Yamashita, Kae Ohkuma, Keiichiro Tada, Kenshiro Shiraishi, Wataru Takahashi, Shino Shibata, Akira Sakumi, Naoya Saotome, Akihiro Haga, Tsuyoshi Onoe, Kenji Ino, Masaaki Akahane, Kuni Ohtomo and Keiichi Nakagawa : Four-Dimensional Measurement of the Displacement of Internal Fiducial and Skin Markers During 320-Multislice Computed Tomography Scanning of Breast Cancer, International Journal of Radiation OncologyBiologyPhysics, Vol.84, No.2, 331-335, 2012. (Summary) To study the three-dimensional movement of internal tumor bed fiducial and breast skin markers, using 320-multislice computed tomography (CT); and to analyze intrafractional errors for breast cancer patients undergoing breast irradiation. This study examined 280 markers on the skin of the breast (200 markers) and on the primary tumor bed (80 markers) of 20 patients treated by external-beam photon radiotherapy. Motion assessment was analyzed in 41 respiratory phases during 20 s of cine CT in the radiotherapy position. To assess intrafractional errors resulting from respiratory motion, four-dimensional CT scans were acquired for 20 patients. Motion in the anterior-posterior (A/P) and superior-inferior (S/I) directions showed a strong correlation (\|r\| > 0.7) with the respiratory curve for most markers (79% and 70%, respectively). The average marker displacements between maximum and minimum value during 20 s for the 200 breast skin metal markers were 1.1 ± 0.3 mm, 2.1 ± 0.6 mm, and 1.6 ± 0.4 mm in the left-right, A/P, and S/I directions, respectively. For the 80 tumor bed clips, displacements were 0.9 ± 0.2 mm in left-right, 1.7 ± 0.5 mm in A/P, and 1.1 ± 0.3 mm in S/I. There was no significant difference in the motion between breast quadrant regions or between the primary site and the other regions. Motion in primary breast tumors was evaluated with 320-multislice CT. Very little change was detected during individual radiation treatment fractions. (Keyword) Adult / Aged / Breast Neoplasms / Dose Fractionation / Female / Fiducial Markers / Four-Dimensional Computed Tomography / Humans / Japan / Lung / Lung Volume Measurements / Mastectomy, Segmental / Middle Aged / Movement / Multidetector Computed Tomography / respiration / Tumor Burden (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ijrobp.2011.12.030 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 22436784 ● Summary page in Scopus @ Elsevier: 2-s2.0-84865633471 (DOI: 10.1016/j.ijrobp.2011.12.030, PubMed: 22436784, Elsevier: Scopus)
86.	Satoshi Kida, Yoshitaka Masutani, Hideomi Yamashita, Toshikazu Imae, Taeko Matsuura, Naoya Saotome, Kuni Ohtomo, Keiichi Nakagawa and Akihiro Haga : In-treatment 4D cone-beam CT with image-based respiratory phase recognition, Radiological Physics and Technology, Vol.5, No.2, 138-147, 2012. (Summary) The use of respiration-correlated cone-beam computed tomography (4D-CBCT) appears to be crucial for implementing precise radiation therapy of lung cancer patients. The reconstruction of 4D-CBCT images requires a respiratory phase. In this paper, we propose a novel method based on an image-based phase recognition technique using normalized cross correlation (NCC). We constructed the respiratory phase by searching for a region in an adjacent projection that achieves the maximum correlation with a region in a reference projection along the cranio-caudal direction. The data on 12 lung cancer patients acquired just prior to treatment and on 3 lung cancer patients acquired during volumetric modulated arc therapy treatment were analyzed in the search for the effective area of cone-beam projection images for performing NCC with 12 combinations of registration area and segment size. The evaluation was done by a "recognition rate" defined as the ratio of the number of peak inhales detected with our method to that detected by eye (manual tracking). The average recognition rate of peak inhale with the most efficient area in the present method was 96.4%. The present method was feasible even when the diaphragm was outside the field of view. With the most efficient area, we reconstructed in-treatment 4D-CBCT by dividing the breathing signal into four phase bins; peak exhale, peak inhale, and two intermediate phases. With in-treatment 4D-CBCT images, it was possible to identify the tumor position and the tumor size in moments of inspiration and expiration, in contrast to in-treatment CBCT reconstructed with all projections. (Keyword) Cone-Beam Computed Tomography / Four-Dimensional Computed Tomography / Image Processing, Computer-Assisted / Radiotherapy, Computer-Assisted / respiration (Link to Publication Site) ● Publication site (DOI): 10.1007/s12194-012-0146-5 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 22367851 ● Summary page in Scopus @ Elsevier: 2-s2.0-84866425750 (DOI: 10.1007/s12194-012-0146-5, PubMed: 22367851, Elsevier: Scopus)
87.	Wataru Takahashi, Hideomi Yamashita, Naoya Saotome, Yoshio Iwai, Akira Sakumi, Akihiro Haga and Keiichi Nakagawa : Evaluation of heterogeneity dose distributions for Stereotactic Radiotherapy (SRT): Comparison of commercially available Monte Carlo dose calculation with other algorithms, Radiation Oncology, Vol.7:20, No.2012, 1-8, 2012. (Summary) The purpose of this study was to compare dose distributions from three different algorithms with the x-ray Voxel Monte Carlo (XVMC) calculations, in actual computed tomography (CT) scans for use in stereotactic radiotherapy (SRT) of small lung cancers. Slow CT scan of 20 patients was performed and the internal target volume (ITV) was delineated on Pinnacle3. All plans were first calculated with a scatter homogeneous mode (SHM) which is compatible with Clarkson algorithm using Pinnacle3 treatment planning system (TPS). The planned dose was 48 Gy in 4 fractions. In a second step, the CT images, structures and beam data were exported to other treatment planning systems (TPSs). Collapsed cone convolution (CCC) from Pinnacle3, superposition (SP) from XiO, and XVMC from Monaco were used for recalculating. The dose distributions and the Dose Volume Histograms (DVHs) were compared with each other. The phantom test revealed that all algorithms could reproduce the measured data within 1% except for the SHM with inhomogeneous phantom. For the patient study, the SHM greatly overestimated the isocenter (IC) doses and the minimal dose received by 95% of the PTV (PTV95) compared to XVMC. The differences in mean doses were 2.96 Gy (6.17%) for IC and 5.02 Gy (11.18%) for PTV95. The DVH's and dose distributions with CCC and SP were in agreement with those obtained by XVMC. The average differences in IC doses between CCC and XVMC, and SP and XVMC were -1.14% (p = 0.17), and -2.67% (p = 0.0036), respectively. Our work clearly confirms that the actual practice of relying solely on a Clarkson algorithm may be inappropriate for SRT planning. Meanwhile, CCC and SP were close to XVMC simulations and actual dose distributions obtained in lung SRT. (Keyword) Algorithms / Humans / Lung Neoplasms / Monte Carlo Method / Phantoms, Imaging / Radiosurgery / Radiotherapy Dosage / Radiotherapy Planning, Computer-Assisted / Retrospective Studies / Tomography, X-Ray Computed (Link to Publication Site) ● Publication site (DOI): 10.1186/1748-717X-7-20 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 22315950 ● Summary page in Scopus @ Elsevier: 2-s2.0-84856810289 (DOI: 10.1186/1748-717X-7-20, PubMed: 22315950, Elsevier: Scopus)
88.	Hideomi Yamashita, Keiichi Nakagawa, Kae Okuma, Akira Sakumi, Akihiro Haga, Reiko Kobayashi and Kuni Ohtomo : Correlation Between Bladder Volume and Irradiated Dose of Small Bowel in CT-based Planning of Intracavitary Brachytherapy for Cervical Cancer, Japanese Journal of Clinical Oncology, Vol.42, No.4, 302-308, 2012. (Summary) To quantify the effect of bladder volume on the dose distribution of intracavitary brachytherapy in computed tomography-based treatment planning for cervical cancer. Ten patients with cervical cancer were treated with high-dose rate radiation brachytherapy. For the three-dimensional analysis, pelvic computed tomographic scans were obtained from patients with indwelling catheters in place and from patients who received 50, 100, 150 and 200 cc injections of sterile water into their bladders ('200 cc' was defined as a full bladder). Additionally, scans were made in the prone position with the full bladder. Bladder fullness significantly affected the dose to the small bowel and bladder. The median of maximal doses to the small bowel was significantly greater with an empty bladder in all factors of hot spot (480 vs. 256 cGy on D-2cc). Although dosimetry revealed lower doses for larger volumes of bladder (D-50 and V-25%), the median maximal dose to the bladder was significantly greater with a full bladder (420 vs. 775 cGy on D-2cc). The rectosigmoid doses were not affected by bladder distension (476 vs. 467 cGy on D-2cc). After changing to the prone position, the hot spot dose of small bowel did not change but that of the bladder significantly decreased, although this procedure was very difficult. An increase in bladder volume resulted in a significant reduction in the hot spot dose of the small bowel at the expense of an increase in that of the bladder without changing the dose distribution of the rectosigmoid. (Keyword) Adult / Aged / Aged, 80 and over / Brachytherapy / Female / Humans / Intestine, Small / Middle Aged / Radiation Dosage / Radiotherapy Planning, Computer-Assisted / Tomography, X-Ray Computed / Urinary Bladder / Uterine Cervical Neoplasms (Link to Publication Site) ● Publication site (DOI): 10.1093/jjco/hyr203 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 22301719 ● Summary page in Scopus @ Elsevier: 2-s2.0-84859231963 (DOI: 10.1093/jjco/hyr203, PubMed: 22301719, Elsevier: Scopus)
89.	Keiichi Nakagawa, Satoshi Kida, Akihiro Haga, Yoshitaka Masutani, Hideomi Yamashita, Tsuyoshi Onoe, Toshikazu Imae, Kenichiro Tanaka, Kuni Ohtomo and Kiyoshi Yoda : 4D digitally reconstructed radiography for verifying a lung tumor position during volumetric modulated arc therapy, Journal of Radiation Research, Vol.53, No.4, 628-632, 2012. (Summary) We have proposed four dimensional (4D) digitally reconstructed radiography (DRR) for verifying a lung tumor position during volumetric modulated arc therapy (VMAT). An internal target volume (ITV) was defined based on two clinical target volumes (CTVs) delineated on maximum exhalation and maximum inhalation images acquired by 4D planning computed tomography (CT). A planning target volume (PTV) was defined by adding a margin of 5 mm to the ITV on the maximum exhalation 3D CT images. A single-arc VMAT plan was created on the same CT data using Pinnacle SmartArc with a maximum multi-leaf collimator leaf speed of 1 mm/degree, thereby resulting in quasi-conformal field shapes while optimizing each beam intensity for each gantry angle. During VMAT delivery, cone-beam CT (CBCT) projection data were acquired by an on-board kilovoltage X-ray unit and a flat panel 2D detector. Four CBCT image sets with different respiratory phases were reconstructed using in-house software, where respiratory phases were extracted from the projection data. Subsequently a CTV was delineated on each of the 4D CBCT images by an oncologist. Using the resulting 4D CBCT data including the CTV contours, 4D DRRs during the VMAT delivery were calculated as a function of gantry angle. It was confirmed that the contoured CTV was within the radiation field during the four-fraction lung VMAT delivery. The proposed 4D DRR may facilitate the verification of the position of a respiratory moving lung tumor during VMAT delivery on each treatment day. (Keyword) Algorithms / Cone-Beam Computed Tomography / Dose-Response Relationship, Radiation / Equipment Design / Four-Dimensional Computed Tomography / Humans / Image Processing, Computer-Assisted / Lung Neoplasms / Phantoms, Imaging / Radiographic Image Enhancement / Radiotherapy / Radiotherapy Dosage / Radiotherapy Planning, Computer-Assisted / Radiotherapy, Intensity-Modulated / Reproducibility of Results / X-Rays (Link to Publication Site) ● Publication site (DOI): 10.1093/jrr/rrs013 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 22843630 ● Search Scopus @ Elsevier (PMID): 22843630 ● Search Scopus @ Elsevier (DOI): 10.1093/jrr/rrs013 (DOI: 10.1093/jrr/rrs013, PubMed: 22843630)
90.	Akira Sakumi, Kenshiro Shiraishi, Tsuyoshi Onoe, Kentaro Yamamoto, Akihiro Haga, Kiyoshi Yoda, Kuni Ohtomo and Keiichi Nakagawa : Single-Arc Volumetric Modulated Arc Therapy Planning for Left Breast Cancer and Regional Nodes, Journal of Radiation Research, Vol.53, No.1, 151-153, 2012. (Summary) We have successfully created a single arc volumetric modulated arc therapy (VMAT) plan for treating post-surgical left breast/chest wall and regional nodes using Elekta multileaf collimator (MLC). Dose volume histograms (DVHs) were compared between the VMAT plans and conventional tangential beam plans using a field-in-field technique, leading to significant DVH advantages in the VMAT plans. The difference between Elekta VMAT and Varian RapidArc due to different MLC designs was discussed in terms of the number of arcs required to cover a large target, highlighting a single arc capability of Elekta VMAT for a large target volume which may be less sensitive to unexpected organ motion during dose delivery. (Keyword) Breast Neoplasms / Combined Modality Therapy / Female / Heart / Humans / Lung / Lymphatic Irradiation / Lymphatic Metastasis / Mastectomy / Organ Size / Organs at Risk / Patient Positioning / Radiation Injuries / Radiotherapy Dosage / Radiotherapy Planning, Computer-Assisted / Radiotherapy, Adjuvant / Thoracic Wall (Link to Publication Site) ● Publication site (DOI): 10.1269/jrr.11159 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 22240941 ● Summary page in Scopus @ Elsevier: 2-s2.0-84863416118 (DOI: 10.1269/jrr.11159, PubMed: 22240941, Elsevier: Scopus)
91.	Satoshi Kida, Naoya Saotome, Yoshitaka Masutani, Hideomi Yamashita, Kuni Ohtomo, Keiichi Nakagawa, Akira Sakumi and Akihiro Haga : 4D-CBCT reconstruction using MV portal imaging during volumetric modulated arc therapy, Radiotherapy and Oncology, Vol.100, No.3, 380-385, 2011. (Summary) Recording target motion during treatment is important for verifying the irradiated region. Recently, cone-beam computed tomography (CBCT) reconstruction from portal images acquired during volumetric modulated arc therapy (VMAT), known as VMAT-CBCT, has been investigated. In this study, we developed a four-dimensional (4D) version of the VMAT-CBCT. The MV portal images were sequentially acquired from an electronic portal imaging device. The flex, background, monitor unit, field size, and multi-leaf collimator masking corrections were considered during image reconstruction. A 4D VMAT-CBCT requires a respiratory signal during image acquisition. An image-based phase recognition (IBPR) method was performed using normalised cross correlation to extract a respiratory signal from the series of portal images. Our original IBPR method enabled us to reconstruct 4D VMAT-CBCT with no external devices. We confirmed that 4D VMAT-CBCT was feasible for two patients and in good agreement with in-treatment 4D kV-CBCT. The visibility of the anatomy in 4D VMAT-CBCT reconstruction for lung cancer patients has the potential of using 4D VMAT-CBCT as a tool for verifying relative positions of tumour for each respiratory phase. (Keyword) Algorithms / Cone-Beam Computed Tomography / Dose Fractionation / Feasibility Studies / Humans / Lung Neoplasms / Phantoms, Imaging / Radiographic Image Interpretation, Computer-Assisted / Radiotherapy Dosage / Radiotherapy Planning, Computer-Assisted / Respiration (Link to Publication Site) ● Publication site (DOI): 10.1016/j.radonc.2011.08.047 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 21963287 ● Summary page in Scopus @ Elsevier: 2-s2.0-80054088820 (DOI: 10.1016/j.radonc.2011.08.047, PubMed: 21963287, Elsevier: Scopus)
92.	伊藤広貴, 越塚誠一, Akihiro Haga and 中川恵一 : Rib Cage Motion Model Construction Based on Patient-Specific CT Images Between Inhalation and Exhalation, Medical Imaging Technology, Vol.29, No.4, 208-214, 2011. (Summary) It is important to estimate rib cage motion quantitatively because lung deformation is caused by motion of the rib cage and diaphragm. In this paper, a patient-specific model of rib cage motion between inhalation and exhalation is proposed based on anatomical knowledge and rib cage kinematics. Time-series geometric changes in the patient-specific 3D rib cage configuration (including the ribs, spine, and sternum), which is segmented based on exhalation CT images, can be simulated using the proposed method. In addition, the validity of the proposed rib cage motion model was evaluated using inhalation and exhalation CT images. (Keyword) Rib cage / Pump handle movement / Bucket handle movement / Lung deformation simulation / 4DCT (Link to Publication Site) ● Publication site (DOI): 10.11409/mit.29.208 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001204651501696 ● Search Scopus @ Elsevier (DOI): 10.11409/mit.29.208 (DOI: 10.11409/mit.29.208, CiNii: 1390001204651501696)
93.	Hideomi Yamashita, Satoshi Kida, Akira Sakumi, Akihiro Haga, Saori Ito, Takeshi Onoe, Kae Okuma, Kenji Ino, Masaaki Akahane, Kuni Ohtomo and Keiichi Nakagawa : Four-Dimensional Measurement of the Displacement of Internal Fiducial Markers During 320-Multislice Computed Tomography Scanning of Thoracic Esophageal Cancer, International Journal of Radiation OncologyBiologyPhysics, Vol.79, No.2, 588-595, 2011. (Summary) To investigate the three-dimensional movement of internal fiducial markers placed near esophageal cancers using 320-multislice CT. This study examined 22 metal markers in the esophageal wall near the primary tumors of 12 patients treated with external-beam photon radiotherapy. Motion assessment was analyzed in 41 respiratory phases during 20 s of cine CT in the radiotherapy position. Motion in the cranial-caudal (CC) direction showed a strong correlation (R(2) > 0.4) with the respiratory curve in most markers (73%). The average absolute amplitude of the marker movement was 1.5 ± 1.6 mm, 1.6 ± 1.7 mm, and 3.3 ± 3.3 mm in the left-right (LR), anterior-posterior (AP), and CC directions, respectively. The average marker displacements in the CC direction between peak exhalation and inhalation for the 22 clips were 1.1 mm (maximum, 5.5 mm), 3.0 mm (14.5 mm), and 5.1 mm (16.3 mm) for the upper, middle, and lower thoracic esophagus, respectively. Motion in primary esophagus tumor was evaluated with 320-multislice CT. According to this study, 4.3 mm CC, 1.5 mm AP, and 2.0 mm LR in the upper, 7.4 mm CC, 3.0 mm AP, and 2.4 mm LR in the middle, and 13.8 mm CC, 6.6 mm AP, and 6.8 mm LR in the lower thoracic esophagus provided coverage of tumor motion in 95% of the cases in our study population. (Keyword) Aged / Aged, 80 and over / Carcinoma, Squamous Cell / Esophageal Neoplasms / Female / Fiducial Markers / Four-Dimensional Computed Tomography / Humans / Male / Middle Aged / Motion / Movement / Radiography, Thoracic / Respiration (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ijrobp.2010.03.045 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 20678869 ● Summary page in Scopus @ Elsevier: 2-s2.0-78650814437 (DOI: 10.1016/j.ijrobp.2010.03.045, PubMed: 20678869, Elsevier: Scopus)
94.	Akira Sakumi, Akihiro Haga, Satoshi Kida, Naoya Saotome, Yukari Okano, Kenshiro Shiraishi, Takeshi Onoe, Kiyoshi Yoda, Kuni Ohtomo and Keiichi Nakagawa : First In-situ Dose Calculation Report Using In-treatment Kilovoltage Cone-beam CT and In-treatment Linac Parameters during Volumetric Modulated Arc Therapy, Journal of Radiation Research, Vol.52, No.4, 536-537, 2011. (Keyword) Cone-Beam Computed Tomography / Humans / Neoplasms / Particle Accelerators / Phantoms, Imaging / Radiotherapy / Radiotherapy Planning, Computer-Assisted / Reproducibility of Results (Link to Publication Site) ● Publication site (DOI): 10.1269/jrr.11061 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 21811044 ● Summary page in Scopus @ Elsevier: 2-s2.0-79960888249 (DOI: 10.1269/jrr.11061, PubMed: 21811044, Elsevier: Scopus)
95.	今江禄一, Akihiro Haga, 中川恵一, 井野賢司, 田中堅一郎, 岡野由香里, 佐々木克剛, 三枝茂輝, 白木尚, 折舘隆, 矢野敬一 and 篠原広行 : Motion Analysis of Target in Stereotactic Radiotherapy of Lung Tumors Using 320-row Multidetector CT, Japanese Journal of Radiological Technology, Vol.67, No.3, 202-211, 2011. (Summary) Multi-detector computed tomography (MDCT) has rapidly evolved and is increasingly used for treatment simulation of thoracic and abdominal radiotherapy. A 320-detector row CT scanner has recently become available that allows axial volumetric scanning of a 16-cm-long range in a patient without table movement. Current radiotherapy techniques require a generous margin around the presumed gross tumor volume (GTV) to account for uncertainties such as tumor motion and set up error. Motion analysis is useful to evaluate the internal margin of a moving target due to respiration and to improve therapeutic precision. The purpose of this study is to propose a method using phase-only correlation to automatically detect the target and to assess the motion of the target in numerical phantoms and patients. Free-breathing scans using 320-detector row CT were acquired for 4 patients with lung tumor(s). The proposed method was feasible for motion analysis of all numerical phantoms and patients. The results reproduced the facts that the motion of tumors in the patients varied in orbits during the respiratory cycle and exhibited hysteresis. The maximum distance between peak exhalation and inhalation increased as the tumors approached the diaphragm. The proposed method detected the three-dimensional position of the targets automatically and analyzed the trajectories. The tumor motion due to respiration differed by region and was greatest for the lower lobe. (Keyword) motion analysis / stereotactic radiotherapy / lung tumor / 320-detector row / computed tomography (Link to Publication Site) ● Publication site (DOI): 10.6009/jjrt.67.202 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282681338724224 ● Search Scopus @ Elsevier (DOI): 10.6009/jjrt.67.202 (DOI: 10.6009/jjrt.67.202, CiNii: 1390282681338724224)
96.	Keiichi Nakagawa, Satoshi Kida, Akihiro Haga, Yoshitaka Masutani, Hideomi Yamashita, Toshikazu Imae, Kenichiro Tanaka, Kuni Ohtomo, Yoshio Iwai and Kiyoshi Yoda : Cone Beam Computed Tomography Data Acquisition during VMAT Delivery with Subsequent Respiratory Phase Sorting Based on Projection Image Cross-correlation, Journal of Radiation Research, Vol.52, No.1, 112-113, 2011. (Keyword) Algorithms / Cone-Beam Computed Tomography / Humans / Imaging, Three-Dimensional / Radiographic Image Interpretation, Computer-Assisted / Radiotherapy, Computer-Assisted / Radiotherapy, Conformal / Reproducibility of Results / Respiratory Mechanics / Respiratory-Gated Imaging Techniques / Sensitivity and Specificity (Link to Publication Site) ● Publication site (DOI): 10.1269/jrr.10170 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 21293076 ● Summary page in Scopus @ Elsevier: 2-s2.0-79751478192 (DOI: 10.1269/jrr.10170, PubMed: 21293076, Elsevier: Scopus)
97.	伊藤広貴, 越塚誠一, 志野亮作, Akihiro Haga, 山下英臣, 尾上剛士 and 中川恵一 : Generation Method for Simulation-based Chest 4DCT Based on Particle Simulation for Radiotherapy, Medical Imaging Technology, Vol.28, No.4, 229-236, 2010. (Summary) In current standard radiotherapy of lung cancer, an excessive volume of normal tissues may be irradiated due to respiratory displacement. If the motion of the lungs could be predicted by computer simulation, radiation exposure to normal tissues could be reduced. Lung deformation is modeled as an elastic material. The lung model is created automatically from computed tomography (CT) images using binarization, mathematical morphology, and region growing. The effects of abdominal breathing and pleural sliding are incorporated into the boundary conditions by template matching between CT images acquired during the inhalation and exhalation phases. Lung motion is then predicted using the moving particle simulation (MPS) method. Since CT images are required for radiotherapy planning, we propose a generation method for simulation-based 4DCT equivalent to actual 4DCT using CT images acquired during the inhalation and exhalation phases and particle simulation results obtained using the image warping technique. (Keyword) Particle simulation / 4DCT / Respiratory displacement / Image warping / radiation therapy (Link to Publication Site) ● Publication site (DOI): 10.11409/mit.28.229 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001204651955840 ● Search Scopus @ Elsevier (DOI): 10.11409/mit.28.229 (DOI: 10.11409/mit.28.229, CiNii: 1390001204651955840)
98.	Hideomi Yamashita, Shino Kobayashi-Shibata, Atsushi Terahara, Kae Okuma, Akihiro Haga, Reiko Wakui, Kuni Ohtomo and Keiichi Nakagawa : Prescreening based on the presence of CT-scan abnormalities and biomarkers (KL-6 and SP-D) may reduce severe radiation pneumonitis after stereotactic radiotherapy, Radiation Oncology, Vol.32, No.1, 1-9, 2010. (Summary) To determine the risk factors of severe radiation pneumonitis (RP) after stereotactic body radiation therapy (SBRT) for primary or secondary lung tumors. From January 2003 to March 2009, SBRT was performed on 117 patients (32 patients before 2005 and 85 patients after 2006) with lung tumors (primary = 74 patients and metastatic/recurrent = 43 patients) in our institution. In the current study, the results on cases with severe RP (grades 4-5) were evaluated. Serum Krebs von den Lungen-6 (KL-6) and serum Surfactant protein-D (SP-D) were used to predict the incidence of RP. A shadow of interstitial pneumonitis (IP) on the CT image before performing SBRT was also used as an indicator for RP. Since 2006, patients have been prescreened for biological markers (KL-6 & SP-D) as well as checking for an IP-shadow in CT. Grades 4-5 RP was observed in nine patients (7.7%) after SBRT and seven of these cases (6.0%) were grade 5 in our institution. A correlation was found between the incidence of RP and higher serum KL-6 & SP-D levels. IP-shadow in patient's CT was also found to correlate well with the severe RP. Severe RP was reduced from 18.8% before 2005 to 3.5% after 2006 (p = 0.042). There was no correlation between the dose volume histogram parameters and these severe RP patients. Patients presenting with an IP shadow in the CT and a high value of the serum KL-6 & SP-D before SBRT treatment developed severe radiation pneumonitis at a high rate. The reduction of RP incidence in patients treated after 2006 may have been attributed to prescreening of the patients. Therefore, pre-screening before SBRT for an IP shadow in CT and serum KL-6 & SP-D is recommended in the management and treatment of patients with primary or secondary lung tumors. (Keyword) Adult / Aged / Aged, 80 and over / Biomarkers, Tumor / Female / Humans / Lung Diseases, Interstitial / Lung Neoplasms / Male / Middle Aged / Mucin-1 / Neoplasm Recurrence, Local / Pulmonary Surfactant-Associated Protein D / Radiation Pneumonitis / Radiosurgery / Retrospective Studies / Tomography, X-Ray Computed / Treatment Outcome (Link to Publication Site) ● Publication site (DOI): 10.1186/1748-717X-5-32 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 20459699 ● Summary page in Scopus @ Elsevier: 2-s2.0-77951959368 (DOI: 10.1186/1748-717X-5-32, PubMed: 20459699, Elsevier: Scopus)
99.	伊藤広貴, 越塚誠一, 中川恵一 and Akihiro Haga : Particle Simulation of Lung Deformation in Axial Plane by Chest Respiration and Heartbeat, Transaction of the Japan Society for Simulation Technology, Vol.2, No.3, 93-100, 2010. (Keyword) Particle simulation / 4DCT / Respiratory displacement / Radiotherapy / lung cancer (Link to Publication Site) ● Publication site (DOI): 10.11308/tjsst.2.93 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1520572359659730944 ● Search Scopus @ Elsevier (DOI): 10.11308/tjsst.2.93 (DOI: 10.11308/tjsst.2.93, CiNii: 1520572359659730944)
100.	Yamashita Hideomi, Akihiro Haga, Yayoi Hayakawa, Kae Okuma, Kiyoshi Yoda, Yukari Okano, Kenichiro Tanaka, Toshikazu Imae, Kuni Ohtomo and Keiichi Nakagawa : Patient setup error and day-to-day esophageal motion error analyzed by cone-beam computed tomography in radiation therapy, Acta Oncologica, Vol.49, No.4, 485-490, 2010. (Summary) Little has been reported on the errors of setup and daily organ motion that occur during radiation therapy (RT) for esophageal cancer. The purpose of this paper was to determine the margins of esophageal motion during RT. The shift of the esophagus was analyzed in 20 consecutive patients treated with RT for esophageal cancer from November 2007. CT images for RT planning were used as the primary image series. Computed tomography (CT) images were acquired using an Elekta Synergy System, equipped with a kilovoltage-based cone-beam CT (CBCT) unit. The subsequent CBCT image series used for daily RT setup were compared with the primary image series to analyze esophageal motion. CBCT was performed before treatment sessions a total of 10 times in each patient twice a week. The outer esophageal wall was contoured on the CBCT images of all 200 sets. In the 200 sets of CBCT images, the mean (absolute) +/- standard deviation (SD) of setup errors were 2 +/- 2 mm (max, 8 mm) in the lateral direction, 4 +/- 3 mm (max, 11 mm) in the longitudinal direction, and 4 +/- 3 mm (max, 13 mm) in the vertical direction. Additionally, the mean +/- SD values of daily esophageal motion comparing the CBCT with RT planning CT were 5 +/- 3 mm (max, 15 mm) in the lateral direction and 5 +/- 3 mm (max, 15 mm) in the vertical direction. Our data support the use of target margins (between the clinical target volume and planning target volume) of 9 mm for day-to-day esophageal motion and 8 mm for patient setup in all directions, respectively. (Keyword) Aged / Aged, 80 and over / Cone-Beam Computed Tomography / Confounding Factors (Epidemiology) / Esophageal Neoplasms / Esophagus / Female / Humans / Male / Middle Aged / Myocardial Contraction / Neoplasm Staging / Peristalsis / Radiographic Image Interpretation, Computer-Assisted / Radiotherapy Dosage / Radiotherapy Planning, Computer-Assisted / Research Design / Respiration (Link to Publication Site) ● Publication site (DOI): 10.3109/02841861003652574 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 20230211 ● Summary page in Scopus @ Elsevier: 2-s2.0-77951220454 (DOI: 10.3109/02841861003652574, PubMed: 20230211, Elsevier: Scopus)
101.	Keiichi Nakagawa, Kenshiro Shiraishi, Satoshi Kida, Akihiro Haga, Kentaro Yamamoto, Shigeki Saegusa, Atsuro Terahara, Saori Itoh, Kuni Ohtomo and Kiyoshi Yoda : First report on prostate displacements immediately before and after treatment relative to the position during VMAT delivery, Acta Oncologica, Vol.48, No.8, 1206-1208, 2009. (Keyword) Cone-Beam Computed Tomography / Humans / Male / Movement / Posture / Prostate / Prostatic Neoplasms / Radiotherapy, Conformal (Link to Publication Site) ● Publication site (DOI): 10.3109/02841860903101190 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 19863231 ● Summary page in Scopus @ Elsevier: 2-s2.0-70350784129 (DOI: 10.3109/02841860903101190, PubMed: 19863231, Elsevier: Scopus)
102.	Akihiro Haga, Keiichi Nakagawa, Kenshiro Shiraishi, Saori Itoh, Atsuro Terahara, Hideomi Yamashita, Kuni Ohtomo, Shigeki Saegusa, Toshikazu Imae, Kiyoshi Yoda and Roberto Pellegrini : Quality assurance of volumetric modulated arc therapy using Elekta Synergy, Acta Oncologica, Vol.48, No.8, 1193-1197, 2009. (Summary) PURPOSE. Recently, Elekta has supplied volumetric modulated arc therapy (VMAT) in which multi-leaf collimator (MLC) shape, jaw position, collimator angle, and gantry speed vary continuously during gantry rotation. A quality assurance procedure for VMAT delivery is described. METHODS AND MATERIALS. A single-arc VMAT plan with 73 control points (CPs) and 5-degree gantry angle spacing for a prostate cancer patient has been created by ERGO + + treatment planning system (TPS), where MLC shapes are given by anatomic relationship between a target and organs at risk and the monitor unit for each CP is optimized based on given dose prescriptions. Actual leaf and jaw positions, gantry angles and dose rates during prostate VMAT delivery were recorded in every 0.25 seconds, and the errors between planned and actual values were evaluated. The dose re-calculation using these recorded data has been performed and compared with the original TPS plan using the gamma index. RESULTS. Typical peak errors of gantry angles, leaf positions, and jaw positions were 3 degrees, 0.6 mm, and 1 mm, respectively. The dose distribution obtained by the TPS plan and the recalculated one agreed well under 2%-2 mm gamma index criteria. CONCLUSIONS. Quality assurance for prostate VMAT delivery has been performed with a satisfied result. (Keyword) Humans / Male / Phantoms, Imaging / Prostatic Neoplasms / Quality Assurance, Health Care / Quality Control / Radiotherapy Dosage / Radiotherapy, Intensity-Modulated (Link to Publication Site) ● Publication site (DOI): 10.3109/02841860903081905 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 19863228 ● Summary page in Scopus @ Elsevier: 2-s2.0-70350776792 (DOI: 10.3109/02841860903081905, PubMed: 19863228, Elsevier: Scopus)
103.	Hiroshi Igaki, Keiichi Nakagawa, Hideomi Yamashita, Atsuro Terahara, Akihiro Haga, Kenshiro Shiraishi, Nakashi Sasano, Kentaro Yamamoto, Tsuyoshi Onoe, Kiyoshi Yoda and Kuni Ohtomo : Contrast media-assisted visualization of brain metastases by kilovoltage cone-beam CT, Acta Oncologica, Vol.48, No.2, 314-317, 2009. (Keyword) Adult / Brain Neoplasms / Carcinoma, Papillary / Cone-Beam Computed Tomography / Contrast Media / Female / Humans / Radiotherapy Planning, Computer-Assisted / Thyroid Neoplasms / Tumor Burden (Link to Publication Site) ● Publication site (DOI): 10.1080/02841860802310983 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 18759140 ● Summary page in Scopus @ Elsevier: 2-s2.0-60549095653 (DOI: 10.1080/02841860802310983, PubMed: 18759140, Elsevier: Scopus)
104.	Keiichi Nakagawa, Akihiro Haga, Kenshiro Shiraishi, Hideomi Yamashita, Hiroshi Igaki, Atsuro Terahara, Kuni Ohtomo, Shigeki Saegusa, Takashi Shiraki, Takashi Oritate and Kiyoshi Yoda : First clinical cone-beam CT imaging during volumetric modulated arc therapy, Radiotherapy and Oncology, Vol.90, No.3, 422-423, 2008. (Keyword) Cone-Beam Computed Tomography / Humans / Male / Prostatic Neoplasms / Radiotherapy, Intensity-Modulated (Link to Publication Site) ● Publication site (DOI): 10.1016/j.radonc.2008.11.009 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 19062117 ● Summary page in Scopus @ Elsevier: 2-s2.0-61449100537 (DOI: 10.1016/j.radonc.2008.11.009, PubMed: 19062117, Elsevier: Scopus)
105.	Setsuo Tamenaga, Hiroshi Toki, Akihiro Haga and Yoko Ogawa : Spontaneous chiral symmetry breaking in the massless linear sigma model by Fermion and Boson loop corrections, Nuclear Physics A, Vol.811, No.3-4, 306-328, 2008. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.nuclphysa.2008.07.015 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-51649100977 (DOI: 10.1016/j.nuclphysa.2008.07.015, Elsevier: Scopus)
106.	Akihiro Haga, Yataro Horikawa and Hiroshi Toki : Reanalysis of muonic ⁹⁰ Zr and ²⁰⁸ Pb atoms, Physical Review C, Nuclear Physics, Vol.75, No.4, 044315-1-044315-8, 2007. (Link to Publication Site) ● Publication site (DOI): 10.1103/PhysRevC.75.044315 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-34247581841 (DOI: 10.1103/PhysRevC.75.044315, Elsevier: Scopus)
107.	Yusuke Sakai, Akihiro Haga, S. Sugita, Shigetomo Kita, S.-I. Tanaka and Fumio Okuyama : Electron gun using carbon-nanofiber field emitter, The Review of Scientific Instruments, Vol.78, No.2007, 013305-1-013305-7, 2007. (Link to Publication Site) ● Publication site (DOI): 10.1063/1.2430650 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-34047097736 (DOI: 10.1063/1.2430650, Elsevier: Scopus)
108.	Akihiro Haga, Hiroshi Toki, Setuo Tamenaga, Yataro Horikawa and H. L. Yadav : Self-consistent relativistic random-phase approximation with vacuum polarization, Physical Review C, Nuclear Physics, Vol.72, No.3, 034301-1-034301-4, 2005. (Link to Publication Site) ● Publication site (DOI): 10.1103/PhysRevC.72.034301 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-28844501880 (DOI: 10.1103/PhysRevC.72.034301, Elsevier: Scopus)
109.	Akihiro Haga, Hiroshi Toki, Setuo Tamenaga and Yataro Horikawa : Relativistic Hartree approach with exact treatment of vacuum polarization for finite nuclei, Physical Review C, Nuclear Physics, Vol.70, No.6, 064322-1-064322-9, 2004. (Link to Publication Site) ● Publication site (DOI): 10.1103/PhysRevC.70.064322 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-13844297897 (DOI: 10.1103/PhysRevC.70.064322, Elsevier: Scopus)
110.	Akihiro Haga, Yataro Horikawa, Yasutoshi Tanaka and Hiroshi Toki : Relativistic random-phase approximation calculation with negative energy states of nuclear polarization in muonic atoms, Physical Review C, Nuclear Physics, Vol.69, No.4, 044308-1-044308-11, 2004. (Link to Publication Site) ● Publication site (DOI): 10.1103/PhysRevC.69.044308 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-3042680179 (DOI: 10.1103/PhysRevC.69.044308, Elsevier: Scopus)
111.	Satoru Senda, M. Tanemura, Yusuke Sakai, Yohei Ichikawa, Shigetomo Kita, Akihiro Haga and Fumio Okuyama : New field-emission x-ray radiography system, The Review of Scientific Instruments, Vol.75, 1366-1368, 2004. (Link to Publication Site) ● Publication site (DOI): 10.1063/1.1711140 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-3042686755 (DOI: 10.1063/1.1711140, Elsevier: Scopus)
112.	Taisuke Nagasawa, Akihiro Haga and Masahiro Nakano : Hyperfine splitting of hydrogenlike atoms based on relativistic mean field theory, Physical Review C, Nuclear Physics, Vol.69, No.3, 034322-1-034322-5, 2004. (Link to Publication Site) ● Publication site (DOI): 10.1103/PhysRevC.69.034322 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-2442476476 (DOI: 10.1103/PhysRevC.69.034322, Elsevier: Scopus)
113.	Akihiro Haga, Satoru Senda, Yusuke Sakai, Yohei Mizuta, Shigetomo Kita and Fumio Okuyama : A miniature x-ray tube, Applied Physics Letters, Vol.84, No.12, 2208-2210, 2004. (Link to Publication Site) ● Publication site (DOI): 10.1063/1.1689757 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-1942444514 (DOI: 10.1063/1.1689757, Elsevier: Scopus)
114.	Yataro Horikawa and Akihiro Haga : Gauge invariant evaluation of nuclear polarization with the collective model, Physical Review C, Nuclear Physics, Vol.67, No.4, 048501-1-048501-4, 2003. (Link to Publication Site) ● Publication site (DOI): 10.1103/PhysRevC.67.048501 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1103/PhysRevC.67.048501 (DOI: 10.1103/PhysRevC.67.048501)
115.	Akihiro Haga, Yataro Horikawa and Yasutoshi Tanaka : Nuclear polarization in muonic ²⁰⁸ Pb, Physical Review A, Atomic, Molecular, and Optical Physics, Vol.66, No.3, 034501-1-034501-4, 2002. (Link to Publication Site) ● Publication site (DOI): 10.1103/PhysRevA.66.034501 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1103/PhysRevA.66.034501 (DOI: 10.1103/PhysRevA.66.034501)
116.	Akihiro Haga, Yataro Horikawa and Yasutoshi Tanaka : Nuclear polarization in hydrogenlike ²⁰⁸₈₂Pb⁸¹⁺, Physical Review A, Atomic, Molecular, and Optical Physics, Vol.65, No.5, 052509-1-052509-11, 2002. (Link to Publication Site) ● Publication site (DOI): 10.1103/PhysRevA.65.052509 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-0036577680 (DOI: 10.1103/PhysRevA.65.052509, Elsevier: Scopus)
117.	Nobuhiro Yamanaka, Akihiro Haga, Yataro Horikawa and Atsushi Ichimura : Nuclear polarization in hydrogenlike heavy ions, Physical Review A, Atomic, Molecular, and Optical Physics, Vol.63, No.6, 062502-1-062502-9, 2001. (Link to Publication Site) ● Publication site (DOI): 10.1103/PhysRevA.63.062502 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1103/PhysRevA.63.062502 (DOI: 10.1103/PhysRevA.63.062502)

