start-ver=1.4 cd-journal=joma no-vol=16 cd-vols= no-issue=4 article-no= start-page=497 end-page=505 dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20230915 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Investigation of uncertainty in internal target volume definition for lung stereotactic body radiotherapy en-subtitle= kn-subtitle= en-abstract= kn-abstract=This study evaluated the validity of internal target volumes (ITVs) defined by three- (3DCT) and four-dimensional computed tomography (4DCT), and subsequently compared them with actual movements during treatment. Five patients with upper lobe lung tumors were treated with stereotactic body radiotherapy (SBRT) at 48 Gy in four fractions. Planning 3DCT images were acquired with peak-exhale and peak-inhale breath-holds, and 4DCT images were acquired in the cine mode under free breathing. Cine images were acquired using an electronic portal imaging device during irradiation. Tumor coverage was evaluated based on the manner in which the peak-to-peak breathing amplitude on the planning CT covered the range of tumor motion (± 3 SD) during irradiation in the left–right, anteroposterior, and cranio-caudal (CC) directions. The mean tumor coverage of the 4DCT-based ITV was better than that of the 3DCT-based ITV in the CC direction. The internal margin should be considered when setting the irradiation field for 4DCT. The proposed 4DCT-based ITV can be used as an efficient approach in free-breathing SBRT for upper-lobe tumors of the lung because its coverage is superior to that of 3DCT. en-copyright= kn-copyright= en-aut-name=NakanishiDaiki en-aut-sei=Nakanishi en-aut-mei=Daiki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=FukunagaJun-Ichi en-aut-sei=Fukunaga en-aut-mei=Jun-Ichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=HiroseTaka-Aki en-aut-sei=Hirose en-aut-mei=Taka-Aki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=YoshitakeTadamasa en-aut-sei=Yoshitake en-aut-mei=Tadamasa kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=SasakiMotoharu en-aut-sei=Sasaki en-aut-mei=Motoharu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=3 en-affil=Division of Radiology, Department of Medical Technology, Kyushu University Hospital kn-affil= affil-num=4 en-affil=Division of Radiology, Department of Medical Technology, Kyushu University Hospital kn-affil= affil-num=5 en-affil=Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University kn-affil= affil-num=6 en-affil=Graduate School of Biomedical Sciences, Tokushima University kn-affil= en-keyword=4DCT kn-keyword=4DCT en-keyword=3DCT kn-keyword=3DCT en-keyword=Internal target volume kn-keyword=Internal target volume en-keyword=EPID imaging kn-keyword=EPID imaging en-keyword=Stereotactic body radiotherapy kn-keyword=Stereotactic body radiotherapy en-keyword=Lung cancer kn-keyword=Lung cancer END start-ver=1.4 cd-journal=joma no-vol=13 cd-vols= no-issue=6 article-no= start-page=1783 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2024 dt-pub=20240320 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Enhancing Diagnostic Precision: Evaluation of Preprocessing Filters in Simple Diffusion Kurtosis Imaging for Head and Neck Tumors en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background: Our initial clinical study using simple diffusion kurtosis imaging (SDI), which simultaneously produces a diffusion kurtosis image (DKI) and an apparent diffusion coefficient map, confirmed the usefulness of SDI for tumor diagnosis. However, the obtained DKI had noticeable variability in the mean kurtosis (MK) values, which is inherent to SDI. We aimed to improve this variability in SDI by preprocessing with three different filters (Gaussian [G], median [M], and nonlocal mean) of the diffusion-weighted images used for SDI. Methods: The usefulness of filter parameters for diagnosis was examined in basic and clinical studies involving 13 patients with head and neck tumors. Results: The filter parameters, which did not change the median MK value, but reduced the variability and significantly homogenized the MK values in tumor and normal tissues in both basic and clinical studies, were identified. In the receiver operating characteristic curve analysis for distinguishing tumors from normal tissues using MK values, the area under curve values significantly improved from 0.627 without filters to 0.641 with G (sigma = 0.5) and 0.638 with M (radius = 0.5). Conclusions: Thus, image pretreatment with G and M for SDI was shown to be useful for improving tumor diagnosis in clinical practice. en-copyright= kn-copyright= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=ShimizuYudai en-aut-sei=Shimizu en-aut-mei=Yudai kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=YoshimuraYuuki en-aut-sei=Yoshimura en-aut-mei=Yuuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=YoshidaSuzuka en-aut-sei=Yoshida en-aut-mei=Suzuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=NakamuraYoshihide en-aut-sei=Nakamura en-aut-mei=Yoshihide kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=FukumuraYuka en-aut-sei=Fukumura en-aut-mei=Yuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=Al-HammadWlla E. en-aut-sei=Al-Hammad en-aut-mei=Wlla E. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=BarhamMajd en-aut-sei=Barham en-aut-mei=Majd kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=TekikiNouha en-aut-sei=Tekiki en-aut-mei=Nouha kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= en-aut-name=AsaumiJunichi en-aut-sei=Asaumi en-aut-mei=Junichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=17 ORCID= affil-num=1 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=2 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=4 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=5 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=7 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=8 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=9 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=11 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=12 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=13 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=14 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=15 en-affil=Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University kn-affil= affil-num=16 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=17 en-affil=Department of Oral and Maxillofacial Radiology, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= en-keyword=diffusion-weighted image kn-keyword=diffusion-weighted image en-keyword=Gaussian filter kn-keyword=Gaussian filter en-keyword=head and neck tumor kn-keyword=head and neck tumor en-keyword=magnetic resonance imaging kn-keyword=magnetic resonance imaging en-keyword=mean kurtosis kn-keyword=mean kurtosis en-keyword=median filter kn-keyword=median filter en-keyword=nonlocal mean filter kn-keyword=nonlocal mean filter en-keyword=phantom kn-keyword=phantom en-keyword=simple diffusion kurtosis imaging kn-keyword=simple diffusion kurtosis imaging en-keyword=restricted diffusion-weighted image kn-keyword=restricted diffusion-weighted image END start-ver=1.4 cd-journal=joma no-vol=26 cd-vols= no-issue=5 article-no= start-page=536 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20231002 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Evaluation of the accuracy of heart dose prediction by machine learning for selecting patients not requiring deep inspiration breath‑hold radiotherapy after breast cancer surgery en-subtitle= kn-subtitle= en-abstract= kn-abstract=Increased heart dose during postoperative radiotherapy (RT) for left‑sided breast cancer (BC) can cause cardiac injury, which can decrease patient survival. The deep inspiration breath‑hold technique (DIBH) is becoming increasingly common for reducing the mean heart dose (MHD) in patients with left‑sided BC. However, treatment planning and DIBH for RT are laborious, time‑consuming and costly for patients and RT staff. In addition, the proportion of patients with left BC with low MHD is considerably higher among Asian women, mainly due to their smaller breast volume compared with that in Western countries. The present study aimed to determine the optimal machine learning (ML) model for predicting the MHD after RT to pre‑select patients with low MHD who will not require DIBH prior to RT planning. In total, 562 patients with BC who received postoperative RT were randomly divided into the trainval (n=449) and external (n=113) test datasets for ML using Python (version 3.8). Imbalanced data were corrected using synthetic minority oversampling with Gaussian noise. Specifically, right‑left, tumor site, chest wall thickness, irradiation method, body mass index and separation were the six explanatory variables used for ML, with four supervised ML algorithms used. Using the optimal value of hyperparameter tuning with root mean squared error (RMSE) as an indicator for the internal test data, the model yielding the best F2 score evaluation was selected for final validation using the external test data. The predictive ability of MHD for true MHD after RT was the highest among all algorithms for the deep neural network, with a RMSE of 77.4, F2 score of 0.80 and area under the curve‑receiver operating characteristic of 0.88, for a cut‑off value of 300 cGy. The present study suggested that ML can be used to pre‑select female Asian patients with low MHD who do not require DIBH for the postoperative RT of BC. en-copyright= kn-copyright= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=Al‑HammadWlla en-aut-sei=Al‑Hammad en-aut-mei=Wlla kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=TekikiNouha en-aut-sei=Tekiki en-aut-mei=Nouha kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=IshizakaHinata en-aut-sei=Ishizaka en-aut-mei=Hinata kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=BarhamMajd en-aut-sei=Barham en-aut-mei=Majd kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=HiranoMasaki