Pixel-by-pixel correction of beam hardening artifacts by bowtie filter in fan-beam CT


Development of numerical phantom converting from electron microscopic analysis to multi-component water fraction for MRI simulator.


Determination of Alzheimer's disease based on morphology and atrophy using machine learning combined with automated segmentation

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.


Simplified assessment for chemical exchanged saturation transfer (CEST) imaging: local offset frequency and CEST effect

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.


A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma.

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.


Quantum annealing-based computed tomography using variational approach for a real-number image reconstruction


Differences of white matter structure for diffusion kurtosis imaging using voxel-based morphometry and connectivity analysis


Conversion Map from Quantitative Parameter Mapping to Myelin Water Fraction: Comparison with R1·R2* and Myelin Water Fraction in White Matter.

Magnetic Resonance Materials in Physics, Biology and Medicine


Virtual cone-beam computed tomography simulator with human phantom library and its application to the elemental material decomposition

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.


Artificial intelligence-assisted interpretation of systolic function by echocardiogram

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.


X-ray energy spectrum estimation based on a virtual computed tomography system

Biomedical Physics & Engineering Express


Myelin Water Atlas Template Derived from Quantitative Parameter Mapping


Myelin-weighted imaging derived from quantitative parameter mapping

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.


Development of a more accurate Geant4 quantum molecular dynamics model for hadron therapy

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.


Gradient Boosting Decision Tree Becomes More Reliable Than Logistic Regression in Predicting Probability for Diabetes With Big Data

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.


Virtual computed-tomography system for deep-learning-based material decomposition

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.


Determination of white matter structure index for voxel basedmorphometry and connectivity analysis.


Development of a B1 correction method without additional scanning VFA T1 map.


Training of deep cross-modality conversion models with a small dataset, and their application in megavoltage CT to kilovoltage CT conversion

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?"


Development of self-calibrating B1 correction for three-dimensional variable flip angle T1 mapping


Conversion map from quantitative parameter mapping to myelin water fraction


Determination of Alzheimer's disease based on morphology and atrophy using machine learning combined with automated segmentation.


Radiomics analysis of [18F]-fluoro-2-deoxyglucose positron emission tomography for the prediction of cervical lymph node metastasis in tongue squamous cell carcinoma

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.


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


Influence of distant scatterer on air kerma measurement in the evaluation of diagnostic X-rays using Monte Carlo simulation

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.


Physical density estimations of single- and dual-energy CT using material-based forward projection algorithm: a simulation study.

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.


Automatic Contour Segmentation of Cervical Cancer using Artificial Intelligence

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.


Deep Learning Analysis of Echocardiographic Images to Predict Positive Genotype in Patients With Hypertrophic Cardiomyopathy.

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.


kV-kV and kV-MV DECT based estimation of proton stopping power ratio - a simulation study

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.


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


Derivation of Water Exchange Constants between Components using Quantitative Parameter Mapping (QPM)


Computed tomography image representation using the Legendre polynomial and spherical harmonics functions

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.


Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups

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.


Retrospective dose reconstruction of prostate stereotactic body radiotherapy using conebeam CT and a log file during VMAT delivery with flatteningfilterfree mode

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.


A Deep Learning Approach for Assessment of Regional Wall Motion Abnormality From Echocardiographic Images

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.


Clinically Feasible and Accurate View Classification of Echocardiographic Images Using Deep Learning

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.


Deep Learning for Assessment of Left Ventricular Ejection Fraction from Echocardiographic Images

Journal of the American Society of Echocardiography


エコーレディオミクス:超音波画像を用いた心エコー解析

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.

ディープラーニングをはじめとする近年の画像解析法の進展に伴い，心臓超音波(心エコー)解析も新たなフェーズに到達しつつある．心エコー画像から心臓疾患の存在診断・鑑別診断・機能診断を行う試みが，近年，活発になされるようになった．本稿ではエコー画像を用いた心機能解析，特に収縮性を示す代表的な指標である左室駆出率(LVEF)の定量解析を紹介する．従来，LVEFの算出のために心臓の内壁をトレースすることが行われるが，多角的エコー動画像からディープニューラルネットワークによって直接LVEFを精度高く求めることができる．また，画像解析で重要となる前処理についてわれわれが行っている手順を紹介するとともに，その前処理で必要となるエコーの撮影断面の自動識別に関する研究を紹介する．


Fast Statistical Iterative Reconstruction for Mega-voltage Computed Tomography

The Journal of Medical Investigation : JMI


Improvement of the robustness to set up error by a virtual bolus in total scalp irradiation with Helical TomoTherapy

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.


Acceptable fetal dose using flattening filter-free volumetric arc therapy (FFF VMAT) in postoperative chemoradiotherapy of tongue cancer during pregnancy,

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.

Clinical and Translational Radiation Oncology


Radiomics Analysis for Glioma Malignancy Evaluation Using Diffusion Kurtosis and Tensor Imaging

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.

International Journal of Radiation Oncology*Biology*Physics


Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy

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.


Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis

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.


Utilization of Artificial Intelligence in Echocardiography

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.


Proceedings of the 27th Annual Meeting of ISMRM


Myelin imaging derived from quantitative parameter mapping


Standardization of imaging features for radiomics analysis

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.

The Journal of Medical Investigation : JMI


Development of a markerless tumor-tracking algorithm using prior four-dimensional cone-beam computed tomography

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.


Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network

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.


Stereotactic body radiotherapy for centrallylocated lung tumors with 56 Gy in seven fractions: A retrospective study

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.