Academic Paper (Unrefereed Paper):

1.	Yuki Matsumoto, Yuki Kanazawa, Yuki Kinjo, Masafumi Harada, Toshiaki Miyati, Hiroaki Hayashi, Mitsuharu Miyoshi, Naoki Maeda, Yasuhisa Kanematsu, Yasushi Takagi and Akihiro Haga : Evaluation of Blood Flow and Plaque Vulnerability in Carotid Artery Stenosis Focusing on Morphological and Component Characteristics, Proceedings of the 2022 ISMRM & SMRT ANNUAL MEETING & EXHIBITION, No.3783, 2022, 2022.

Review, Commentary:

1.	Akihiro Haga : Introduction to Medical Physics Course in Tokushima University, Japanese Journal of Medical Physics, Vol.42, No.4, 1-3, Dec. 2022. (Link to Publication Site) ● Publication site (DOI): 10.11323/jjmp.42.4_212 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390294573737223040 ● Search Scopus @ Elsevier (DOI): 10.11323/jjmp.42.4_212 (DOI: 10.11323/jjmp.42.4_212, CiNii: 1390294573737223040)
2.	Akihiro Haga : The front lines of dual-energy computed tomography, Japanese Journal of Medical Physics, Vol.41, No.Sup4, 1-6, Dec. 2021. (Tokushima University Institutional Repository) ● Metadata: 116614 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1570854177637571712 (Tokushima University Institutional Repository: 116614, CiNii: 1570854177637571712)
3.	Akihiro Haga and Kenya Kusunose : 4. Ultra-sound Image Analysis, Japanese Journal of Radiological Technology, Vol.77, No.12, 1479-1484, Dec. 2021. (Keyword) General Medicine (Tokushima University Institutional Repository) ● Metadata: 116615 (Link to Publication Site) ● Publication site (DOI): 10.6009/jjrt.2021_JSRT_77.12.1479 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390290473084321792 ● Search Scopus @ Elsevier (DOI): 10.6009/jjrt.2021_JSRT_77.12.1479 (Tokushima University Institutional Repository: 116615, DOI: 10.6009/jjrt.2021_JSRT_77.12.1479, CiNii: 1390290473084321792)
4.	Akihiro Haga : Physics, machine learning, and medicine, Shikoku Acta Medica, Vol.75, No.5,6, 155-164, Dec. 2019. (Summary) In this manuscript, the relationship between physics and machine learning(ML)and the application to the medical field were informally described. The recent development of artificial intelligence, which is based on ML, urged us to apply the disease finding, disease classification, a decision-making system, and so on. I believe that the ML based medicine is a natural way to proceed. However, the important thing is not only to apply the ML technology in the various medical problems, but also to understand the causality in those problems : That's the approach in the physics, and the quantitative consideration in the research in physics yields almost same ways used in the ML. In addition, the state-of-the-art ML such as a deep learning, becomes one of the powerful tools in a discovery in physics. Although, the medical field has a benefit with ML, we'd need to go to the next stage to find solutions in more fundamental problems with the medically developed ML methodology. (Keyword) physics / machine learning / Medical Physics / medicine (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1050020697875859456 (CiNii: 1050020697875859456)
5.	仲本宗泰, Akihiro Haga and 高橋渉 : Radiomics入門, 医学物理:解説, Vol.38, No.2, Nov. 2018.
6.	木田智士, 鍛治静雄, 今江禄一, 名和要武, 中川恵一 and Akihiro Haga : 敵対的生成ネットワーク(GAN)の放射線治療への応用, Rad Fan, Nov. 2018.
7.	Akihiro Haga : 四次元コーンビームCTによる臓器運動の可視化とその応用, 日本放射線技術学会誌, Vol.74, No.11, Nov. 2018.

Proceeding of International Conference:

1.	Mayuka Seguchi, Yuki Kanazawa, Tosiaki Miyati, Masafumi Harada, Mitsuharu Miyoshi, Hiroaki Hayashi and Akihiro Haga : How Can We Measure Biological Viscosity Non-Invasively Using MRI?, RSNA2024 (the 110th Scientific Assembly and Annual Meeting of the Radiological Society of North America), Chicago, Dec. 2024.
2.	Ohshiro Ryuji, Yuki Kanazawa, Akihiro Haga and Masafumi Harada : How Do We Determine the Water Volume in the Central Nervous System?, RSNA2024 (the 110th Scientific Assembly and AnnualMeeting of the Radiological Society of North America), Chicago, Dec. 2024.
3.	Iwasaki Ren, Kusunose Kenya, Tanaka Hidekazu, Miyake Makoto, Moriuchi Kenji, Takeda Yasuharu, Hirotsugu Yamada and Akihiro Haga : Left Ventricular Ejection Fraction Prediction: Preprocessing Network & Data Augmentation for Echocardiographic Standardization, The 10th JKMP meeting at Nagoya, Sep. 2024.
4.	SATO Yoshihide, Sakata Dousatsu, Bolst David, Simpson Edward, Guatelli Susanna and Akihiro Haga : Validation of fragment cross section with Light Ion QMD model, 62nd Annual PTCOG conference, Jun. 2024.
5.	Ohshiro Ryuji, Yuki Kanazawa, Akihiro Haga and Masafumi Harada : Development of numerical phantom converting from electron microscopic analysis to multi-component water fraction for MRI simulator., The 33st Annual Meeting of ISMRM, Singapore, May 2024.
6.	Kanon Monda, Hitoshi Ikushima, Yuki Matsumoto, Motoharu Sasaki, Micihito Shimokawa and Akihiro Haga : Proposal of a differential diagnostic index for recurrent brain metastasis or radiation-induced brain necrosis by radiomics analysis using C-11 methionine PET, The 3rd ICRPT, Apr. 2024.
7.	Yoshihide Sato, Dousatsu Sakata, David Bolst, Edward Simpson, Susanna Guatelli and Akihiro Haga : Validation of positron-emitting radionuclide production with Light Ion QMD model, The 5th Geant4 International User Conference at the Physics-Medicine-Biology frontier, Osaka, Mar. 2024.
8.	Shunsuke Uotani, Yuki Kanazawa, Taniguchi Yo, Ito Kosuke, Bito Yoshitaka, Yuki Matsumoto, Masafumi Harada and Akihiro Haga : Determining Imaging Parameters of a Gradient-echo Technique for Myelin Water Fraction., European Congress of RadiologyECR2024, Wien, Mar. 2024.
9.	Kanon Monda, Hitoshi Ikushima, Yuki Matsumoto, Motoharu Sasaki, Michihito Shimokawa and Akihiro Haga : The differential diagnosis of recurrent brain metastasis or radiationinduced brain necrosis by radiomics analysis using C-11 methionine positron emission tomography, ECR 2024, Feb. 2024.
10.	Yoshihide Sato, Dousatsu Sakata, David Bolst, Edward Simpson, Susanna Guatelli and Akihiro Haga : Development and implementation of a new Geant4 QMD model and its validation, Geant4 Collaboration Meeting 2023, Sapporo, Sep. 2023.
11.	Yoshihide Sato, Dousatsu Sakata, David Bolst, Edward Simpson, Susanna Guatelli and Akihiro Haga : Development of a more accurate Geant4 quantum molecular dynamics model for hadron therapy, Annual meeting of American Association for Physicist in Medicine, Jul. 2023.
12.	Yuki Kanazawa, KITANO Shun, Masafumi Harada, Yo Taniguchi, Yuki Matsumoto, Hiroaki Hayashi, Kosuke Ito, Yoshitaka Bito and Akihiro Haga : Myelin Water Atlas Template Derived from Quantitative Parameter Mapping, The 32st Annual Meeting of ISMRM, Toronto, Jun. 2023.
13.	Shimomura Taisei, Fujiwara Daiyu, Inoue Yuki, Takeya Atsushi, Ohta Takeshi, Nozawa Yuki, Nawa Kanabu, Nakagawa Keiichi and Akihiro Haga : Development of virtual CBCT simulator and deep-learning based elemental material decomposition, ESTRO 2023, Wien, May 2023.
14.	Atsushi Takeya, Keiichiro Watanabe, Taisei Shimomura and Akihiro Haga : Development of an accurate and rapid auto-segmentation method for alveolar bone and teeth using virtual cone-beam computed tomography and artificial intelligence technology, International Conference on Radiological Physics and Technology (ICRPT), Vol.43, No.Sup1, 153-154, Yokohama, Apr. 2023.
15.	Sato Yoshihide, Akihiro Haga, Sakata Dousatsu, Guatelli Susanna, Bolst David and Simpson Edward : Validation of improved quantum molecular dynamics modeland impact of parameters regarding time evolution, International Conference of Mini- Micro- Nano- Dosimetry, Noosa, QLD, Australia, Feb. 2023.
16.	Nagomi Fukuda, Yuki Kanazawa, Hiroaki Hayashi, Yuki Matsumoto, Masafumi Harada, Motoharu Sasaki and Akihiro Haga : Development of a B1 correction method without additional scanning VFA T1 map., European Congress of Radiology, Wien, Jul. 2022.
17.	Yuki Matsumoto, Yuki Kanazawa, Yuki Kinjo, Masafumi Harada, Toshiaki Miyati, Hiroaki Hayashi, Mitsuharu Miyoshi, Naoki Maeda, Yasuhisa Kanematsu, Yasushi Takagi and Akihiro Haga : Evaluation of Blood Flow and Plaque Vulnerability in Carotid Artery Stenosis Focusing on Morphological and Component Characteristics, ISMRM 30th Annual Meeting, London, May 2022.
18.	Nagomi Fukuda, Yuki Kanazawa, Hiroaki Hayashi, Yuki Matsumoto, Masafumi Harada, Motoharu Sasaki and Akihiro Haga : Development of self-calibrating B1 correction for three-dimensional variable flip angle T1 mapping, Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, London, May 2022.
19.	Shun Kitano, Yuki Kanazawa, Masafumi Harada, Yo Taniguchi, Yuki Matsumoto, Hiroaki Hayashi, Kosuke Ito, Yoshitaka Bito and Akihiro Haga : Conversion map from quantitative parameter mapping to myelin water fraction, Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, London, May 2022.
20.	Natsuki Ikemitsu, Yuki Kanazawa, Akihiro Haga, Hiroaki Hayashi, Yuki Matsumoto and Masafumi Harada : Determination of Alzheimer's disease based on morphology and atrophy using machine learning combined with automated segmentation., Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting, London, May 2022.
21.	Sato Yoshihide, Akihiro Haga, Sakata Dousatsu, Guatelli Susanna and Bolst David : Establishment of Evaluation Method for Fragmentation Model in Heavy-ion Therapy Energy, International Conference on Radiological Physics and Technology (ICRPT), Vol.42, No.Sup1, 126, Yokohama, Apr. 2022.
22.	Shimomura Taishi, Inoue Yuki, Fujiwara Daiyu and Akihiro Haga : A Generative Cone-Beam Computed Tomography Model, International Conference on Radiological Physics and Technology (ICRPT), Vol.42, No.Sup1, 126, Yokohama, Apr. 2022.
23.	Inoue Yuki, Taisei Shimomura and Akihiro Haga : Novel Scattered X-ray model for Cone-Beam Computed Tomography, International Conference on Radiological Physics and Technology (ICRPT), Vol.42, No.Sup1, 126, Yokohama, Apr. 2022.
24.	Fukuda Nagomi, Yuki Kanazawa, Hiroaki Hayashi, Yuki Matsumoto, Masafumi Harada, Motoharu Sasaki and Akihiro Haga : Variable flip angle T1 mapping without acquiring data for B1 correction, Joint International Conference on Radiological Physics and Technology, Yokohama, Apr. 2022.
25.	Natsuki Ikemitsu, Yuki Kanazawa, Masafumi Harada, Yuki Matsumoto, Hiroaki Hayashi, Kosuke Ito, Yo Taniguchi, Yoshitaka Bito and Akihiro Haga : Determination of white matter structure index for voxel basedmorphometry and connectivity analysis., European Congress of Radiology, Wien, Mar. 2022.
26.	Sato Yoshihide, Akihiro Haga, Sakata Dousatsu, Guatelli Susanna and Bolst David : Assessment of Fragmentation Model in Heavy-ion Therapy, International Conference of Mini- Micro- Nano- Dosimetry, Wollongong, Australia, Feb. 2022.
27.	Naoki Maeda, Yuki Kanazawa, Yuki Kinjo, Yuki Matsumoto, Masafumi Harada, Tosiaki Miyati, Hiroaki Hayashi, Mitsuharu Miyoshi, Yasuhisa Kanematsu, Yasushi Takagi and Akihiro Haga : Is it possible to evaluate morphological carotid artery stenosis information using NASCET criteria?, RSNA2021 (Radiological Society of North America), Chicago, Nov. 2021.
28.	Naoki Maeda, Yuki Kanazawa, Masafumi Harada, Yo Taniguchi, Yuki Matsumoto, Hiroaki Hayashi, Kosuke Ito, Yoshitaka Bito and Akihiro Haga : Derivation of Water Exchange Constants between Components using Quantitative Parameter Mapping (QPM), ISMRM 29th Annual Meeting, Online, May 2021.
29.	Yuki Kanazawa, Masafumi Harada, Yo Taniguchi, Syun Kitano, Nagomi Fukuda, Yuki Matsumoto, Hiroaki Hayashi, Kosuke Ito, Yoshitaka Bito and Akihiro Haga : Myelin imaging derived quantitative parameter mapping compared to myelin water fraction, ISMRM 29th Annual Meeting, Online, May 2021.
30.	Hitoshi Ikushima, Akihiro Haga, Ando Ken, Kato Shingo, Kaneyasu Yuko, Uno Takashi, Okonogi Noriyuki, Yoshida Kenji, Ariga Takuro, Isohashi Fumiaki, Harima Yoko, Kanemoto Ayae, Ii Noriko, Wakatsuki Masaru and Ohno Tatsuya : Prediction of survival in cervical cancer patients treated with chemoradiotherapy by imaging analysis using artificial intelligence -A multi-institutional survey study of Japanese Radiation Oncology Study Group (JROSG)-, The 62nd annual meeting of the Japan Society of gynecologic oncology, International Session, Jan. 2021.
31.	Hasegawa Yu, Akihiro Haga, Sakata Dousatsu, Yuki Kanazawa, Masahide Tominaga, Motoharu Sasaki, Imae T. and Nakagawa Keiichi : Estimation of X-ray energy spectrum for CT scannerfrom percentage depth dose measurement, 2020 JOINT AAPM COMP meeting, Jul. 2020.
32.	Shimomura Taisei and Akihiro Haga : Cone-beam CT image reconstruction with spherical harmonics, 2020 JOINT AAPM COMP meeting, Jul. 2020.
33.	Takahashi Satoshi, Tanaka Shota, Takahashi Masamichi, Yamazawa Erika, Hana Taijun, Kitagawa Yosuke, Takayanagi Shunsaku, Takahashi Wataru, Nakamoto Takahiro, Akihiro Haga, Hamamoto Ryuhi and Saito Nobuhito : Visualization of judgment basis of CNN to grading glioma, 2019 SNO Annual Meeting, Nov. 2019.
34.	Nakano Masahiro, Image Toshikazu, Nakamoto Toshi, Akihiro Haga, Nawa Kanabu, Nomura Yuki, Chhatkuli Ritu, Demachi Kenji, Takahashi Wataru and Yamamoto Koichi : Pseudo-CBCT Image Prediction of Head and Neck Cancer Patient Using Principal Component Vector Fields of Early Treatment Fractions, AAPM 61th annual meeting, Jul. 2019.
35.	Yuki Kanazawa, Chiba Daiki, Masafumi Harada, Miyati Tosiaki, Miyoshi Mitsuharu, Hiroaki Hayashi, Yuki Matsumoto, Takashi Abe and Akihiro Haga : Thermal sensitive pH imaging using CEST, ISMRM 27th Annual Meeting, Montreal, May 2019.
36.	Yuki Kanazawa, Masafumi Harada, Taniguchi Yo, Hiroaki Hayashi, Takashi Abe, Maki Ohtomo, Yuki Matsumoto, Ono Masafumi, Bito Yoshitaka and Akihiro Haga : Myelin imaging derived from quantitative parameter mapping, ISMRM 27th Annual Meeting, Montreal, May 2019.
37.	Yuki Kanazawa, Chiba Daiki, Masafumi Harada, Miyati Tosiaki, Miyoshi Mitsuharu, Hayashi Hiroaki and Akihiro Haga : Quantitative evaluation of local offset frequency for CEST imaging, The 3rd Annual Scientific Meeting of the Japanese Chapter of ISMRM, Nagoya, Dec. 2018.
38.	Chisato Tonoiso, Hitoshi Ikushima, Akihiro Haga, Shunsuke Furutani, Takashi Kawanaka, Akiko Kubo, Masato Nishimura, Akiko Abe, Minoru Irahara and Masafumi Harada : Investigation of prognostic factors of cervical cancer using pre-treatment MRI images, The 2018 International Gynecologic Cancer Society Meeting, Sep. 2018.
39.	Akihiro Haga : Standardization of imaging features for radiomics analysis, The second workshop of Technological Competency as Caring in the Health Sciences, Aug. 2018.
40.	Watanabe Y, Magome T, Akihiro Haga, Nawa K, Nakano M, Nomura Y, Hanaoka S and Nakagawa K : Field-Of-View Expansion of Megavoltage CT Based On Iterative Reconstruction Algorithm Using Information of Treatment Planning kV-CT, Medical Physics (Annual Meeting of American Association of Physicist in Medicine), Nashville, America, Jul. 2018.
41.	Akihiro Haga, Takahashi W, Aoki S, Nawa K, Yamashita H, Kotoku J, Abe O and Nakagawa K : Histology Classification in Early-Stage Non-Small Cell Lung Cancers Using Radiomic Features, Medical Physics (Annual Meeting of American Association of Physicist in Medicine), Nashville, America, Jul. 2018.
42.	Kotoku J, Oyama A, Hiraoka Y, Obayashi I, Shiraishi K, Akihiro Haga, Kondo H, Saikawa Y, Kobayashi T and Furui S : Classification of Hepatic Tumor Images Using Persistent Homology, Medical Physics (Annual Meeting of American Association of Physicist in Medicine), Nashville, America, Jul. 2018.
43.	Nakamoto T, Takahashi W, Akihiro Haga, Takahashi S, Nawa K, Ohta T, Ozaki S, Tanaka S, Mukasa A and Nakagawa K : Radiomics-Based Prediction of Malignant Glioma Grades Using T2-Weighted Magnetic Resonance Images, Medical Physics (Annual Meeting of American Association of Physicist in Medicine), Nashville, America, Jul. 2018.
44.	Hashimoto M, Nakano M, Akihiro Haga and Nawa K : Patient Setup System Using a Virtual X-Ray Image, Medical Physics (Annual Meeting of American Association of Physicist in Medicine), Nashville, America, Jul. 2018.
45.	Akihiro Haga : Imaging Database and Radiomics, International workshop on biological effects of radiation, Mar. 2018. (Keyword) Radiomics / Standardization / Database
46.	Akihiro Haga : Introduction to Medical Physics in Japan, Workshop in Medical Physics, Beijing, Dec. 2017.
47.	Akihiro Haga, Wataru Takahashi, Shuri Aoki, Kanabu Nawa, Hideomi Yamashita, Osamu Abe and Keiichi Nakagawa : Radiomics predictability of early-stage non-small cell lung cancer histology: interobserver delineation analysis, 8th Japan-Korea Joint Meeting on Medical Physic, Osaka, Sep. 2017.
48.	W. Takahashi, S. Aoki, H. Yamashita, M. Ogita, K. Nakagawa and Akihiro Haga : CT-based Radiomics Histology Analysis of Stereotactic Ablative Radiotherapy Patients with Early-Stage Non-Small-Cell Lung Cancer, International Journal of Radiation OncologyBiologyPhysics, Vol.99, No.Supplement, E726, San Diego, Sep. 2017. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ijrobp.2017.06.2349 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.ijrobp.2017.06.2349 (DOI: 10.1016/j.ijrobp.2017.06.2349)
49.	Image ToshiKazu, Akihiro Haga, Watanabe Yuishi, Takenaka Shigeharu and Nawa Kanabu : Dose Reconstruction for Prostate SBRT by Use of Cone-Beam CT and a Log File During FFF-VMAT Delivery, Medical Physics, Denver, Aug. 2017.
50.	Jun'ichi Kotoku, Takuya Hirose, Shinobu Kumagai, Akari Matsushima, Kenshiro Shiraishi, Norikazu Arai, Akihiro Haga and Takenori Kobayashi : Automatic Chest X-Ray Screening with Convolutional Neural Networks, Medical Physics, Denver, Aug. 2017.
51.	Masahiro Nakano, Roel Kierkels, Shouhei Hanaoka, Erik Korevaar, Toshikazu Imae, Akihiro Haga and Nanna Sijtsema : Optimizing B-Spline Deformable Image Registration Between Planning CT and Cone-Beam CT in Head and Neck Cancer Patients, Medical Physics, Denver, Aug. 2017.
52.	Akihiro Haga, Dousatsu Sakata, Jun'ichi Kotoku, Taiki Magome, Toshikazu Imae, Kanabu Nawa and Keiichi Nakagawa : Material Decomposition with Prior Information Compressive Sensing, Medical Physics, Denver, Aug. 2017.
53.	Masahiro Nakano, Akihiro Haga, Jun'ichi Kotoku, Taiki Magome, Yoshitaka Masutani, Shouhei Hanaoka and Keiichi Nakagawa : Four-Dimensional Cone-Beam CT Iterative Reconstruction with Time-Ordered Chain Graph Model for Non-Periodic Organ Motion and Deformation, Medical Physics, Vol.43, 3702, Washington, D.C., Aug. 2016. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4957236 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1118/1.4957236 (DOI: 10.1118/1.4957236)
54.	Jun'ichi Kotoku, Shinobu Kumagai, Susumu Nakabayashi, Akihiro Haga and Takenori Kobayashi : Anomaly Detection of Respiratory Motion by Use of Singular Spectrum Analysis, Medical Physics, Vol.43, 3681, Washington, D.C., Aug. 2016. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4957113 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1118/1.4957113 (DOI: 10.1118/1.4957113)
55.	Taiki Magome, Akihiro Haga, Yutaka Takahashi, Keiichi Nakagawa, Kathryn Dusenbery and Susanta Hui : Fast Megavoltage CT Imaging with Rapid Scan Time and Low Imaging Dose in Helical Tomotherapy, Medical Physics, Vol.43, 3814, Washington, D.C., Aug. 2016.
56.	Akihiro Haga, T Magome, M Nakano, J Kotoku and K Nakagawa : Field-Of-View Expansion in Cone-Beam CT Reconstruction by Use of Prior Information, Medical Physics, Vol.43, 3411, Washington, D.C., Aug. 2016. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4955931 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1118/1.4955931 (DOI: 10.1118/1.4955931)
57.	Daisuke Kon, Masahiro Nakano, Kanabu Nawa, Akihiro Haga and Keiichi Nakagawa : Measurement and Calculation of Doses Outside the Treatment Volume from Gamma Knife Radiosurgery, YITP International Workshop: Biological & Medical Science based on Physics 2015 in Kyoto, Kyoto, Nov. 2015.
58.	Magome Taiki, Akihiro Haga, Hiroshi Igaki and Noriyasu Sekiya : Survival time prediction after radiotherapy for high-grade glioma patients based on machine learning technique, YITP International Workshop: Biological & Medical Science based on Physics 2015 in Kyoto, Kyoto, Nov. 2015.
59.	Ritu Bhusal Chhatkuli, Kazuyuki Demachi, Mitsuru Uesaka and Akihiro Haga : Markerless tumor tracking for moving tumors using pre4DCBCT for image guided radiation therapy, YITP International Workshop: Biological & Medical Science based on Physics 2015 in Kyoto, Kyoto, Nov. 2015.
60.	Dousatsu Sakata, Akihiro Haga, Satoshi Kida, Toshikazu Image and Keiichi Nakagawa : Estimation of Effective Chage and Electron Density Distribution by Dual Energy CT Reconstruction, IEEE Nuclear Science Symposium and Medical Imaging Conference 2015 in San diego, San Diego, Nov. 2015.
61.	Daisuke Kon, Masahiro Nakano, Kanabu Nawa, Akihiro Haga and Keiichi Nakagawa : Investigation of Factors Contributing to Extracranial Radiation Doses From Leksell Gamma Knife, Medical Physics, Vol.41, 3454, Anaheim, Jul. 2015.
62.	J Kotoku, S Kumagai, Akihiro Haga, S Nakabayashi, N Arai and T Kobayashi : Prediction of Respiratory motion using State Space Models, Medical Physics, Vol.41, 3638, Anaheim, Jul. 2015. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4925793 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1118/1.4925793 (DOI: 10.1118/1.4925793)
63.	Akihiro Haga, J Kotoku, Y Horikawa and K Nakagawa : Photoelectric Cross Section Revisited, Medical Physics, Vol.41, 3251, Anaheim, Jul. 2015.
64.	Akihiro Haga and Dousatsu Sakata : Dual energy CT reconstruction using LINAC, JSPS Core-to-Core Program Pre-AAPM Scientific Symposium, Anaheim, Jul. 2015.
65.	Ritu Bhusal Chhatkuli, Kazuyuki Demachi, Mitsuru Uesaka and Akihiro Haga : 4DCBCT based phase recognition for tracking moving tumors in image guided radiation therapy, International Conference on Radiation Research 2015 in Kyoto, Kyoto, May 2015.