en-aut-sei=Hirano en-aut-mei=Masaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=MutoYuki en-aut-sei=Muto en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=IharaHiroki en-aut-sei=Ihara en-aut-mei=Hiroki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=SugiyamaSoichi en-aut-sei=Sugiyama en-aut-mei=Soichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= affil-num=1 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=2 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=4 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=5 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=7 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=8 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=9 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An‑Najah National University kn-affil= affil-num=11 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=12 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=13 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=14 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=15 en-affil=Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=16 en-affil=Department of Proton Beam Therapy, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= en-keyword=BC kn-keyword=BC en-keyword=RT kn-keyword=RT en-keyword=heart dose kn-keyword=heart dose en-keyword=ML kn-keyword=ML en-keyword=DNN kn-keyword=DNN en-keyword=DIBH kn-keyword=DIBH END start-ver=1.4 cd-journal=joma no-vol=9 cd-vols= no-issue=8 article-no= start-page=e19038 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=202308 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Bayesian statistical modeling to predict observer-specific optimal windowing parameters in magnetic resonance imaging en-subtitle= kn-subtitle= en-abstract= kn-abstract=Magnetic resonance (MR) images require a process known as windowing for optimizing the display conditions. However, the conventional windowing process often fails to achieve the preferred display conditions for observers due to various factors. This study proposes a novel framework for predicting the preferred windowing parameters for each observer using Bayesian statistical modeling. MR images obtained from 1000 patients were divided into training and test sets at a 7:3 ratio. The image intensity and windowing parameters were standardized using previously reported methods. Bayesian statistical modeling was utilized to predict the windowing parameters preferred by three MR imaging (MRI) operators. The performance of the proposed framework was evaluated by assessing the mean relative error (MRE), mean absolute error (MAE), and Pearson's correlation coefficient (ρ) of the test set. In addition, the naive method, which presumes that the average value of the windowing parameters for each acquisition sequence and body region in the training set is optimal, was also used for comparison. Three MRI operators and three radiologists conducted visual assessments. The mean MRE, MAE, and ρ values for the window level and width (WL/WW) in the proposed framework were 12.6 and 13.9, 42.9 and 85.4, and 0.98 and 0.98, respectively. These results outperformed those obtained using the naive method. The visual assessments revealed no significant differences between the original and predicted display conditions, indicating that the proposed framework accurately predicts individualized windowing parameters with the additional advantages of robustness and ease of use. Thus, the proposed framework can effectively predict the windowing parameters preferred by each observer. en-copyright= kn-copyright= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= affil-num=1 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=2 en-affil=Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=3 en-affil=Department of Radiological Technology, Faculty of Health Sciences, Okayama University kn-affil= en-keyword=MR image kn-keyword=MR image en-keyword=Image intensity standardization kn-keyword=Image intensity standardization en-keyword=Windowing kn-keyword=Windowing en-keyword=Prediction kn-keyword=Prediction en-keyword=Bayesian statistical modeling kn-keyword=Bayesian statistical modeling END start-ver=1.4 cd-journal=joma no-vol=30 cd-vols= no-issue=8 article-no= start-page=7412 end-page=7424 dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20230804 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Mean Heart Dose Prediction Using Parameters of Single-Slice Computed Tomography and Body Mass Index: Machine Learning Approach for Radiotherapy of Left-Sided Breast Cancer of Asian Patients en-subtitle= kn-subtitle= en-abstract= kn-abstract=Deep inspiration breath-hold (DIBH) is an excellent technique to reduce the incidental radiation received by the heart during radiotherapy in patients with breast cancer. However, DIBH is costly and time-consuming for patients and radiotherapy staff. In Asian countries, the use of DIBH is restricted due to the limited number of patients with a