Flattening filter-free technique in volumetric modulated arc therapy for lung stereotactic body radiotherapy: A clinical comparison with the flattening filter technique

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.


Classification of early stage non-small cell lung cancers on computed tomographic images into histological types using radiomic features: interobserver delineation variability analysis

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.


Cone-beam CT reconstruction for non-periodic organ motion using time-ordered chain graph model


Effective atomic number estimation using kV-MV dual-energy source in LINAC


Evaluation of a commercial automatic treatment planning system for prostate cancers


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


Adequate target volume in total-body irradiation by intensity-modulated radiation therapy using helical tomotherapy: a simulation study

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.


A rapid imaging method for total body or marrow irradiation in helical tomotherapy

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.

International Journal of Radiation Oncology*Biology*Physics


Evaluation of Functional Marrow Irradiation Based on Skeletal Marrow Composition Obtained Using Dual-Energy Computed Tomography

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.


Evaluation of a Post-analysis Method for Cumulative Dose Distribution in Stereotactic Body Radiotherapy

肺定位放射線治療における積算線量分布の事後評価法の検討

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.

Japanese Journal of Radiological Technology


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

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.


Dynamic Image Prediction Using Principal Component and Multi-Channel Singular Spectral Analysis: A Feasibility Study


Patterns of Recurrence in Malignant Glioma Patients: Association with Subventricular Zone and Radiotherapy Dose


Improvement of Kilo-voltage Cone Beam CT Image Quality with Iterative Optimized Scatter Correction Algorithm

逐次最適化散乱補正アルゴリズムを用いたkilo-voltage cone beam CTの画質改善

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.

本研究では，kilo-voltage cone beam computed tomography(kV CBCT)の画質改善に向けて，散乱補正と統計的逐次近似画像再構成法を組み合わせた逐次最適化散乱補正アルゴリズムを構築した．散乱補正においては，鉛製コリメータを用いた実験による散乱成分の測定と，Klein-Nishinaの散乱公式に基づく解析的な散乱シミュレーションを相補的に組み合わせることにより，簡便かつ高精度に二次元検出器上での散乱分布を推定する手法を提案した．画像再構成には，統計ノイズ抑制のために，統計的逐次近似画像再構成法(Convex法)を用いた．この画像再構成と散乱補正を繰り返し計算の枠組みの中に並列的に組み込んだ，逐次最適化散乱補正アルゴリズムを構築した．円柱水ファントム再構成画像の減衰係数値を解析することにより，本手法の妥当性を評価した．


Analysis of motion of the rectum during preoperative intensity modulated radiation therapy for rectal cancer using cone-beam computed tomography

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.


Volumetric modulated arc therapy for lung stereotactic radiation therapy can achieve high local control rates

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.


Dose Reconstruction Using Respiratory Signals and Machine Parameters during Treatment in Stereotactic Body Radiotherapy

肺定位放射線治療中の呼吸信号および照射制御パラメータを用いた線量分布の再構成

Purpose: 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). Methods: 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. Results and discussion: 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. Conclusion: 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.


Reconstruction of the treatment area by use of sinogram in helical tomotherapy


Stereotactic Body Radiotherapy for Small Lung Tumors in the University of Tokyo Hospital

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.


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

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.


Winston-Lutz Test and Acquisition of Flexmap Using Rotational Irradiation

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

Purpose: 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. Method: 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. Results and discussion: 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. Conclusion: 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.

Japanese Journal of Radiological Technology


Independent Absorbed Dose Calculation Using the Monte Carlo Algorithm in Volumetric Modulated Arc Therapy

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.


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


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

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.

International Journal of Radiation Oncology*Biology*Physics


Dose verification of volumetric modulated arc therapy (VMAT) by use of in-treatment linac parameters

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.


Comparison of total MU and segment areas in VMAT and step-and-shoot IMRT plans

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.


Motion Analysis of Target during Stereotactic Radiotherapy of Lung Tumors

肺定位放射線治療中における標的の移動量の評価

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.

回転型強度変調放射線治療(volumetric modulated arc therapy, VMAT)は，診断用kV-X線および治療用MV-X線の投影画像からコーンビームCT(cone beam CT, CBCT)画像を再構成することが可能であり，治療中の臓器の位置を同定することが期待されている．本研究ではVMATを肺定位放射線治療に適用し，診断用kV-X線および治療用MV-X線を用いて治療中の標的の位置検出と移動量を評価することを目的とした．臨床例を対象としてVMATによる治療計画を行い，照射中に対象を透過した診断用kV-X線および治療用MV-X線による投影画像を収集し，CBCT画像の再構成を行った．再構成画像から標的の位置検出および移動量を評価した．臨床例において治療中の標的の移動量の評価は可能であり，同一患者でも標的の移動量の日々の変化があることが確認できた．VMATは今までガントリ固定の照射法で同定が困難であった治療中の対象内の構造を簡便かつ低侵襲に評価可能であった．


Four-dimensional measurement of the displacement of metal clips or postoperative surgical staples during 320-multislice computed tomography scanning of gastric cancer

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.


4D registration and 4D verification of lung tumor position for stereotactic volumetric modulated arc therapy using respiratory- correlated cone-beam CT

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.


Four-Dimensional Measurement of the Displacement of Internal Fiducial and Skin Markers During 320-Multislice Computed Tomography Scanning of Breast Cancer

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.


In-treatment 4D cone-beam CT with image-based respiratory phase recognition

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.


Evaluation of heterogeneity dose distributions for Stereotactic Radiotherapy (SRT): Comparison of commercially available Monte Carlo dose calculation with other algorithms

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.