66.	Daisuke Kon, Akihiro Haga and Keiichi Nakagawa : Investigation of factors contributing to extracranial doses for Leksell Gamma Knife®, International Conference on Radiation Research 2015 in Kyoto, Kyoto, May 2015.
67.	Dousatsu Sakata, Akihiro Haga, Satoshi Kida, Toshikazu Imae, Shigeharu Takenakam, Yoshitaka Masutani and Keiichi Nakagawa : Estimation of Effective Atomic Number and Electron Density by Dual-Energy CT Reconstruction, International Conference on Radiation Research 2015 in Kyoto, Kyoto, May 2015.
68.	M Nakano, S Kida, Y Masutani, T Imae, K Yamamoto, K Shiraishi, K Nakagawa and Akihiro Haga : Time-Ordered Four Dimensional Cone-Beam CT Reconstruction for Pelvic Region, International Conference on Radiation Research 2015 in Kyoto, Kyoto, May 2015.
69.	Akihiro Haga : Four-dimensional dose reconstruction by use of in-treatment data set, Groningen, OSAKA, INDIANA, GRONINGEN symposium on Medical Physics, Groningen, Jul. 2014.
70.	Akihiro Haga : Reconstruction of the Treatment Area by Use of Sinogram in Helical Tomotherapy and conventional LINAC, University of Minnesota, The University of Tokyo, Osaka University, Scientific Symposium, Minnesota., Minnesota, Jul. 2014.
71.	T Magome, Akihiro Haga, H Igaki, N Sekiya, Y Masutani, A Sakumi, A Mukasa and K Nakagawa : Comparison of Survival-Time Prediction Models After Radiotherapy for High-Grade Glioma Patients Based On Clinical and DVH Features, Medical Physics, Vol.41, 570, Austin, Jul. 2014.
72.	M Hashimoto, T Nishio, Akihiro Haga, T Hanada, S Kabuki, T Kozuka and M Oguchi : Four-Dimensional Dose Distribution Measurement Using Plastic Scintillator, Medical Physics, Vol.41, 382, Austin, Jul. 2014. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4889004 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1118/1.4889004 (DOI: 10.1118/1.4889004)
73.	Akihiro Haga, K Nakagawa, C Maurer, K Ruchala, E Chao, D Casey, S Kida, D Sakata, T Magome, M Nakano and Y Masutani : Reconstruction of the Treatment Area by Use of Sinogram in Helical Tomotherapy, Medical Physics, Vol.41, 203-204, Austin, Jul. 2014. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4888256 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1118/1.4888256 (DOI: 10.1118/1.4888256)
74.	M Nakano, S Kida, Y Masutani, T Shiraki, K Yamamoto, K Shiraishi, K Nakagawa and Akihiro Haga : Improvement of Time-Ordered Four Dimensional Cone-Beam CT; Image Mosaicing with Real and Virtual Projections, Medical Physics, Vol.41, 195, Austin, Jul. 2014. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4888220 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1118/1.4888220 (DOI: 10.1118/1.4888220)
75.	J Kotoku, S Nakabayashi, Akihiro Haga, S Kumagai, T Ishibashi, N Arai, N Saotome and T Kobayashi : 3D Surface Reconstruction of a Patient Using Epipolar Geometry, Medical Physics, Vol.41, 185, Austin, Jul. 2014. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4888180 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1118/1.4888180 (DOI: 10.1118/1.4888180)
76.	T Imae, Akihiro Haga, N Saotome, S Kida, M Nakano, Y Takeuchi, T Shiraki, K Yano, H Yamashita, K Nakagawa and K Ohtomo : Dose Distribution Using Respiratory Signals and Machine Parameters During Treatment, Medical Physics, Vol.41, 248, Austin, Jul. 2014.
77.	D Sakata, S Kida, M Nakano, Y Masutani, K Nakagawa and Akihiro Haga : Estimation of Effective Charge Distribution by Dual-Energy CT Reconstruction, Medical Physics, Vol.41, 153, Austin, Jul. 2014. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4888049 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1118/1.4888049 (DOI: 10.1118/1.4888049)
78.	N Saotome, Akihiro Haga, T Imae, S Kida, M Nakano, H Yamashita, K Nakagawa and K Ohtomo : Delivered 4D Dose Calculation for Lung-VMAT Patients Using In-Treatment CBCT and LINAC Log Data, Medical Physics, Vol.41, 98, Austin, Jul. 2014. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4889724 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1118/1.4889724 (DOI: 10.1118/1.4889724)
79.	Takayuki Okura, Kazuya Shibata, Seiichi Koshizuka, Akihiro Nomoto, Akihiro Haga and Keiichi Nakagawa : LUNG DEFORMATION SIMULATION BASED ON MEDICAL IMAGES AND MOTION MODELS OF DIAPHRAGM AND RIBS USING THE MPS METHOD, 11th World Congress on Computational Mechanics (WCCM), Barcelona, Jul. 2014.
80.	A. Sakumi, K. Mizuno, Y. Nishijima, M. Uesaka, Akihiro Haga, Y. Iwai, K. Yoda and K. Nakagawa : Evaluation of an intrafraction 4D cone-beam CT (CBCT) imaging system using a 4D lung tumor phantom driven by measured tumor motions for a lung cancer patient, International Conference on Medical Physics (ICMP 2013), Brighton, Sep. 2013.
81.	Ritu Bhusal Chhatkuli, Kazuyuki Demachi, Akihiro Haga, Hiroshi Sakakibara, Kazuma Kamiaka, Yusuke Nakamura and Mitsuru Uesaka : A study for the implementation of dynamic tracking radiotherapy system based on time series image prediction method, International Conference on Medical Physics (ICMP 2013), Brighton, Sep. 2013.
82.	Akihiro Haga, S. Saegusa, J. Kotoku, H. Yamashita and et. al. : What Respiratory-Phase CT Is Optimal in Treatment Planning of Lung Cancer?, International Journal of Radiation OncologyBiologyPhysics, Vol.87, S531, Atlanta, Sep. 2013. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ijrobp.2013.06.1404 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.ijrobp.2013.06.1404 (DOI: 10.1016/j.ijrobp.2013.06.1404)
83.	K. Shiraishi, K. Yamamoto, Akihiro Haga, A. Sakumi and et. al : Volumetric Modulated Arc Therapy (VMAT) in the Treatment of Localized Prostate Cancer: Initial Experience in 200 Patients, International Journal of Radiation OncologyBiologyPhysics, Vol.90, S436, Atlanta, Sep. 2013. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ijrobp.2014.05.1372 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.ijrobp.2014.05.1372 (DOI: 10.1016/j.ijrobp.2014.05.1372)
84.	K. Nakagawa, Akihiro Haga, W. Takahashi, H. Yamashita and et. al. : Accuracy Verification of 4D Cone Beam CT (CBCT)-Based Lung Tumor Registration by Way of In-Treatment 4D CBCT Analysis, International Journal of Radiation OncologyBiologyPhysics, Vol.87, S752, Atlanta, Sep. 2013. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ijrobp.2013.06.1993 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.ijrobp.2013.06.1993 (DOI: 10.1016/j.ijrobp.2013.06.1993)
85.	M Futaguchi, Akihiro Haga, A Sakumi, H Okamoto, M Hamada, M Fuse, Y Abe, N Murakami, J Itami and K Nakagawa : Calculation of Dose Distributions From 106 Ruthenium COC Type Eye Applicator, Medical Physics, Vol.40, 282, Indianapolis, Aug. 2013. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4814772 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85024809645 (DOI: 10.1118/1.4814772, Elsevier: Scopus)
86.	N Saotome, S Kida, T Imae, K Sasaki, A Sakumi, Y Masutani, Akihiro Haga and K Nakagawa : The Performance Test of EPID for In Vivo Dosimetry, Medical Physics, Vol.40, 202, Indianapolis, Aug. 2013. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4814431 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85024779301 (DOI: 10.1118/1.4814431, Elsevier: Scopus)
87.	Akihiro Haga, J Kotoku, S Kida, Y Masutani, H Yamashita, W Takahashi, T Imae and K Nakagawa : Registration Accuracy with Four Dimensional Cone Beam CT for Lung Cancer Treatment, Medical Physics, Vol.40, 92, Indianapolis, Aug. 2013. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4813966 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85024790712 (DOI: 10.1118/1.4813966, Elsevier: Scopus)
88.	M Nakano, S Kida, Y Masutani, T Shiraki, K Shiraishi, K Yamamoto, K Nakagawa and Akihiro Haga : Time Ordered Four Dimensional Cone Beam CT, Medical Physics, Vol.40, 178, Indianapolis, Aug. 2013. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4814334 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85024773674 (DOI: 10.1118/1.4814334, Elsevier: Scopus)
89.	J Kotoku, Akihiro Haga, S Kumagai, N Saotome and T Kobayashi : Energy Spectrum Inference of Clinical Photon Beam by Use of PDD, Medical Physics, Vol.40, 396, Indianapolis, Aug. 2013. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4815237 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85024797225 (DOI: 10.1118/1.4815237, Elsevier: Scopus)
90.	S Kida, M Nakano, Y Masutani, K Nakagawa and Akihiro Haga : Improvement of Four Dimensional Cone Beam CT Image Quality with Iterative Reconstruction, Medical Physics, Vol.40, 128, Indianapolis, Aug. 2013. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4814122 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85024795982 (DOI: 10.1118/1.4814122, Elsevier: Scopus)
91.	Wataru Takahashi, Akihiro Haga, Naotaka Yamamoto, Hideomi Yamashita, Toshikazu Imae, Satoshi Kida, Akira Sakumi, Kuni Ohtomo, Tadashi Kamada and Keiichi Nakagawa : Verification System for Image-Guided Radiation Therapy by Using In-Treatment 4D CBCT, 2nd ESTRO Forum, Geneva, Apr. 2013.
92.	K. Nakagawa, Akihiro Haga, W. Takahashi, H. Yamashita, T. Imae, S. Kida, Y. Masutani, A. Sakumi and K. Ohtomo : Accuracy comparison of 3D and 4D CBCT based lung tumor registrations by way in-treatment 4D CBCT analysis, 2nd ESTRO Forum, Geneva, Apr. 2013. (Link to Publication Site) ● Publication site (DOI): 10.1016/S0167-8140(15)32349-5 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/S0167-8140(15)32349-5 (DOI: 10.1016/S0167-8140(15)32349-5)
93.	Akihiro Haga, S. Takenaka, T. Image, A. Sakumi, K. Ohtomo and K. Nakagawa : Independent dose calculation with X-ray voxel Monte Carlo algorithm in volumetric modulated arc therapy, 2nd ESTRO Forum, Geneva, Apr. 2013. (Link to Publication Site) ● Publication site (DOI): 10.1016/S0167-8140(15)33102-9 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/S0167-8140(15)33102-9 (DOI: 10.1016/S0167-8140(15)33102-9)
94.	Masatoshi Hashimoto, Akihiro Haga, R. Takahashi, M. Nakajima and et al. : Verification of MLC Position Using Electric Portal Imaging Device During VMAT Delivery, International Journal of Radiation OncologyBiologyPhysics, Vol.84, No.Supplement, S768-S769, Boston, Oct. 2012. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ijrobp.2012.07.2057 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.ijrobp.2012.07.2057 (DOI: 10.1016/j.ijrobp.2012.07.2057)
95.	Akihiro Haga, K Nakagawa, H Yamashita, W Takahashi, A Sakumi, Y Masutani, S Kida, N Saotome, S Itoh, T Shiraki and K Ohtomo : Determination of PTV Margin for Lung Tumor Using In-Treatment 4D CBCT, Medical Physics, Vol.39, No.6Part9, 3699, Charlotte, Aug. 2012. (Summary) To determine a planning target volume (PTV) margin for lung cancer patients using a four-dimensional cone-beam CT (4D CBCT) acquired during volumetric modulated arc therapy (VMAT) treatment. A VMAT plan for lung cancer patients was created by Pinnacle v9.0 (Philips) treatment planning system (TPS), where the gross target volume (GTVs) in each breathing phase was delineated by using 4D-planning CT scan (TOSHIBA and ANZAI). The VMAT treatment was performed with a stereotactic body frame after the registration using Elekta X-ray volume imaging (XVI) unit. Simultaneous cone-beam projection images were acquired for 3 or 4 fractions of 10 patients. The in-treatment 4D CBCT was reconstructed by dividing into four breathing phase bins. A total of 38 in-treatment 4D-CBCT sets were exported to Pinnacle TPS. The isocenter of in-treatment 4D CBCT was matched with that of 4D-planning CT. The tumor motion during treatment was manually tracked on in-treatment 4D CBCT, and the center-of-mass (COM) location of the tumor was estimated. Analyzing the tumor regions observed by in-treatment 4D CBCT, a PTV margin in our system was derived. The average difference in COM location of the tumor was less than 1mm for all directions, while the standard deviations (SD's) were about 1.3mm, 1.6mm, and 2.1mm for the lateral, the vertical, and the longitudinal directions, respectively. The large discrepancy more than 3mm was observed for one patient. The required PTV margin was about 3-4mm for the lateral and the vertical directions, whereas it was about 5mm for the longitudinal direction. The uncertainties of the tumor motion caused by respiration were observed by in-treatment 4D CBCT images. It was feasible to determine the PTV margin from 4D volume images. K. Nakagawa receives research funding from Elekta. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4735043 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 28519043 ● Summary page in Scopus @ Elsevier: 2-s2.0-85024826929 (DOI: 10.1118/1.4735043, PubMed: 28519043, Elsevier: Scopus)
96.	S Itoh, Akihiro Haga, Y Fukuda, N Sasano, T Onoe, S Shibata and K Nakagawa : Dose Distribution Generated from ¹³¹ I Radionuclide Using SPECT-CT., Medical Physics, Vol.39, 3694-3695, Charlotte, Aug. 2012. (Summary) (131) I radionuclide therapy is widely performed in a thyroid cancer treatment, but there has been almost no evaluation of the dose distribution. The aim of this work is to develop the calculation system using the data of SPECT-CT and to examine the effects of their image resolutions on the dose distribution. We designed and constructed an acrylic phantom for measurement. A radioactive iodine capsule and glass dosimeters can be set in the layer structure of the phantom. We put iodine capsules (37MBq, 111MBq, 185MBq) in the middle of the phantom and acquired SPECT-CT (Infinia Hawkeye4 (GE)) images. Both the CT value data (image resolution: 1.1 mm) and the intensity map data of SPECT (image resolution: 4.4 mm) were independently used for the estimation of the cumulative dose distribution generated from the radioactive iodine in the phantom. We adopted Monte Carlo program PHITS2.0 as the simulation of the dose calculation. The absolute dose was measured by glass dosimeters. The measurement result by glass dosimeters was very similar to the Monte Carlo simulation result, in which the difference was about 0.3 %. We obtained the dose distributions reconstructed by the radioactive iodine distribution using CT value data and SPECT data, respectively. The iodine distribution from CT could be finer than that of SPECT data because of its higher image resolution. As a result, the difference was found to be factor two in the middle of the iodine distribution. On the other hand, both of the dose distribution was almost same above 2.2 cm distance from the center. We can reconstruct the 131I dose distribution using SPECT-CT data. For more accurate calculation of the dose distribution, it would be crucial to increase the resolution of SPECT data. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4735024 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 28519058 ● Summary page in Scopus @ Elsevier: 2-s2.0-85024793063 (DOI: 10.1118/1.4735024, PubMed: 28519058, Elsevier: Scopus)
97.	N Saotome, S Kida, Akihiro Haga, Y Masutani, K Ohtomo and K Nakagawa : Geometric Agreement Check for Imaging System with Radiation Beam by KV and MV-CBCT., Medical Physics, Vol.39, 3659, Charlotte, Aug. 2012. (Summary) The verification method of the geometry agreement between a light field and/or a laser coordinate and treatment beam should be easy and quick. In this presentation, we propose a novel QA method by using both kV- and MV-CBCT for kV-IGRT system. This method confirms the temporal unchanging the agreement of geometry in the kV-IGRT system with the treatment beam geometry. 1) MV-flexmap: Sequential MV-projection images were acquired during gantry rotation by iViewGT (Elekta) and MV-CBCT was reconstructed by in-house software with a flexmap correction. The flexmap is displacement of gantry and detector panel related with gantry sag. The geometric change affects the deranging reconstructed image. To evaluate how much displacement of EPID panel and gantry was detectable, the images of 8mm diameter ball-bearing (BB) located at the radiation isocenter were reconstructed with improper Flexmap.2) A comparison between the kV-CBCT and the MV-CBCT: The kV-CBCT was provided by X-ray Volume image (XVI) system (Elekta). To confirm the agreement for the geometry between kV-IGRT system and treatment beam, the kV-CBCTs of BB are compared with that of MV-CBCTs. The flexmaps were modified to (b)1mm / (c)3mm shifted to the rotation direction and (d)3mm to the rotation axis. The MV-CBCT were reconstructed with the correct flexmap and with incorrect flexmap (b), (c) and (d).ã The geometric confirmation for MV-CBCT was done by comparison of the width and center of the BB on the MV-CBCT. The discrepancy of center between kV-CBCT and MV-CBCT was less than 1mm. Less than 1mm of the geometrical changing to rotation direction for MV-detector panel could be recognized by reconstructed images of BB. Using kV- and MV-CBCT enable us to perform the simple comparison for geometrical non-idealities between the kV-IGRT system and the treatment beam. Dr. K. Nakagawa received research grant from Elekta. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4734866 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 28517564 ● Summary page in Scopus @ Elsevier: 2-s2.0-85024822757 (DOI: 10.1118/1.4734866, PubMed: 28517564, Elsevier: Scopus)
98.	J Kotoku, T Kobayashi, N Arai, T Hatakeyama, S Kumagai and Akihiro Haga : Energy Spectrum Inference of X Rays from a Linac, Medical Physics, Vol.39, 3715, Charlotte, Aug. 2012. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.4735112 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85024793493 (DOI: 10.1118/1.4735112, Elsevier: Scopus)
99.	Akira Sakumi, Akihiro Haga, Kenshiro Shiraishi, Tsuyoshi Onoe, Kentaro Yamamoto, Akihiro Nomoto, Yoshio Iwai, Kiyoshi Yoda, Kuni Ohtomo and Keiichi Nakagawa : INTERCOMPARISON OF IMPACT OF CONTINUOUSLY VARIABLE DOSE RATE ON THREE TREATMENT PLANNING SYSTEMS FOR ELEKTA VMAT, Radiotherapy and Oncology, Vol.103, S588, Barcelona, May 2012. (Link to Publication Site) ● Publication site (DOI): 10.1016/S0167-8140(12)71865-0 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/S0167-8140(12)71865-0 (DOI: 10.1016/S0167-8140(12)71865-0)
100.	Keiichi Nakagawa, Yoshitaka Masutani, Akihiro Haga, Hideomi Yamashita, Wataru Takahashi, Akira Sakumi, Satoshi Kida, Naoya Saotome, Kuni Ohtomo and Kiyoshi Yoda : A COMPARISON OF THREE REGISTRATION METHODS USING 4D PLANNING CT AND ON-BOARD 4D CONE-BEAM CT FOR LUNG VMAT, Radiotherapy and Oncology, Vol.103, S556, Barcelona, May 2012. (Link to Publication Site) ● Publication site (DOI): 10.1016/S0167-8140(12)71791-7 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/S0167-8140(12)71791-7 (DOI: 10.1016/S0167-8140(12)71791-7)
101.	Naoya Saotome, Akira Sakumi, Satohi Kida, Tsuyoshi Onoe, Akihiro Haga, Yoshitaka Masutani, Kuni Ohtomo, Hideomi Yamashita and Keiichi Nakagawa : DOSE COMPARSION BETWEEN DIFFERENT RESPIRATORY PHASES DURING LUNG VMAT DELIVERY USING IN-TREATMENT 4D CONE-BEAM CT, Radiotherapy and Oncology, Vol.103, S188-S189, Barcelona, May 2012. (Link to Publication Site) ● Publication site (DOI): 10.1016/S0167-8140(12)70809-5 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/S0167-8140(12)70809-5 (DOI: 10.1016/S0167-8140(12)70809-5)
102.	Kenshiro Shiraishi, Akira Sakumi, Akihiro Haga, Tsuyoshi Onoe, Kentaro Yamamoto, Kae Okuma, Kiyoshi Yoda and Keiichi Nakagawa : IMPACT OF EMBEDDED SEEDS ON DOSE DISTRIBUTIONS IN X-RAY RADIOTHERAPY: A PHANTOM STUDY WITH MONTE CARLO CALCULATION., Radiotherapy and Oncology, Vol.103, S68-S69, Barcelona, May 2012. (Link to Publication Site) ● Publication site (DOI): 10.1016/S0167-8140(12)72133-3 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/S0167-8140(12)72133-3 (DOI: 10.1016/S0167-8140(12)72133-3)
103.	Akira Sakumi, Akihiro Haga, Satoshi Kida, Naoya Saotome, Kiyoshi Yoda and Keiichi Nakagawa : IN-TREATMENT DOSE CALCULATION USING INTREATMENT 4D KILOVOLTAGE CONE-BEAM CT AND IN-TREATMENT LINAC PARAMETERS DURING VOLUMETRIC MODULATED ARC THERAPY FOR A LUNG TUMOR, Radiotherapy and Oncology, Vol.102, S125, Barcelona, May 2012. (Link to Publication Site) ● Publication site (DOI): 10.1016/S0167-8140(12)70212-8 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/S0167-8140(12)70212-8 (DOI: 10.1016/S0167-8140(12)70212-8)
104.	Akihiro Haga : Four Dimensional Cone-Beam CT and VMAT, Second International Conference on Real-time Tumor-tracking Radiation Therapy with 4D Molecular Imaging Technique, Kyoto, Feb. 2012.
105.	Akihiro Haga : Lung SBRT using VMAT and in-treatment KV & MV cone beam CT imaging, Symposium for Stereotactic Body Radiation Therapy (SBRT) for Liver, Lung & Spine Tumour, New Delhi, Jan. 2012.
106.	Akihiro Haga, Satoshi Kida, Naoya Saotome, Yoshitaka Masutani, Akira Sakumi, Saori Itoh, Hideomi Yamashita, Toshikazu Imae, Kuni Ohtomo and Keiichi Nakagawa : Novel verification method using in-treatment 4D CBCT, The 6th JKMP and the 11th AOCMP Meeting, Fukuoka, Sep. 2011.
107.	Satoshi Kida, Akihiro Haga, Naoya Saotome, Yoshitaka Masutani, Hideomi Yamashita and Keiichi Nakagawa : The development of portal image based phase recognition, The 6th JKMP and the 11th AOCMP Meeting, Fukuoka, Sep. 2011.
108.	Naoya Saotome, Satoshi Kida, Tsuyoshi Onoe, Akira Sakumi, Yoshitaka Masutani, Akihiro Haga, Kuni Otomo and Keiichi Nakagawa : Treated Dose Calculation with Binary Density For IGRT, The 6th JKMP and the 11th AOCMP Meeting, Fukuoka, Sep. 2011.
109.	Toshikazu Imae, Akihiro Haga, Tsuyoshi Onoe, Keiichi Nakagawa, K Ino, Yukari Okano, K Tanaka, Katsutake Sasaki, Shigeki Saegusa, Takashi Shiraki, T Oritate, K Yano and H Shinohara : Motion Analysis of Target during Stereotactic Radiotherapy of Lung Tumors Using Volumetric Modulated Arc Therapy, Medical Physics, Vol.38, 3475, Vancouver, Aug. 2011. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.3611908 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85024798176 (DOI: 10.1118/1.3611908, Elsevier: Scopus)
110.	Naoya Saotome, Satoshi Kida, Tsuyoshi Onoe, Katsutake Sasaki, Toshikazu Imae, Kenichiro Tanaka, Akira Sakumi, Yoshitaka Masutani, Akihiro Haga and Keiichi Nakagawa : Projection Image Correction for 4D VMAT CT, Medical Physics, Vol.38, 3611, Vancouver, Aug. 2011. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.3612492 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85024823008 (DOI: 10.1118/1.3612492, Elsevier: Scopus)
111.	Jun'ichi Kotoku, Akihiro Haga, Shigeki Saegusa, N Arai, T Kobayashi, K Wada, Akira Sakumi, Saori Itoh, Satoshi Kida, Naoya Saotome, Keiichi Nakagawa, H Kanemitsu and K Ishioka : Dose calculation of kV Cone-Beam CT by use of Pinnacle, Medical Physics, Vol.38, 3723, Vancouver, Aug. 2011. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.3613006 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85024817411 (DOI: 10.1118/1.3613006, Elsevier: Scopus)
112.	Keiichi Nakagawa, Akihiro Haga, Hideomi Yamashita, Satoshi Kida, Naoya Saotome, Akira Sakumi, Toshikazu Imae, Kuni Ohtomo, Yoshitaka Masutani and Kiyoshi Yoda : Beam's eye view based verification of actual delivery using in-treatment 4D cone-beam CT, Radiotherapy and Oncology, Vol.99, S438, London, May 2011.
113.	Akira Sakumi, Akihiro Haga, Satoshi Kida, Naoya Saotome, Yukari Okano, Kenshiro Shiraishi, Tsuyoshi Onoe, Kiyoshi Yoda and Keiichi Nakagawa : In-treatment cone-beam CT and in-treatment Linac parameters can reproduce semi-actual dose distribution, Radiotherapy and Oncology, Vol.99, S446, London, May 2011.
114.	Hirotaka Ito, Seiichi Koshizuka, R. Shino, Akihiro Haga, Tsuyoshi Onoe and Keiichi Nakagawa : Quasi-4DCT Images based on Physics-based Lung Deformation Simulation, Radiotherapy and Oncology, Vol.99, S484, London, May 2011.
115.	Akihiro Haga, A Sakumi, S Itoh, D Kon, N Saotome, K Ohtomo, K Shiraishi, K Nakagawa and Y Iwai : VMAT with Monaco, the novel TPS: Present status and future application, Radiotherapy and Oncology, Vol.99, S498-S499, London, May 2011. (Link to Publication Site) ● Publication site (DOI): 10.1016/S0167-8140(11)71455-4 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/S0167-8140(11)71455-4 (DOI: 10.1016/S0167-8140(11)71455-4)
116.	Kenshiro Shiraishi, Akihiro Haga, Katsutake Sasaki, Naoya Saotome, Satoshi Kida, Akira Sakumi, Tsuyoshi Onoe, Kentaro Yamamoto, Kuni Ohtomo and Keiichi Nakagawa : Analysis of patient setup uncertainties by using in-treatment cone-beam CT for prostate cancer, Radiotherapy and Oncology, Vol.99, S472, London, May 2011.
117.	Hirotaka Ito, Seiichi Koshizuka, R. Shino, Akihiro Haga, Tsuyoshi Onoe and Keiichi Nakagawa : Quasi-4DCT Images based on Lung Deformation Simulation Considering Rib Kinematics, International Forum on Medical Imaging in Asia (IFMIA), Okinawa, Jan. 2011.
118.	Akira Sakumi, Kenshiro Shiraishi, Akihiro Haga, Keiichi Nakagawa, Kuni Ohtomo and Kiyoshi Yoda : Single arc VMAT for locoreginal radiotherapy of leaf-sided breast cancer, Radiotherapy and Oncology, Vol.96, S506, Barcelona, Sep. 2010.
119.	Kenshiro Shiraishi, Akihiro Haga, Akira Sakumi, Keiichi Nakagawa, Kuni Ohtomo and Kiyoshi Yoda : Comparison of the performance among treatment planning systems for prostate cancer with ELEKTA VMAT, Radiotherapy and Oncology, Vol.96, S548, Barcelona, Sep. 2010.
120.	Atsuro Terahara, Akira Sakumi, Akihiro Haga, Yoshio Iwai, Kiyoshi Yoda and Keiichi Nakagawa : Initial clinical experience of volumetric modulated arc therapy for head and neck boost using MONACO, Radiotherapy and Oncology, Vol.96, S320, Barcelona, Sep. 2010.
121.	Akihiro Haga, Taeko Matsuura, Satoshi Kida, Yoshitaka Masutani, Saori Itoh, Naoya Saotome, Akira Sakumi, Jun'ichi Kotoku, Atsuro Terahara and Keiichi Nakagawa : Tracking volume change during IMRT treatment using non-rigid registration, Radiotherapy and Oncology, Vol.96, S444, Barcelona, Sep. 2010.
122.	Naoya Saotome, Akihiro Haga, Satoshi Kida, Saori Itoh, Taeko Matsuura, Yoshio Iwai and Keiichi Nakagawa : Comparison of leaf position between EPID imaging and log file for in-vivo dosimetry, Radiotherapy and Oncology, Vol.96, S451, Barcelona, Sep. 2010.
123.	Jun'ichi Kotoku, Akira Sakumi, Akihiro Haga, Nobuhiro Arai, H Wada, T Kobayashi, T Fujii, Shigeki Saegusa, Taeko Matsuura, Saori Itoh, Satoshi Kida, Keiichi Nakagawa and Hiroshi Igaki : Comparative verification of absorbed dose within inhomogeneities between commercially available TPSs, Radiotherapy and Oncology, Vol.96, S482, Barcelona, Sep. 2010.
124.	Hirotaka Ito, Seiichi Koshizuka, M. Sakai, Akihiro Haga, Tsuyoshi Onoe, Hideomi Yamashita and Keiichi Nakagawa : Lung Deformation Simulation Using MPS Method for High Accuracy Radiotherapy, WCCM/APCOM 2010Proceedings, 19-23, Sydney, Jul. 2010.
125.	Satoshi Kida, Taeko Matsuura, Akihiro Haga, Yoshitaka Masutani, Saori Itoh, Naoya Saotome, Akira Sakumi, Jun'ichi Kotoku, Kenichiro Tanaka and Keiichi Nakagawa : CBCT Reconstruction during VMAT Delivery Using Elekta Synergy System, Medical Physics, Vol.37, 3107, Pennsylvania, Jul. 2010. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.3468058 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85024802980 (DOI: 10.1118/1.3468058, Elsevier: Scopus)
126.	Akihiro Haga, Satoshi Kida, Yukari Okano, Saori Itoh, Taeko Matsuura, Naoya Saotome, Akira Sakumi, Jun'ichi Kotoku, Kenshiro Shiraishi, Keiichi Nakagawa and Yoshio Iwai : Comparison of Treatment Planning Systems in Elekta Volumetric Modulated Arc Therapy (Elekta VMAT) Prostate Cancer Study, Medical Physics, Vol.37, 3215, Pennsylvania, Jul. 2010. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.3468525 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85024784652 (DOI: 10.1118/1.3468525, Elsevier: Scopus)
127.	Akihiro Haga, Keiichi Nakagawa, Kenshiro Shiraishi, Saori Itoh, Satoshi Kida, Kuni Ohtomo, Shigeki Saegusa, Katsutake Sasaki, Kiyoshi Yoda and Roberto Pellegrini : Property of volumetric modulated arc therapy (VMAT) for prostate cancer using ELEKTA Synergy and ERGO++ treatment planning system, Radiotherapy and Oncology, Vol.88, S216, Maastricht, Holland, Sep. 2009.
128.	Keiichi Nakagawa, Kenshiro Shiraishi, Satoshi Kida, Akihiro Haga, Kentaro Yamamoto, Shigeki Saegusa, Atsuro Terahara, Saori Itoh and Kiyoshi Yoda : Prostate displacement before and after treatment relative to the position during VMAT delivery, Radiotherapy and Oncology, Vol.88, S216, Maastricht, Holland, Sep. 2009.
129.	Akihiro Haga, Keiichi Nakagawa, Kenshiro Shiraishi, Saori Itoh, Toshikazu Imae, Katsutake Sasaki, Satoshi Kida, Atsuro Terahara and Kiyoshi Yoda : Influence of Gantry Angle, Multi Leaf Collimator (MLC), and Jaw Position Errors to Dose Distribution in Volumetric Modulated Arc Therapy (VMAT), Medical Physics, Vol.36, No.6Part12, 2577, Anaheim, Jul. 2009. (Link to Publication Site) ● Publication site (DOI): 10.1118/1.3181723 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1118/1.3181723 (DOI: 10.1118/1.3181723)
130.	Akihiro Haga, Keiichi Nakagawa, Kenshiro Shiraishi, Kuni Ohtomo, Yukari Okano, Takashi Oritate, Kiyoshi Yoda and Roberto G. Pellegrini : Physical Aspects of Volumetric Modulated Arc Therapy (VMAT) using Elekta synergy® and Ergo++ Treatment Planning system, International Journal of Radiation OncologyBiologyPhysics, Vol.72, No.Issue 1 supplement1, 656, Boston, Sep. 2008. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.ijrobp.2008.06.341 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.ijrobp.2008.06.341 (DOI: 10.1016/j.ijrobp.2008.06.341)
131.	Kenshiro Shiraishi, Keiichi Nakagawa, Akihiro Haga, Kuni Ohtomo, Yukari Okano, Takashi Oritate and Kiyoshi Yoda : Clinical Implementation of IMAT Prostate Treatment using Elekta synergy® and Ergo++ Treatment Planning system, International Journal of Radiation OncologyBiologyPhysics, Vol.72, No.supplement1, 650, Boston, Sep. 2008.
132.	Akihiro Haga, Keiichi Nakagawa, Kenshiro Shiraishi, Kuni Ohtomo, Yukari Okano, Takashi Oritate, Kiyoshi Yoda and Roberto. G. Pellegrini : Physical Aspects of Volumetric Modulated Arc Therapy (VMAT) using Elekta synergy and Ergo++ Treatment Planning system, Radiotherapy and Oncology, Vol.88, S420, Goteberg, Sweden, Sep. 2008.
133.	Kenshiro Shiraishi, Keiichi Nakagawa, Akihiro Haga, Kuni Ohtomo, Yukari Okano, Takashi Oritate and Kiyoshi Yoda : Clinical Implementation of IMAT Prostate Treatment using Elekta synergy, Radiotherapy and Oncology, Vol.88, S315, Goteberg, Sweden, Sep. 2008.
134.	G. Saxena, H. L. Yadav, Akihiro Haga and H. Toki : WEAKLY BOUND STRUCTURES, HALOS AND MAGICITY IN NEUTRON RICH NUCLEI, Modern Physics Letters A, Vol.23, No.27 30, 2589-2592, Osaka, Nov. 2007. (Link to Publication Site) ● Publication site (DOI): 10.1142/S0217732308029861 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1142/S0217732308029861 (DOI: 10.1142/S0217732308029861)
135.	Akihiro Haga, Hiroshi Toki, Setsuo Tamenaga and Yataro Horikawa : Nuclear ground states and excitations in relativistic nuclear models, Modern Physics Letters A, Vol.23, No.27 30, 2485-2488, Osaka, Nov. 2007. (Link to Publication Site) ● Publication site (DOI): 10.1142/S0217732308029642 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1142/S0217732308029642 (DOI: 10.1142/S0217732308029642)
136.	Akihiro Haga, Yataro Horikawa, Yasutoshi Tanaka and Hiroshi Toki : Nuclear polarization effect in muonic heavy atoms, Chiral Symmetry in Hadron and Nuclear Physics (Chiral07), Osaka, Nov. 2007.
137.	Akihiro Haga : Nuclear structure in relativistic nuclear models, Korea-Japan Joint Nuclear Workshop, Korean Physical Society Conference, at International Convention Center in Jeju, Korea, Jeju, Korea, Oct. 2007.
138.	Akihiro Haga, Yataro Horikawa, Yasutoshi Tanaka and Hiroshi Toki : Reanalysis of ⁹⁰Zr and ²⁰⁸Pb atoms, Muon Science Researches at the RIKEN-RAL Muon Facility 2007, RIKEN, Wako, Jul. 2007.
139.	Akihiro Haga, Hiroshi Toki, Setsuo Tamenaga, Yoko Ogawa and Yataro Horikawa : G-matrix with negative energy states and spin-orbit splittings, International Nuclear Physics Conference (INPC2007), Tokyo International Forum, Tokyo, Jun. 2007.
140.	Akihiro Haga, Hiroshi Toki, Setsuo Tamenaga, Yoko Ogawa and Yataro Horikawa : Relativistic mean field and RPA with negative energy states for finite nuclei, International Nuclear Physics Conference (INPC2007), Tokyo International Forum, Tokyo, Jun. 2007.
141.	Setuo Tamenaga, Hiroshi Toki, Akihiro Haga and Yoko Ogawa : The massless linear sigma model with the Coleman-Weinberg mechanism in finite nuclei and at finite temperature, International Nuclear Physics Conference (INPC2007), Tokyo International Forum, Tokyo, Jun. 2007.
142.	Akihiro Haga, Hiroshi Toki, Setuo Tamenaga and Yataro Horikawa : Vacuum polarization effect in relativistic nuclear model, Pusan National University Press. Hadron and Nuclear Physics 2007, Busan, Feb. 2007.
143.	Setuo Tamenaga, Hiroshi Toki, Akihiro Haga and Yoko Ogawa : Renormalization of the linear sigma model with Coleman-Weinberg mechanism, Pusan National University Press. Hadron and Nuclear Physics 2007, Busan, Feb. 2007.
144.	Akihiro Haga, Hiroshi Toki, Setuo Tamenaga and Yataro Horikawa : Role of vacuum polarization in quantum hadrodynamics, The 2nd German-Japanese Workshop on Nuclear Structure and Astrophysics, RIKEN, Wako, Oct. 2006.
145.	Setuo Tamenaga, Hiroshi Toki, Akihiro Haga and Yoko Ogawa : Coleman-Weinberg mechanism with chiral symmetry for nuclear structure, The 2nd German-Japanese Workshop on Nuclear Structure and Astrophysics, RIKEN, Wako, Oct. 2006.
146.	Hiroshi Toki, Yoko Ogawa, Setuo Tamenaga and Akihiro Haga : Relativistic mean-field model for finite nuclei, International School of Nuclear Physics 28th course, Erice, Italy, Sep. 2006.
147.	Akihiro Haga, Hiroshi Toki, Setuo Tamenaga and Yataro Horikawa : Relativistic mean-field model and random phase approximation with vacuum polarization, Modern Physics Letters A, Vol.21, No.31-33, 2447-2454, Osaka, Apr. 2006. (Link to Publication Site) ● Publication site (DOI): 10.1142/S0217732306022092 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1142/S0217732306022092 (DOI: 10.1142/S0217732306022092)
148.	Setsuo Tamenaga, Hiroshi Toki, Akihiro Haga and Yoko Ogawa : One-loop corrections as origin of spontaneous chiral symmetry breaking in the massless chiral sigma model, Methods of many-body systems: mean field theories and beyond, RIKEN, Wako, Mar. 2006.
149.	Akihiro Haga, Hiroshi Toki, Setuo Tamenaga and Yataro Horikawa : Nuclear excitations in relativistic nuclear model, Methods of many-body systems: mean field theories and beyond, RIKEN, Wako, Mar. 2006.
150.	Setuo Tamenaga, Hiroshi Toki, Akihiro Haga and Yoko Ogawa : One-loop corrections as origin of spontaneous chiral symmetry breaking in the massless chiral sigma model (Hadrons at finite density), Soryushiron Kenkyu Electronics, Vol.114, No.2, B39-B44, Kyoto, Feb. 2006. (Link to Publication Site) ● Publication site (DOI): 10.24532/soken.114.2_B39 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.24532/soken.114.2_B39 (DOI: 10.24532/soken.114.2_B39)
151.	Taisuke Nagasawa, Yataro Horikawa, Akihiro Haga and Masahiro Nakano : Configuration mixing effects on hyperfine structure in the relativistic model, Journal of Physics: Conference Series, Vol.20, 207-208, Tokyo, Feb. 2005. (Link to Publication Site) ● Publication site (DOI): 10.1088/1742-6596/20/1/049 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1088/1742-6596/20/1/049 (DOI: 10.1088/1742-6596/20/1/049)
152.	Akihiro Haga, Setuo Tamenaga, Hiroshi Toki and Yataro Horikawa : Relativistic Hartree approach with exact treatment of vacuum polarization for finite nuclei, Proceedings in The 9th Asia Pacific Physics conference, Hanoi, Vietnam, Oct. 2004.
153.	Taisuke Nagasawa, Akihiro Haga, Masahiro Nakano and Yataro Horikawa : Hyperfine splitting in hydrogen-like ions and relativistic nuclear model, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, Vol.235, No.1-4, 61-64, Vilnius, Lithuania, Sep. 2004. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.nimb.2005.03.146 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-20144373656 (DOI: 10.1016/j.nimb.2005.03.146, Elsevier: Scopus)
154.	Akihiro Haga, Yataro Horikawa, Yasutoshi Tanaka and Hiroshi Toki : Full-relativistic calculation of nuclear polarization in atomic and muonic levels, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, Vol.235, No.1-4, 71-75, Vilnius, Lithuania, Sep. 2004. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.nimb.2005.03.148 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.nimb.2005.03.148 (DOI: 10.1016/j.nimb.2005.03.148)
155.	Akihiro Haga, Yataro Horikawa, Yasutoshi Tanaka and Hiroshi Toki : Relativistic and non-relativistic descriptions of nuclear polarization in electronic and muonic atoms, Nuclear Physics B - Proceedings Supplements, Vol.139, 234-237, Gran Sasso, Italy, May 2004. (Link to Publication Site) ● Publication site (DOI): 10.1016/j.nuclphysbps.2004.11.228 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-26444516171 (DOI: 10.1016/j.nuclphysbps.2004.11.228, Elsevier: Scopus)

Proceeding of Domestic Conference:

1.	門田香音, Yuki Matsumoto, Hitoshi Ikushima, Motoharu Sasaki and Akihiro Haga : 11C-Methionine PETラジオミクスによる脳腫瘍の再発と壊死の鑑別, 日本放射線腫瘍学会第37回学術大会, Nov. 2024.
2.	MURATA Seiya and Akihiro Haga : 敵対的生成ネットワークを用いたCBCTの画質改善, 第26回鹿児島放射線治療技術研究会, Aug. 2024.
3.	Chisato Tonoiso, Hitoshi Ikushima, Akihiro Haga, Motoharu Sasaki, Takashi Kawanaka, Akiko Kubo, Masato Nishimura and Akiko Abe : 子宮頸癌リンパ節転移の術前予測におけるMRIレディオミクス解析の有用性, 第66回日本婦人科腫瘍学会学術講演会, Jul. 2024.
4.	Akihiro Haga : 子宮頸癌におけるMRIレディオミクス, 小線源治療部会第26回大会, May 2024.
5.	Akihiro Haga : 特別企画「AI を用いた消化器診療はどこまで進んだのか?」まとめと今後の展望, 第110回日本消化器病学会総会, May 2024.
6.	Akihiro Haga : 徳島大学大学院保健科学研究科医学物理学コース紹介, 第127回日本医学物理学会, Apr. 2024.
7.	OHSHIRO Ryuji, Yuki Kanazawa, Akihiro Haga and Masafumi Harada : Development numerical phantom of MR signal for multi-component analysis in biological tissues, 第80回日本放射線技術学会総会学術大会, Apr. 2024.
8.	SEGUCHI Mayuka, Yuki Kanazawa, 宮地利明, Masafumi Harada, 三好光晴, Hiroaki Hayashi and Akihiro Haga : Clinical study of atherosclerosis using derivation viscosity function, 第80回日本放射線技術学会総会学術大会, Apr. 2024. (Summary) 【目的】拡散係数と粘稠度の関係は線形性を示すことが知られているが，以前に我々が開発した粘稠度算出手法は課題を残した．本研究の目的は，拡散強調画像(DWI)を用いて粘稠度を導出する手法を改良し，アテローム性動脈硬化症のプラークの物性を評価することである．【方法】本研究は，3テスラMRI装置を用いて検討を行った．ファントム実験は，濃度の異なるグリセリン水溶液(20–60%)を作成し，恒温水槽内を37 ℃の状態にして粘稠度を計測した．各濃度の水溶液からファントムを作成し，エコープラナー法を用いて DWI を撮像した．撮影パラメータは，繰り返し時間(TR)，10000 ms;エコー時間(TE)，63.5 ms;b値，0–1500 s/mm2 に設定した．DWI の信号値を単一指数関数モデルで曲線近似し，ADC を算出した．実測した水溶液の粘稠度と ADC の関係を直線近似により粘稠度関数を導出した．臨床検討は，頸動脈内膜剥離術(CEA)が施行され，粥腫形成ありと病理診断された頸動脈狭窄症患者(27名:平均年齢74±6.2歳)の DWI を撮像した．撮影パラメータは，TR，3000 ms;TE，52.1 ms;b値，0，250，500 s/mm2に設定した．ファントム実験と同様に，DWI の信号値から算出した ADC 値を粘稠度関数に当てはめ粘稠度を求め可視化した．各症例群間の粘稠度を比較した．【結果】グリセリン水溶液の粘稠度は，濃度により粘稠度が高値になった．ファントムのDw-MRIから算出した粘稠度の算出式は，y=-0.79x+2.94 となった．ここで，y は粘稠度，x はADCとする．臨床検討において，全症例の平均 ADC 値は，1.72 ± 0.489 [× 10-3 mm2/s]，平均粘稠度値は 0.003 ± 0.386 [µPa·s] となった．症候性および出血の有無で粘稠度を比較すると有意な差があった(P < 0.05および < 0.01)．
9.	Kanon Monda, Yuki Matsumoto, Motoharu Sasaki, Michihito Shimokawa, Akihiro Haga and Hitoshi Ikushima : 転移性脳腫瘍に対する放射線治療後の脳壊死と再発を鑑別する11C-Mthionine PET Radiomicsモデルの開発, 37, Mar. 2024.
10.	HORIKAWA Shohei, SATO Yoshihide, Yu Hasegawa and Akihiro Haga : Development of a generative energy spectrum model for radiotherapy, 第37回高精度放射線外部照射部会学術大会, Mar. 2024.
11.	Akihiro Haga : Next Artificial Intelligence in Medicine, 第十九回前立腺癌密封小線源永久挿入治療研究会, Feb. 2024.
12.	下川通仁, Motoharu Sasaki, Akihiro Haga and Hitoshi Ikushima : 脳転移放射線治療後の壊死と再発の鑑別におけるMTI Radiomicsの有用性, 日本放射線腫瘍学会第36回学術大会, Nov. 2023.
13.	Keiichiro Watanabe, TAKEYA Atsushi, Akihiro Haga and Eiji Tanaka : 仮想コーンビームCT(CBCT)とAI技術を用いたCBCT画像の歯の高精度オートセグメンテーション手法の開発, 第82回日本矯正歯科学会学術大会抄録集, 151, Nov. 2023.
14.	Akihiro Haga : A role and clinical application of virtual computed tomography system in the AI era, 第126回日本医学物理学会, Sep. 2023.
15.	Akihiro Haga : 徳島大学大学院保健科学研究科医学物理学コース紹介, 第125回日本医学物理学会, Apr. 2023.
16.	Mayuka Seguchi, Yuki Kanazawa, 宮地利明, Yuki Matsumoto, Masafumi Harada, 林裕晃 and Akihiro Haga : アテローム性動脈硬化症におけるプラーク性状評価のための粘稠度MRI, 第 79 回日本放射線技術学会総会学術大会, Apr. 2023.
17.	Shunsuke Uotani, Yuki Kanazawa, Ryuji Ohshiro, Akihiro Haga and Masafumi Harada : 最尤推定を用いたMyelin Water Fraction導出法の検討, 第 79 回日本放射線技術学会総会学術大会, Apr. 2023.
18.	akinari kasai, Jinsei Miyoshi, Akihiro Haga, Takashi Kawanaka and Hiroshi Miyamoto : CT画像に基づいたAI機械学習モデルによる食道扁平上皮癌に対する化学放射線療法の効果予測., 第20回日本臨床腫瘍学会学術集会, Mar. 2023.
19.	KAJINO Akimi, Hitoshi Ikushima, Motoharu Sasaki, Tamaki Otani, Michiko Yamashita and Akihiro Haga : 放射線治療効果を早期に検出できるRadiomics特徴量の探索, 日本放射線腫瘍学会第35回学術大会, Nov. 2022.
20.	Mayuka Seguchi, Yuki Kanazawa, Akihiro Haga and Masafumi Harada : アテローム性動脈硬化症におけるプラーク形成の潜在的危険因子の評価, 第50回日本放射線技術学会秋季学術大会, Oct. 2022.
21.	Shunsuke Uotani, Yuki Kanazawa, Ryuji Ohshiro, Akihiro Haga and Masafumi Harada : 信号雑音比を考慮したMyelin Water Fraction導出法の開発, 第50回日本放射線技術学会秋季学術大会, Oct. 2022.
22.	YAMAGUCHI Natsumi, Kenya Kusunose, Akihiro Haga, Yukina Hirata, Susumu Nishio, Hirotsugu Yamada and Masataka Sata : 人工知能を用いた左室壁運動異常の検出, 第86回日本循環器学会学術集会, Mar. 2022.
23.	Takaharu Kudoh, Keiko Kudoh, Hitoshi Ikushima, Akihiro Haga, Akira Takahashi and Youji Miyamoto : Dosimetric evaluation of contralateral neck in the unilateral neck volumetric modulated arc radiotherapy, 第19 回中国四国口腔癌研究会学術集会, Nov. 2021.
24.	Akihiro Haga : Physics and AI/Machine Learning, 第34回日本放射線腫瘍学会シンポジウム, Nov. 2021.
25.	Akinari Kasai, Jinsei Miyoshi, Akihiro Haga, Takashi Kawanaka, Koichi Okamoto, Naoki Muguruma, Yasushi Sato and Tetsuji Takayama : CT-based AI radiomics model for predicting complete response and progression free survival of chemoradiothearapy in ESCC., 第80回日本癌学会学術総会, Oct. 2021.
26.	FUKUDA Nagomi, Yuki Kanazawa, Yuki Matsumoto, KITANO Shun, Akihiro Haga and Masafumi Harada : VFA法における多変量正規分布の確率密度関数を用いたB1補正手法の提案, 第49回日本磁気共鳴医学会大会, Sep. 2021.
27.	IKEMITSU Natsuki, Yuki Kanazawa, Masafumi Harada, Yuki Matsumoto, 伊藤公輔, 谷口陽, 尾藤良孝 and Akihiro Haga : 白質領域に対する非剛体処理に伴う数値解析の影響, 第49回日本磁気共鳴医学会大会, Sep. 2021.
28.	Kitano Shun, Yuki Kanazawa, Masafumi Harada, Yuki Matsumoto, Fukuda Nagomi, Hiroaki Hayashi, 谷口陽 and Akihiro Haga : Myelin Water Fraction(MWF)におけるT2* cut-off値の検討, 第49回日本磁気共鳴医学会大会, Sep. 2021.
29.	Hitoshi Ikushima, 加納洋介, Motoharu Sasaki and Akihiro Haga : 深層学習を用いた子宮頸がん肉眼的腫瘍体積自動抽出, 第63回日本婦人科腫瘍学会, Jul. 2021.
30.	Akihiro Haga : レディオミクス(Radiomics)と生成モデル(Generative model)のアプローチ, 第32回臨床MR脳機能研究会, Jun. 2021.
31.	Higuchi Takayuki, Akihiro Haga and Yuki Kanazawa : Estimation of CT X-ray spectrum from reconstructed images using a deep neural network, 第121回日本医学物理学会, Apr. 2021.
32.	Fujiwara Daiyu and Akihiro Haga : Multi-material decomposition based on neural network, 第121回日本医学物理学会, Apr. 2021.
33.	Shimomura Taisei, Inoue Yuki and Akihiro Haga : X-ray scattering estimation with spherical harmonics in cone-beam computed tomography, 第121回日本医学物理学会, Apr. 2021.
34.	Jinsei Miyoshi, Akihiro Haga and Tetsuji Takayama : AI機械学習を用いたRadiogenomics解析による食道癌化学放射線療法感受性・p53変異予測モデルの構築., 第107回日本消化器病学会総会, Apr. 2021.
35.	Yohsuke Kanoh, Hitoshi Ikushima, Akihiro Haga and Motoharu Sasaki : 人工知能を用いた子宮頸がんの自動輪郭抽出, 第34回高精度放射線外部照射部会学術大会(WEB開催), Apr. 2021.
36.	Shun Kitano, Yuki Kanazawa, Yuki Matsumoto, Nagomi Fukuda, Kenta Sasaki, Akihiro Haga and Masafumi Harada : T₂値およびT₂^*値導出に係るMyelin Water Fraction(MWF)値の影響, 第77回日本放射線技術学会総会学術大会, Apr. 2021.
37.	Nagomi Fukuda, Yuki Kanazawa, Yuki Matsumoto, Shun Kitano, Kenta Sasaki, Akihiro Haga and Masafumi Harada : 可変フリップアングル法を用いたT1値算出における線形近似と非線形近似の比較, 第77回日本放射線技術学会総会学術大会, Apr. 2021.
38.	Kenya Kusunose, Akihiro Haga, Hirotsugu Yamada, Susumu Nishio, Yukina Hirata and Masataka Sata : 加速する循環器画像領域におけるAI研究の現在と未来, 日本超音波医学会第93回学術集会, Dec. 2020.
39.	Takaharu Kudoh, Akihiro Haga, Motoharu Sasaki, Chisato Tonoiso, Akiko Kubo, Takashi Kawanaka, Shunsuke Furutani and Hitoshi Ikushima : Radiomicsの手法を用いたPETによる舌癌頸部リンパ節転移の予測, 日本放射線腫瘍学会第33回学術大会, Oct. 2020.
40.	Hitoshi Ikushima and Akihiro Haga : MRI画像解析による子宮頸癌化学放射線療法後の予後予測-JROSG多施設共同調査研究-, 日本放射線腫瘍学会, Oct. 2020.
41.	Yuki Kanazawa, MAEDA Naoki, Masafumi Harada, 谷口陽, Yuki Matsumoto, Takashi Abe, Hiroaki Hayashi, 伊藤公輔, 尾藤良孝 and Akihiro Haga : Quantitative Parameter Mapping(QPM) を用いたコンポーネント間スピン交換定数の導出, 第48回日本磁気共鳴医学会大会Web開催, Sep. 2020.
42.	Kenya Kusunose, Akihiro Haga, Muneyuki Kadota, 石井亜由美, Takayuki Ise, Shusuke Yagi, Hirotsugu Yamada and Masataka Sata : 心エコーへのAI応用について, 第26回日本心臓リハビリテーション学会学術集会, Jul. 2020.
43.	Takahiro Nakamoto, Wataru Takahashi, Akihiro Haga, Satoshi Takahashi, Shigeru Kiryu, Kanabu Nawa, Takeshi Ohta, Sho Ozaki, Yuki Nozawa, Shota Tanaka, Akitake Mukasa and Keiichi Nakagawa : Radiomic-based prediction of malignant glioma grades using preoperative contrast-enhanced T1-weighted and T2-weighted magnetic resonance images, 第119回日本医学物理学会(WEB開催), May 2020. (Summary) We conducted a feasibility study to predict malignant glioma grades via radiomic analysis using contrast-enhanced T1-weighted magnetic resonance images (CE-T1WIs) and T2-weighted magnetic resonance images (T2WIs). We proposed a framework and applied it to CE-T1WIs and T2WIs (with tumor region data) acquired preoperatively from 157 patients with malignant glioma (grade III: 55, grade IV: 102) as the primary dataset and 67 patients with malignant glioma (grade III: 22, grade IV: 45) as the validation dataset. Radiomic features such as size/shape, intensity, histogram, and texture features were extracted from the tumor regions on the CE-T1WIs and T2WIs. The Wilcoxon-Mann-Whitney (WMW) test and least absolute shrinkage and selection operator logistic regression (LASSO-LR) were employed to select the radiomic features. Various machine learning (ML) algorithms were used to construct prediction models for the malignant glioma grades using the selected radiomic features. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the prediction models in the primary dataset. The selected radiomic features for all folds in the LOOCV of the primary dataset were used to perform an independent validation. As evaluation indices, accuracies, sensitivities, specificities, and values for the area under receiver operating characteristic curve (or simply the area under the curve (AUC)) for all prediction models were calculated. The mean AUC value for all prediction models constructed by the ML algorithms in the LOOCV of the primary dataset was 0.902 ± 0.024 (95% CI (confidence interval), 0.873-0.932). In the independent validation, the mean AUC value for all prediction models was 0.747 ± 0.034 (95% CI, 0.705-0.790). The results of this study suggest that the malignant glioma grades could be sufficiently and easily predicted by preparing the CE-T1WIs, T2WIs, and tumor delineations for each patient. Our proposed framework may be an effective tool for preoperatively grading malignant gliomas.
44.	Yuki Kinjo, Yuki Kanazawa, Masafumi Harada, Yo Taniguchi, Yuki Matsumoto, Takashi Abe, Hiroaki Hayashi, Masaharu Ono, Yoshitaka Bito and Akihiro Haga : Spatially smoothing processing of quantitative values in voxel-based morphometry (VBM) analysis, 第76回日本放射線技術学会総会学術大会WEB開催, May 2020.
45.	Yu Hasegawa, Akihiro Haga, Dousatsu Sakata, Yuki Kanazawa, Masahide Tominaga, Motoharu Sasaki, Toshikazu Imae and Keiichi Nakagawa : Estimation of X-ray energy spectrum for CT scanner from percentage depth dose measurement, 第119回⽇本医学物理学会学術⼤会WEB開催, May 2020.
46.	Shimomra Taisei and Akihiro Haga : Cone-beam CT image reconstruction with spherical harmonics, 第119回⽇本医学物理学会学術⼤会WEB開催, May 2020.
47.	Akihiro Haga : Multi-energy CT imaging as CT standardization, 第119回⽇本医学物理学会学術⼤会シンポジウムWEB開催, May 2020.
48.	Taisei Shimomura and Akihiro Haga : ルジャンドル関数による人体表現, 第33回高精度放射線外部照射部会学術大会 WEB開催, May 2020.
49.	Akihiro Haga : 高精度放射線治療におけるAI/レディオミクス:画像の標準化とレディオミクス, 第33回高精度放射線外部照射部会学術大会 WEB開催, May 2020.
50.	Yokota Kento and Akihiro Haga : 深層学習による電子相関を含む多電子原子系の波動関数の再現, 日本物理学会春季大会, Mar. 2020.
51.	Taisei Shimomura and Akihiro Haga : ルジャンドル関数を用いた人体表現, 第2回四国地区4大学大学院合同研究発表会, Dec. 2019.
52.	Chisato Tonoiso, Akihiro Haga, Akiko Kubo, Takashi Kawanaka, Shunsuke Furutani, Takaharu Kudoh, Hitoshi Ikushima and Masafumi Harada : 子宮頸部扁平上皮癌における予後因子の検討, 日本放射線腫瘍学会第32回学術大会, Nov. 2019.
53.	Masahide Tominaga, 山内奈緒, Akihiro Haga, Motoharu Sasaki and Takeshi Kojima : 治療計画用CT画像を用いた非小細胞肺癌の病理分類, 第15回中国四国放射線医療技術フォーラムCSFRT2019, Sep. 2019.
54.	Kenya Kusunose, Akihiro Haga, Hirotsugu Yamada, 中谷敏 and Masataka Sata : 心エコー図法における人工知能技術活用の今(シンポジウム19 AIでどこまで心臓病診療は進むか?), 第67回日本心臓病学会学術集会, Sep. 2019.
55.	Natsumi Yamaguchi, Kenya Kusunose, Akihiro Haga, Sae Morita, Yukina Hirata, Yuta Torii, 西尾進, nao Yamada, 阿部美保, Daiju Fukuda, Hirotsugu Yamada and Masataka Sata : 深層学習を用いた左室駆出率の推定:心エコー図法による検討(優秀演題メディカルスタッフ), 第67回日本心臓病学会学術集会, Sep. 2019.
56.	Yuki Kinjo, Yuki Kanazawa, Masafumi Harada, Yo Taniguchi, Yuki Matsumoto, Takashi Abe, Hiroaki Hayashi, Masaharu Ono, Yoshitaka Bito and Akihiro Haga : Comparison of estimation parameters of relaxation rate and susceptibility for myelin content with quantitative parameter mapping, JSMRM2019, Sep. 2019. (Summary) To evaluate myelin content derived from QPM, we compared relaxation rate and susceptibility using simultaneously acquired imaging datasets. The relaxation rate and susceptibility derived from QPM enabled us to simultaneously and specifically evaluate structural changes and brain iron homeostasis.
57.	Akihiro Haga : Physics, machine learning, and medicine, 第259回徳島医学会, Aug. 2019.
58.	Yuki Kanazawa, Masafumi Harada, 谷口陽, Hiroaki Hayashi, Takashi Abe, Maki Ohtomo, Yuki Matsumoto, 小野順玄, 尾藤良孝 and Akihiro Haga : Quantitative Parameter Mappingを用いたミエリンMRI, 第75回日本放射線技術学会総会学術大会, Apr. 2019.
59.	Akihiro Haga : 私が思う科研費獲得に重要なこと, 第117 回⽇本医学物理学会学術⼤会シンポジウム, Apr. 2019.
60.	仲本宗泰, 高橋渉, Akihiro Haga, 高橋慧, 名和要武, 太田岳史, 尾崎翔, 野沢祐樹, 田中将太, 武笠昭武 and 中川恵一 : ラディオミクスに基づく神経膠腫グレード分類の基礎的検討, 日本放射線腫瘍学会第31回学術大会, Oct. 2018.
61.	高橋渉, 仲本宗泰, Akihiro Haga, 高橋慧, 田中将太, 山下英臣 and 中川恵一 : MRI画像に基づくラディオミクスによる神経膠腫1p/19q共欠失の非侵襲的解析, 日本放射線腫瘍学会第31回学術大会, Oct. 2018.
62.	外礒千智, Akihiro Haga, 高橋彩加, Akiko Kubo, Takashi Kawanaka, Shunsuke Furutani, Hitoshi Ikushima and Masafumi Harada : 治療前MRI画像を用いた子宮頸癌予後因子の検討, 日本放射線腫瘍学会第31回学術大会, Oct. 2018.
63.	Ryoko Nagasawa, Yuki Kanazawa, Yuki Kinjoh, Daiki Chiba, Masafumi Harada, Hiroaki Hayashi, Yuki Matsumoto and Akihiro Haga : Comparison of curve fitting models of intravoxel incoherent motion analysis for shear stiffness, 第46回日本放射線技術学会秋季学術大会, Oct. 2018. (Summary) 【目的】拡散強調画像(DWI)から生体の硬さを推定するために，intravoxel incohernet motion(IVIM)手法を用いた3つの曲線解析手法を評価した．【方法】 1.5テスラMRI装置(GE社製)にて，ファントム実験を行なった．ファントムは，異なる濃度のagar(0.75，1.0，1.5，2.0wt%)をアクリルボックスの中に配列し，1 wt%のagarで満たした．DWIはsingle shot echo planner imagingを用いて，複数のb値(0，20，50，100，200，400，600，800，1000，1200，1500 s/mm2)に設定し撮像した．MR elastography(MRE)はMR touchを用いて，ドライバー周波数120 Hzに設定し撮像した．DWIは，関心領域を各サンプル内に設定し，各b値の信号値を取得した後，mono-exponential, bi-exponential, stretched-exponentialモデルを用いて曲線解析を行い，各種パラメータ(ADC，f，D，D，DDC，α)を算出した．このとき，MREのせん弾性率とDWIから取得した各種パラメータを比較した．【結果】 MREから算出した0.75，1.0，1.5，2.0 wt%ファントムのせん弾性率は，6.5，10.6，30.8，40.0 kPaであった．bi-exponentialモデルから算出したDは，MREのせん弾性率と負の相関を示した(r = 0.57)．stretched-exponentialモデルから算出したα値は，MREのせん弾性率と負の相関を示した(r = 0.80)．【結論】 stretched exponential モデルを用いた拡散解析は，生体内の組織の硬さを測定するのに有用な手法である．
64.	Daiki Chiba, Yuki Kanazawa, Yuki Kinjoh, Ryoko Nagasawa, 三好光晴, Hiroaki Hayashi, Masafumi Harada and Akihiro Haga : Quantitative analysis of local-offset frequency in chemical exchange saturation transfer (CEST) imaging, 第46回日本放射線技術学会秋季学術大会, Oct. 2018. (Summary) 【目的】この研究の目的は，CEST画像における局所のオフセット周波数を定量的に評価することである．【方法】 3テスラMRI装置(GE社製)にて，CESTイメージングを用いてファントム実験を行った．アクリルボックスの底にプラスチックを格子状に配列し，アルブミン水溶液(45 g/L)を封入したファントムを作成した．CESTイメージングの撮像シーケンスはsingle shot fast spin-echo法を使用し，オフセット周波数帯域，±7 ppm(Δ0.5 ppm); 最大B1，5.16 μTに設定した．オフセット周波数補正ありと無しのデータセットを取得した．MTパルスを付加しない画像(S0)と，各オフセット周波数(1，2，3.5 ppm)の画像に対して，ファントムの中心部および辺縁部の格子に関心領域を設定し，信号値を測定した．各関心領域について，S0画像の信号値で正規化し比較した．【結果】中心部と左右辺縁部では，オフセット周波数が高くなるほど信号値は基準値に近づいた．上下辺縁部では，オフセット周波数補正なしの場合に周波数2 ppmで最も基準値との差が大きくなった．各オフセット周波数での全領域で，オフセット周波数補正ありの場合は補正なしと比較して，全て基準値に近づいた．【結論】我々が提案した解析手法は，CEST後処理の評価指標として系統誤差を特定するのに有用である．
65.	Yuki Kinjoh, Yuki Kanazawa, Ryoko Nagasawa, 谷口陽, 尾藤良孝, 小野順玄, Masafumi Harada, Daiki Chiba, Yuki Matsumoto and Akihiro Haga : Quantitative Parameter Mapping(QPM)を用いたミエリン描出能の検討: ポリエチレングリコールを用いたファントム実験, 第46回日本放射線技術学会秋季学術大会, Oct. 2018. (Summary) 【目的】 quantitative parameter mapping(QPM)-MRIは，一度の撮像で定量値マップや任意のコントラスト画像を計算できる手法である．本研究は，myelinと分子量の近いポリエチレングリコール(PEG)と水の含有量の違いによる信号変化を定量するために，QPM-MRIを用いて検討した. 【方法】 3テスラMRI装置(日立社製)にて，3D-partially radio frequency-spoiled steady state gradient-echo法を用いてファントム実験を行った． PEG(平均分子量，420)と水の含有量が異なる水溶液(0-50 wt%)サンプルを配置したファントムを作成した．QPM-MRIの撮像条件は，TE，4.6-22.1 ms(∆TE，3.5 ms);TR，20，40 ms;フリップ角，10，25，35，40 deg.;RF phases，2，5，8，20，22とした．QPMデータセット撮像後，T1，T2，PDの定量値マップを作成した．定量値画像の各サンプル内に関心領域を設定し，定量値を比較した．【結果】 PEG溶液(0，10，20，30，40，50 wt%)の測定した定量値は，T1，510 ± 25，342 ± 19，229 ± 13，152 ± 21，130 ± 17，118 ± 14 ms;T2，338 ± 22，300 ± 22，275 ± 39，214 ± 36，179 ± 23，189 ± 28 ms;PD，218 ± 15，142 ± 6，99 ± 6，84 ± 10，71 ± 4，77 ± 12であった．PEG濃度が高くなるとT1，T2*，PDの減少する傾向を示した．【結論】分子量の近いmyelinでも同様の傾向を示すことが予想されるので，水含有量の変化がmyelinの信号変化に影響を及ぼす可能性があることを実験的に示すことができた．QPM-MRIは，加齢や疾患によるmyelin体積減少の評価に応用できる可能性がある．
66.	Pohl Michel, Demachi Kazuyuki, Chhatkuli Bhusal Ritu, Akihiro Haga and Uesaka Mituru : Combination of optical flow and principal component analysis for tumor motion analysis during X-ray radiotherapy, 第115回日本医学物理学会, 横浜, Apr. 2018.
67.	Akihiro Haga : Radiomics and Image Analysis, 日本医学物理学会日本医学放射線学会日本放射線技術学会ー合同シンポジウム, 横浜, Apr. 2018.
68.	Masahide Tominaga, Motoharu Sasaki, Motokatsu Yasutomo, Kenji Yamada, Takeshi Kojima, Akihiro Haga, Hitoshi Ikushima and 角谷倫之 : Digital Phantomを用いたImage Registrationソフトウェアの性能評価, 第31回高精度放射線外部照射部会学術大会, Feb. 2018.
69.	Akihiro Haga : ログファイルによる体内線量分布の推定, 第31回高精度外部放射線治療学会シンポジウム, Feb. 2018. (Keyword) 線量分布 / ログファイル
70.	今江禄一, 渡邊雄一, Akihiro Haga, 高橋渉 and 山下英臣 : Flattening Filter Free VMATを用いた前立腺癌に対する体幹部定位放射線治療中の線量分布の再構成, 第45回日本放射線技術学会秋季学術大会,広島国際会議場, Sep. 2017.
71.	Akihiro Haga : 画像誘導放射線治療の先端技術:治療中に撮像されたコーンビームCT の利用, 第36回日本医用画像工学会大会, Jul. 2017.
72.	C. Ritu, 出町和之, 上坂充 and Akihiro Haga : A feasibility study for analyzing abnormal motion using relative cross correlation in tracking moving tumors in radiation therapy, 第113回日本医学物理学会, Apr. 2017.
73.	Akihiro Haga : 画像誘導放射線治療(IGRT), 第5回JASTRO物理セミナー, Mar. 2017.
74.	Akihiro Haga : 医学物理研究の切り開く放射線治療の技術革新と臨床応用, 第2回名古屋大学放射線治療物理学セミナー, Feb. 2017.
75.	竹中亮介, Akihiro Haga, 山下英臣 and 他2名 : Intra-therapiutic MVCT reconstruction of Helical Tomotherapy using PICCS algorighm, 日本放射線腫瘍学会第29回学術大会, Nov. 2016.
76.	青木秀梨, 山下英臣, Akihiro Haga and 中川恵一 : The initial experience of FFF VMAT-SBRT in Tokyo University, 日本放射線腫瘍学会第29回学術大会, Nov. 2016.
77.	高橋渉, 今江禄一, 竹中重治, 山下英臣, 中川恵一 and Akihiro Haga : 4 dimensional dosimetric evaluation in flattening filter free volumetric modulated arc therapy, 日本放射線腫瘍学会第29回学術大会, Nov. 2016.
78.	名和要武, Akihiro Haga, 野元昭弘 and 他3名 : Evaluation of a commercial automatic treatment planning system for prostate cancers and its prospect, 日本放射線腫瘍学会第29回学術大会, Nov. 2016.
79.	Akihiro Haga : 最適化の基礎:その数理と応用, 慶應大学がんプロインテンシブコース, Oct. 2016.
80.	Akihiro Haga, 馬込大貴, 中野正博 and 他4名 : Planning constraint cone-beam CT reconstruction feasibility study -, 第112回日本医学物理学会, Sep. 2016.
81.	Akihiro Haga : 放射線治療の頭脳治療-計画装置- 本質的理解とモデリング, 第13回新潟放射線治療技術懇話会, Jul. 2016.
82.	大熊加恵, Akihiro Haga, 江原幸康 and 他4名 : CLINICAL RESULTS OF TOTAL SKIN IRRADIATION FOR T-CELL LYMPHOMA USING HELICAL TOMOTHERAPY : A REPORTS OF THREE CASES, 第75回日本医学放射線学会総会, Apr. 2016.
83.	馬込大貴, Akihiro Haga, 高橋豊 and 他5名 : Reconstruction of megavoltage computed tomography with rapid scan time and extended field of view, 第111回日本医学物理学会, Apr. 2016.
84.	C. Ritu, 出町和之, 上坂充 and Akihiro Haga : Phase recognition system for tracking tumor using pre 4D CBCT in FFF mode: A makerlss study, 第111回日本医学物理学会, Apr. 2016.
85.	Akihiro Haga, 坂田洞察, 馬込大貴 and 他4名 : Theoretical consideration of material decomposition with prior information compressed sensing, 第111回日本医学物理学会, Apr. 2016.
86.	竹中亮介, Akihiro Haga, 山下英臣 and 他2名 : What is an adequate target volume in IMRT for total body irradiation?, 日本放射線腫瘍学会第28回学術大会, Nov. 2015.
87.	Akihiro Haga : エレクタ社製研究リニアックにおけるFFFの臨床応用, 第5回東北医療技術学会ランチョンセミナー, Nov. 2015.
88.	Akihiro Haga : 理工系医学物理士としての臨床と教育現場での経験から, 第28回高精度放射線外部照射研究会シンポジウム, May 2015.
89.	今江禄一, Akihiro Haga, 竹中重治 and 他7名 : 非剛体レジストレーションを用いた肺定位放射線治療中の線量分布, 第109回日本医学物理学会, Apr. 2015.
90.	C. Ritu, 出町和之, 上坂充 and Akihiro Haga : A real time prediction and phase recognition of moving tumors based on pre-4DCBCT in radiation therapy, 第109回日本医学物理学会, Apr. 2015.
91.	Akihiro Haga, 中川恵一, Maurer C. and 他7名 : Visualization of the treatment area in helical tomotherapy system, 第109回日本医学物理学会, Apr. 2015.
92.	Akihiro Haga : 非荷電粒子と物質の相互作用, 平成26年度東京都がん診療連携拠点病院研修会, Mar. 2015.
93.	Akihiro Haga : 医学物理士と放射線技師-東京大学における役割分担, 平成26年度国立大学技師長会議, Jan. 2015.
94.	Akihiro Haga : VMATの最適化アルゴリズム, 東北大学医学物理セミナー, Nov. 2014.
95.	Akihiro Haga : 光電効果の物理, 群馬大学医工連携セミナー, Nov. 2014.
96.	Akihiro Haga : 放射線診療事故防止の在り方, 2014年医療機器安全管理研修会, Nov. 2014.
97.	Akihiro Haga : 医学物理士現状と課題, 医学物理士の現状と課題研究会, Nov. 2014.
98.	Akihiro Haga : 東京大学医学部附属病院における放射線管理の経験から, 平成26年度医療放射線管理講習会, Oct. 2014.
99.	Akihiro Haga : CBCT再構成の基礎, 医学物理サマーセミナー, Sep. 2014.
100.	中川恵一, Akihiro Haga, 佐々木克剛 and 他6名 : 呼吸抑制下定位VMATにおける腫瘍移動軌跡の再現性, 日本放射線技術学会第70回総会学術大会, Apr. 2014.
101.	佐々木誠, Akihiro Haga, 今江禄一 and 他7名 : 装置間における幾何学的補正テーブルの特性の比較, 日本放射線技術学会第70回総会学術大会, Apr. 2014.
102.	今江禄一, Akihiro Haga, 早乙女直也 and 他7名 : VMATを用いた体幹部定位放射線治療中の線量評価, 日本放射線技術学会第70回総会学術大会, Apr. 2014.
103.	中野正博, 木田智士, 増谷佳孝, 白木尚, 山本健太郎, 白石健史郎, 中川恵一 and Akihiro Haga : Full FOV reconstruction of cone-beam CT acquired by asymmetrically-located flat-panel detector, 第107回日本医学物理学会, Apr. 2014.
104.	C. Ritu, 出町和之, Akihiro Haga and 他2名 : A STUDY FOR THE CONTRAST ENHANCEMENT OF KV IMAGE FOR THE PREDICTION OF LUNG TUMOR MOTION AND DEFORMATION USING PCA, 第107回日本医学物理学会, Apr. 2014.
105.	今大輔, 作美明, 井垣 (名), Akihiro Haga and 中川恵一 : ガンマナイフによる頭蓋外被ばくの寄与要因, 第107回日本医学物理学会, Apr. 2014.
106.	馬込大貴, Akihiro Haga, 井垣浩 and 他5名 : Preliminary study for prediction of survival time after radiotherapy for high-grade glioma patients based on clinical and DVH features, 第107回日本医学物理学会, Apr. 2014.
107.	坂田洞察, Akihiro Haga, 増谷佳孝 and 他2名 : Measurement of Eective Charge Distribution by Dual-Energy, 第107回日本医学物理学会, Apr. 2014.
108.	Akihiro Haga, 古徳純一, 堀川弥太郎 and 他2名 : Photoelectric cross sections based on the Hatree-Fock calculation, 第107回日本医学物理学会, Apr. 2014.
109.	竹中亮介, 山下英臣, 大森万美, 野元昭弘, Akihiro Haga and 中川恵一 : 直腸癌術前放射線治療期間中の直腸INTERFRACTION MOTIONの解析, 日本放射線腫瘍学会第26回学術大会, Oct. 2013.
110.	野元昭弘, 早乙女直也, Akihiro Haga and 他3名 : STRUCTUREの平均CT値から算出した電子密度をSTRUCTUREに代入した上での線量計算の検討, 日本放射線腫瘍学会第26回学術大会, Oct. 2013.
111.	芝田紫野, 山下英臣, Akihiro Haga and 他4名 : 肺SRTにおけるVMATと固定多門法と原体照射のDVH比較, 日本放射線腫瘍学会第26回学術大会, Oct. 2013.
112.	中川恵一, Akihiro Haga, 木田智士 and 他5名 : 肺がんVMAT-SRTにおける呼吸抑制再現性の検証, 日本放射線腫瘍学会第26回学術大会, Oct. 2013.
113.	Akihiro Haga : 医学物理と放射線治療情報システム, 第106回医学物理学会ランチョンセミナー, Sep. 2013.
114.	今江禄一, 早乙女直也, Akihiro Haga and 他7名 : 回転照射法を用いたWINSTON-LUTZテストおよび幾何学的補正テーブルの取得, 日本放射線技術学会第69回総会学術大会, Apr. 2013.
115.	三枝茂輝, 竹中重治, 白木尚, 矢野敬一, Akihiro Haga, 野元昭弘 and 中川恵一 : 前立腺の回転型強度変調放射線治療(VMAT)における直腸ガスが線量分布に及ぼす影響の評価, 日本放射線技術学会第69回総会学術大会, Apr. 2013.
116.	杉本聡, Akihiro Haga, 山岸隆宏 and 他4名 : 動体追尾リニアックに搭載されたコーンビームCTの4次元再構成の検討, 第105回日本医学物理学会, Apr. 2013.
117.	中野正博, 木田智士, 増谷佳孝, 白木尚, 山本健太郎, 中川恵一 and Akihiro Haga : Time-ordered four dimensional Cone-Beam CT, 第105回日本医学物理学会, Apr. 2013.
118.	早乙女直也, 木田智士, 今江禄一, 作美明, 増谷佳孝, Akihiro Haga and 中川恵一 : THE CHARACTERISTICS OF EPID FOR IN-VIVO DOSIMETRY, 第105回日本医学物理学会, Apr. 2013.
119.	二口将彦, 作美明, Akihiro Haga and 他6名 : THREE-DIMENSIONAL DOSE DISTRIBUTION OF RUTHENIUM 106 BRACHYTHERAPY FOR RETINOBLASTOMA, 第105回日本医学物理学会, Apr. 2013.
120.	Akihiro Haga, 中川恵一, 木田智士 and 他6名 : Registration accuracy for lung tumor verified by using in-treatment 4D cone-beam CT, 第105回日本医学物理学会, Apr. 2013.
121.	Akihiro Haga : 医療従事者が心得ておくべき放射線物理と放射線治療, 医療従事者研修会, Oct. 2012.
122.	伊藤さおり, Akihiro Haga, 福田芳夫 and 他6名 : SPECT-CTを用いた放射性ヨードの線量分布, 第104回日本医学物理学会, Sep. 2012.
123.	Akihiro Haga, 橋本成世, 木田智士 and 他4名 : 治療ビームを用いた画像再構成, 第104回日本医学物理学会, Sep. 2012.
124.	木田智士, 早乙女直也, Akihiro Haga and 他3名 : 放射線治療におけるCone-beam CT の画質改善-コンプトン散乱の影響, 基研主導研究会「原子力・生物学と物理, Sep. 2012.
125.	Akihiro Haga : CBCT再構成の基礎, 医学物理サマーセミナー, Sep. 2012.
126.	今江禄一, Akihiro Haga, 木田智士 and 他7名 : 治療用MV-X線を用いた肺定位放射線治療中の標的の軌跡解析, 日本放射線技術学会第68回総会学術大会, Apr. 2012.
127.	竹中重治, Akihiro Haga, 白木尚 and 他7名 : VMAT(VOLUMETRIC MODULATED ARC THERAPY)におけるモンテカルロ治療計画装置を用いた独立線量検証方法の検討, 日本放射線技術学会第68回総会学術大会, Apr. 2012.
128.	三枝茂輝, Akihiro Haga, 竹中重治 and 他7名 : 連続可変線量率制御システムを新規採用した加速器による回転型強度変調放射線治療(VMAT)の評価, 日本放射線技術学会第68回総会学術大会, Apr. 2012.
129.	岡野由香里, Akihiro Haga, 竹中重治 and 他7名 : VMATを適用した体幹部定位放射線治療の臨床照射LOG FILEを用いて再計算した投与線量分布の解析, 日本放射線技術学会第68回総会学術大会, Apr. 2012.
130.	佐々木克剛, Akihiro Haga, 三枝茂輝 and 他7名 : 治療同時4D-CBCTによるVMAT-体幹部定位放射線治療の治療位置精度評価, 日本放射線技術学会第68回総会学術大会, Apr. 2012.
131.	高橋渉, 山下英臣, 木田智士, 尾上剛士, Akihiro Haga and 中川恵一 : VMATによる肺SBRTにおける治療中同時4D CBCTを用いた検証, 第71回日本医学放射線学会総会, Apr. 2012.
132.	伊藤さおり, Akihiro Haga, 中川恵一 and 他7名 : 甲状腺癌に対する放射性ヨード内用療法における体内線量分布描出手法の検討, 第103回日本医学物理学会, Apr. 2012.
133.	作美明, 白石憲史郎, Akihiro Haga and 他7名 : モンテカルロ手法による125Iシードに対する外照射X線の線量分布の影響, 第103回日本医学物理学会, Apr. 2012.
134.	Akihiro Haga : 強度変調放射線治療-概要と導入までの準備, 第13回医学物理士実務者講習会, Mar. 2012.
135.	木田智士, 早乙女直也, Akihiro Haga, 増谷佳孝 and 中川恵一 : 放射線治療におけるCone-beam CT の画質改善, 電子情報通信学会医用画像研究会, Jan. 2012.
136.	早乙女直也, 木田智士, 尾上剛士, 白石憲史郎, 作美明, 増谷佳孝, Akihiro Haga, 大友邦 and 中川恵一 : Elekta SynergyによるVMAT-CTの開発, 日本放射線腫瘍学会第24回学術大会, Nov. 2011.
137.	Akihiro Haga : 強度変調放射線治療のQA, QC, 放射線治療品質管理講習会, Oct. 2011.
138.	Akihiro Haga : 放射線治療におけるイメージレジストレーションの基本, 日本放射線治療専門放射線技師認定機構講習会, Jan. 2011.
139.	Akihiro Haga : 最新X線治療-経時的最適化に向けて, 「放射線によるがん治療・診断の最前線を探る」シンポジウム, Dec. 2010.
140.	Akihiro Haga, 白石憲史郎, 三枝茂輝 and 中川恵一 : 放射線治療の高度化が臨床に与える影響, 第23回JASTRO学術大会シンポジウム, Oct. 2010.
141.	中川恵一, Akihiro Haga, 山下英臣 and 他5名 : Monaco VMAT for Lung SRT, 第69回日本医学放射線学会総会, Oct. 2010.
142.	Akihiro Haga : IGRTのQA, QC (kV CT), 放射線治療品質管理講習会, Sep. 2010.
143.	木田智士, Akihiro Haga, 松浦妙子 and 他5名 : VMAT(回転型強度変調放射線治療)中の4次元画像再構成技術の開発, 第100回日本医学物理学会, Sep. 2010.
144.	早乙女直也, 木田智士, 松浦妙子, 伊藤さおり, 今江禄一, 田中堅一郎, 古徳純一, Akihiro Haga and 中川恵一 : 治療照射確認のためのlogfileに記録されたMLC位置のEPID画像との比較による検証, 第100回日本医学物理学会, Sep. 2010.
145.	岡野由香里, Akihiro Haga, 白石憲史郎 and 他4名 : Volumetric Modulated Arc Therapyによる前立腺がん治療の検討-治療計画ソフトの比較-, 第67回日本放射線技術学会総会, Apr. 2010.
146.	田中堅一郎, Akihiro Haga, 木田智士 and 他4名 : 呼吸同期装置の呼吸検出能の検証, 第67回日本放射線技術学会総会, Apr. 2010.
147.	三枝茂輝, 折舘隆, 白木尚, 兼子芳浩, 佐々木克剛, 岡野由香里, 田中堅一郎, 今江禄一, 美馬和男, Akihiro Haga, 作美明 and 古徳純一 : 不均質領域における市販治療計画機間の比較, 第67回日本放射線技術学会総会, Apr. 2010.
148.	木田智士, Akihiro Haga, 田中堅一郎 and 他7名 : 呼吸同期装置の呼吸検出応答の検証, 第99回日本医学物理学会, Apr. 2010.
149.	古徳純一, 作美明, Akihiro Haga and 他10名 : 不均質標的内の線量分布に対する市販治療計画装置間の比較・検証 II, 第99回日本医学物理学会, Apr. 2010.
150.	作美明, 古徳純一, Akihiro Haga and 他10名 : 不均質標的内の線量分布に対する市販治療計画装置間の比較・検証 I, 第99回日本医学物理学会, Apr. 2010.
151.	伊藤さおり, Akihiro Haga, 中川恵一 and 他7名 : SPECTとPinnacleを利用した外部照射併用ヨード内用療法の線量評価, 第99回日本医学物理学会, Apr. 2010.
152.	松浦妙子, Akihiro Haga, 木田智士 and 他5名 : 患部の経時的な形態変化追跡に対する Deformable image registration の利用の検討, 第99回日本医学物理学会, Apr. 2010.
153.	Akihiro Haga, 松浦妙子, 伊藤さおり and 他7名 : MatriXXの照射角依存性を考慮に入れたVMAT線量分布の解析, 第99回日本医学物理学会, Apr. 2010.
154.	Akihiro Haga and 中川恵一 : 高精度放射線治療広域展開時代を迎えて, 第21回高精度外部放射線治療研究会シンポジウム, Jan. 2010.
155.	Akihiro Haga, 中川恵一, 白石憲史郎 and 他7名 : VMAT線量検証における誤差要因の評価, 日本放射線腫瘍学会第22回学術大会, Sep. 2009.
156.	三枝茂輝, Akihiro Haga, 折舘隆 and 他7名 : Volumetric Modulated Arc Therapy検証のためのradiochromic filmの評価, 第65回日本放射線技術学会総会, Apr. 2009.
157.	佐々木克剛, Akihiro Haga, 白木尚 and 他7名 : MLCを併用したRod CompensatorによるIMRT, 第65回日本放射線技術学会総会, Apr. 2009.
158.	田中堅一郎, Akihiro Haga, 今江禄一 and 他6名 : Pinnacle³とSynergyシステムにおけるStep and Shoot IMRT計画時の問題, 第65回日本放射線技術学会総会, Apr. 2009.
159.	岡野由香里, Akihiro Haga, 田中堅一郎 and 他4名 : VMATによる前立腺がん治療の検討, 第65回日本放射線技術学会総会, Apr. 2009.
160.	Akihiro Haga, 中川恵一, 白石憲史郎 and 他6名 : VMATの物理検証, 第98回日本医学物理学会, Apr. 2009.
161.	Akihiro Haga and 中川恵一 : 強度変調回転照射の特徴と課題, 第19回高精度外部放射線治療研究会シンポジウム, Jan. 2009.
162.	Akihiro Haga, 中川恵一, 白石憲史郎 and 他6名 : VMATの物理検証, 日本放射線腫瘍学会第21回学術大会, Oct. 2008.
163.	Akihiro Haga, 寺原敦朗, 中川恵一 and 他4名 : 頭頸部IMRTとMLCのキャリブレーション, 日本放射線腫瘍学会第21回学術大会, Oct. 2008.
164.	Akihiro Haga : クライアント設定ティップス, 第2回JLDG構築運用検討会, Sep. 2007.
165.	Akihiro Haga, 土岐博, 為永節雄, 小川洋子 and 堀川弥太郎 : 相対論的原子核模型と核子の有効質量, 日本物理学会, Sep. 2007.
166.	Akihiro Haga, 堀川弥太郎 and 土岐博 : Reanalysis of transition energy in muonic heavy atoms, 日本物理学会, Mar. 2007.
167.	Akihiro Haga, 土岐博, 為永節雄, 小川洋子 and 堀川弥太郎 : Dirac-Brueckner-Hartree-Fock theory with vacuum polarization, 少数核子系とバリオン間相互作用, Mar. 2007.
168.	Akihiro Haga, 土岐博, 為永節雄 and 堀川弥太郎 : Nuclear structure in relativistic nuclear models, 核子多体系におけるクラスター現象, Feb. 2007.
169.	Akihiro Haga, 土岐博, 為永節雄 and 堀川弥太郎 : Gamow-Teller and M1 resonances in relativistic nuclear model, 日本物理学会, Sep. 2006.
170.	Akihiro Haga, 土岐博, 為永節雄, 堀川弥太郎 and HL. Yadav : Self-consistent RPA with vacuum polarization, 日本物理学会, Mar. 2006.
171.	Akihiro Haga : 原子核に束縛されたミューオンスピンによる核分極依存性, 原子核・ハドロン物理におけるスピンの魅力, Jan. 2006.
172.	Akihiro Haga, 為永節雄, 土岐博 and 堀川弥太郎 : Relativistic Hartree approach with exact treatment of vacuum polarization in finite nuclei, 日本物理学会, Sep. 2004.
173.	Akihiro Haga, 堀川弥太郎, 田中靖敏 and 土岐博 : Role of Anti-nucleon states in nuclear polarization, 日本物理学会, Mar. 2004.
174.	Akihiro Haga : Vacuum polarization in finite nuclei, 核力と構造研究会, Mar. 2004.
175.	Akihiro Haga, 堀川弥太郎, 田中靖敏 and 土岐博 : エキゾチック原子準位における核分極効果, 大阪大学COE冬の学校2004, Feb. 2004.
176.	Akihiro Haga, 堀川弥太郎 and 田中靖敏 : Muonic and anti-nucleonic atoms, エキゾチックアトムの生成と構造, Oct. 2003.
177.	Akihiro Haga : 反陽子原子と反核子状態, 日本物理学会, Mar. 2003.
178.	Akihiro Haga, 堀川弥太郎 and 田中靖敏 : 電子およびミューオン²⁰⁸Pbにおける核分極補正, 日本物理学会, Mar. 2002.
179.	Akihiro Haga, 田中靖敏 and 堀川弥太郎 : Relativistic RPAを用いた核分極補正計算, 原子核における集団運動の微視的記述研究会, Dec. 2001.
180.	Akihiro Haga, 田中靖敏 and 堀川弥太郎 : 水素様イオンとミューオン原子における核分極補正, 宇宙空間原子分子過程研究会, Dec. 2000.
181.	Akihiro Haga, 田中靖敏 and 堀川弥太郎 : 81価鉛原子の電子準位に対する核分極補正, 日本物理学会, Sep. 2000.
182.	Akihiro Haga and 吉田忠義 : 気-水界面上を横方向に流れるプロトン電導, 日本生物物理学会, Oct. 1997.
183.	Akihiro Haga and 吉田忠義 : 気-水界面に生じる等電位面, 日本中部化学連合会, Apr. 1996.

Et cetera, Workshop:

1.	Yoshihide Sato, Dousatsu Sakata, David Bolst, Edward Simpson, Susanna Guatelli and Akihiro Haga : Development and implementation of a new Geant4 QMD model and its validation, Geant4 Hadronic Physics Working Group Meeting, Aug. 2023.
2.	Shimomura Taisei and Akihiro Haga : 第5回レディオミクス研究会:@WEB開催, レディオミクス研究会(WEB開催), Jul. 2020.
3.	Akihiro Haga : Imaging physics: Standardization of medical image, Medical Physics Forum (WEB), Mar. 2020.
4.	Akihiro Haga : 第四回レディオミクス研究会:@東北大, レディオミクス研究会(東北大学星陵キャンパス), Oct. 2019.
5.	Akihiro Haga : レディオミクス(Radiomics):医用画像を用いた病期・病理・予後予測の可能性, 第2回ビッグデータ・AI研究会(日本消化器学会), Jun. 2019.
6.	Akihiro Haga : レディオミクス概論:特徴量抽出と深層学習, 日本放射線技術学会中国四国支部2019年度支部セミナー, Jun. 2019.
7.	Akihiro Haga : Invivo dosimetry: 放射線治療中のデータを用いた線量再構成, 第10回四国放射線治療ネットワークセミナー, Jun. 2019.
8.	Akihiro Haga : レディオミクス(Radiomics)と画像の表現, 第38回徳島CT研究会, Jun. 2019.
9.	Akihiro Haga : 第三回レディオミクス研究会:徳島大, レディオミクス研究会(中国・四国高度がんプロ養成基盤プログラム), Mar. 2019.
10.	Akihiro Haga : ヘリカル照射型直線加速機の課題と今後の展望について, ヘリカル照射型直線加速機における放射線治療計画セミナー(中国・四国高度がんプロ養成基盤プログラム), Feb. 2018.
11.	Akihiro Haga : 治療装置の違いにおける線量分布及び治療成績の比較, ヘリカル照射型直線加速機における放射線治療計画セミナー(中国・四国高度がんプロ養成基盤プログラム), Feb. 2018.
12.	Akihiro Haga : X線治療関連:VMAT治療中に撮像されたコーンビームCTの利用, BIOClinica, No.7, 1-6, Jun. 2017.
13.	Akihiro Haga : DVHを用いた患者QAとQA簡素化(log fileの利用), JASTROニュースレター, No.124, 16-17, Jun. 2017.
14.	Akihiro Haga : 高精度放射線治療, がんプロ「がん治療のブレイクスルーを担う医療人育成」成果報告書, 26-33, Jan. 2017.
15.	Akihiro Haga : VMATとその有用性:臨床応用の観点から, 医学物理:解説, Vol.36, No.3, 161-165, Dec. 2016.
16.	Akihiro Haga : エレクタ製研究リニアックにおけるFFFの臨床応用, 第5回東北医療技術学会:ランチョンセミナー解説, 94-95, May 2016.
17.	Akihiro Haga : 機械学習が拓く医学物理の新たな世界, 医学物理:巻頭言, Vol.36, No.1, May 2016.
18.	Akihiro Haga : CT再構成ノート改訂版, 日本医学物理学会サマーセミナー, 1-19, Sep. 2014.
19.	Akihiro Haga : 放射線治療におけるチーム医療-医学物理士の立場から, JASTROニュースレター, No.107, 16-17, Sep. 2013.
20.	中川恵一, Akihiro Haga and 他 : 病院と患者のための放射線治療, 月刊新医療, No.1, 1-184, Jan. 2012.
21.	Akihiro Haga and 中川恵一 : 高精度放射線治療の普及-海外と比較した我が国の現状と課題, インナービジョン, No.3, 6-7, Mar. 2011.
22.	Akihiro Haga and 中川恵一 : 強度変調放射線治療(VMAT), インナービジョン, No.3, 55-59, Mar. 2009.
23.	Akihiro Haga : 純粋物理からの転身-医学物理の面白さ-, 日本放射線科専門医会・医会JCRニュース特集, 1-4, Nov. 2008.
24.	Akihiro Haga : 東京大学における医学物理士と医学物理教育, Rad Fan, Vol.6, No.7, 58-59, Jul. 2008.
25.	Akihiro Haga and 中川恵一 : 医学物理士の国家資格制度創設を, 原子力eye, Vol.54, No.10, 21-23, Jul. 2008.
26.	Akihiro Haga and 堀川弥太郎 : 水素様重イオンの精密分光と核分極効果, 日本物理学会誌「最近の研究から」, Vol.60, No.5, 368-372, May 2005.

Patent:

1.	Akihiro Haga, Taiki Magome and Keiichi Nakagawa : 画像処理装置および画像処理方法, 2017-556094 (Dec. 2016), 2017-556094 (Dec. 2016), 6803077 (Dec. 2020).

Grants-in-Aid for Scientific Research (KAKEN Grants Database @ NII.ac.jp)

Novel Cancer Treatment Strategies Based on Radiomics Imaging Analysis (Project/Area Number: 23K07158 )
Development of medical computer vision system and the clinical application (Project/Area Number: 23K07084 )
Molecular Imaging Radiomics Model for Prediction of Radiotherapy Effect (Project/Area Number: 20K07698 )
Material estimation and functional estimation with medical image normalization (Project/Area Number: 19K08201 )
Proposal of efficient specific health guidance operation based on the combined receipt and specific medical checkup big data of Osaka Prefecture (Project/Area Number: 19H03871 )
Feature value analysis of delivered dose distribution contributing to individualized radiotherapy (Project/Area Number: 18K07667 )
Real-time dose estimation for IR (Project/Area Number: 18K07646 )
Patient setup system using a virtual X-ray image (Project/Area Number: 15K19212 )
Volumetric modulated arc therapy with flattening filter free techniques for stereotactic body radiotherapy (Project/Area Number: 15K19201 )
Prediction of respiratory motion using state space models (Project/Area Number: 15K08703 )
Prediction of tumor control and side effects using in-treatment dose distribution in stereotactic body radiotherapy (Project/Area Number: 15K08692 )
Development of multi-energy CT reconstruction algorithm for material decomposition (Project/Area Number: 15K08691 )
Improvement of image quality using estimated filters and physical filters in image-guided radiation therapy (Project/Area Number: 25461907 )
Development of Hybrid and Dual Energy CT System in Radiation Therapy Device (Project/Area Number: 24689048 )
Four-dimensional dose distribution measurement using plastic scintillator (Project/Area Number: 24601017 )
Dose distribution during stereotactic body radiotherapy using volumetric modulated arc therapy (Project/Area Number: 24601005 )
Development of four-dimensional CT acquisition and tumor detection system during volumetric modulated arc therapy. (Project/Area Number: 23390298 )
Stereotactic body radiotherapy using volumetric modulated arc therapy in consideration of respiratory motion (Project/Area Number: 22791177 )
Dose calculation system using in-treatment cone-beam CT for adaptive radiation therapy (Project/Area Number: 22791176 )
Study about Improvement of Reliability on Maintenance TechnologyBased on NPP Condition and Medical Application Using Time Series Analysis (Project/Area Number: 22560826 )
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Professor : Haga, Akihiro

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Grants-in-Aid for Scientific Research (KAKEN Grants Database @ NII.ac.jp)