high mean heart dose (MHD) and the shortage of radiotherapy personnel and equipment compared to that in the USA. This study aimed to develop, evaluate, and compare the performance of ten machine learning algorithms for predicting MHD using a patient's body mass index and single-slice CT parameters to identify patients who may not require DIBH. Machine learning models were built and tested using a dataset containing 207 patients with left-sided breast cancer who were treated with field-in-field radiotherapy with free breathing. The average MHD was 251 cGy. Stratified repeated four-fold cross-validation was used to build models using 165 training data. The models were compared internally using their average performance metrics: F2 score, AUC, recall, accuracy, Cohen's kappa, and Matthews correlation coefficient. The final performance evaluation for each model was further externally analyzed using 42 unseen test data. The performance of each model was evaluated as a binary classifier by setting the cut-off value of MHD & GE; 300 cGy. The deep neural network (DNN) achieved the highest F2 score (78.9%). Most models successfully classified all patients with high MHD as true positive. This study indicates that the ten models, especially the DNN, might have the potential to identify patients who may not require DIBH. en-copyright= kn-copyright= en-aut-name=Al-HammadWlla E. en-aut-sei=Al-Hammad en-aut-mei=Wlla E. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=TekikiNouha en-aut-sei=Tekiki en-aut-mei=Nouha kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=IshizakaHinata en-aut-sei=Ishizaka en-aut-mei=Hinata kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=BarhamMajd en-aut-sei=Barham en-aut-mei=Majd kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=ShimizuYudai en-aut-sei=Shimizu en-aut-mei=Yudai kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=AsaumiJunichi en-aut-sei=Asaumi en-aut-mei=Junichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= affil-num=1 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=2 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=4 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=5 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=7 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=8 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=9 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Department of Dentistry and Dental Surgery, College of Medicine and Health Sciences, An-Najah National University kn-affil= affil-num=11 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=12 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=13 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=14 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= en-keyword=breast cancer kn-keyword=breast cancer en-keyword=radiotherapy kn-keyword=radiotherapy en-keyword=heart dose kn-keyword=heart dose en-keyword=machine learning kn-keyword=machine learning en-keyword=deep neural network kn-keyword=deep neural network en-keyword=deep inspiration breath-hold technique kn-keyword=deep inspiration breath-hold technique en-keyword=computed tomography kn-keyword=computed tomography END start-ver=1.4 cd-journal=joma no-vol=77 cd-vols= no-issue=3 article-no= start-page=273 end-page=280 dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=202306 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Usefulness of Simple Diffusion Kurtosis Imaging for Head and Neck Tumors: An Early Clinical Study en-subtitle= kn-subtitle= en-abstract= kn-abstract=Diffusion kurtosis (DK) imaging (DKI), a type of restricted diffusion-weighted imaging, has been reported to be useful for tumor diagnoses in clinical studies. We developed a software program to simultaneously create DK images with apparent diffusion coefficient (ADC) maps and conducted an initial clinical study. Multi-shot echo-planar diffusion-weighted images were obtained at b-values of 0, 400, and 800 sec/mm2 for simple DKI, and DK images were created simultaneously with the ADC map. The usefulness of the DK image and ADC map was evaluated using a pixel analysis of all pixels and a median analysis of the pixels of each case. Tumor and normal tissues differed significantly in both pixel and median analyses. In the pixel analysis, the area under the curve was 0.64 for the mean kurtosis (MK) value and 0.77 for the ADC value. In the median analysis, the MK value was 0.74, and the ADC value was 0.75. The MK and ADC values correlated moderately in the pixel analysis and strongly in the median analysis. Our simple DKI system created DK images simultaneously with ADC maps, and the obtained MK and ADC values were useful for differentiating head and neck tumors from normal tissue. en-copyright= kn-copyright= en-aut-name=ShimizuYudai en-aut-sei=Shimizu en-aut-mei=Yudai kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=Al-HammadWlla E. en-aut-sei=Al-Hammad en-aut-mei=Wlla E. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=YoshidaSuzuka en-aut-sei=Yoshida en-aut-mei=Suzuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=FukumuraYuka en-aut-sei=Fukumura en-aut-mei=Yuka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=NakamuraYoshihide en-aut-sei=Nakamura en-aut-mei=Yoshihide kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=ImajohSatoshi en-aut-sei=Imajoh en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=BamgboseBabatunde O. en-aut-sei=Bamgbose en-aut-mei=Babatunde O. kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=YanagiYoshinobu en-aut-sei=Yanagi en-aut-mei=Yoshinobu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= en-aut-name=AsaumiJunichi en-aut-sei=Asaumi en-aut-mei=Junichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=17 ORCID= affil-num=1 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=2 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=4 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=5 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=6 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=7 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=8 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=9 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=11 en-affil=Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=12 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=13 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=14 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=15 en-affil=Department of Oral Diagnostic Sciences, Faculty of Dentistry, Bayero University kn-affil= affil-num=16 en-affil=Department of Dental Informatics, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=17 en-affil=Department of Oral and Maxillofacial Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= en-keyword=simple diffusion kurtosis imaging kn-keyword=simple diffusion kurtosis imaging en-keyword=mean kurtosis kn-keyword=mean kurtosis en-keyword=clinical trial kn-keyword=clinical trial en-keyword=head and neck tumor kn-keyword=head and neck tumor en-keyword=magnetic resonance imaging kn-keyword=magnetic resonance imaging END start-ver=1.4 cd-journal=joma no-vol=25 cd-vols= no-issue=3 article-no= start-page=109 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=2023124 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Quantitative evaluation of the reduction of distortion and metallic artifacts in magnetic resonance images using the multiacquisition variable‑resonance image combination selective sequence en-subtitle= kn-subtitle= en-abstract= kn-abstract=Magnetic resonance imaging (MRI) is superior to computed tomography (CT) in determining changes in tissue structure, such as those observed following inflammation and infection. However, when metal implants or other metal objects are present, MRI exhibits more distortion and artifacts compared with CT, which hinders the accurate measurement of the implants. A limited number of reports have examined whether the novel MRI sequence, multiacquisition variable-resonance image combination selective (MAVRIC SL), can accurately measure metal implants without distortion. Therefore, the present study aimed to demonstrate whether MAVRIC SL could accurately measure metal implants without distortion and whether the area around the metal implants could be well delineated without artifacts. An agar phantom containing a titanium alloy lumbar implant was used for the present study and was imaged using a 3.0 T MRI machine. A total of three imaging sequences, namely MAVRIC SL, CUBE and magnetic image compilation (MAGiC), were applied and the results were compared. Distortion was evaluated by measuring the screw diameter and distance between the screws multiple times in the phase and frequency directions by two different investigators. The artifact region around the implant was examined using a quantitative method following standardization of the phantom signal values. It was revealed that MAVRIC SL was a superior sequence compared with CUBE and MAGiC, as there was significantly less distortion, a lack of bias between the two different investigators and significantly reduced artifact regions. These results suggested the possibility of utilizing MAVRIC SL for follow-up to observe metal implant insertions. en-copyright= kn-copyright= en-aut-name=HiranoMasaki en-aut-sei=Hirano en-aut-mei=Masaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=MutoYuki en-aut-sei=Muto en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KurodaMasahiro en-aut-sei=Kuroda en-aut-mei=Masahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=FujiwaraYuta en-aut-sei=Fujiwara en-aut-mei=Yuta kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=SasakiTomoaki en-aut-sei=Sasaki en-aut-mei=Tomoaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KurodaKazuhiro en-aut-sei=Kuroda en-aut-mei=Kazuhiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=KamizakiRyo en-aut-sei=Kamizaki en-aut-mei=Ryo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=ImajohSatoshi en-aut-sei=Imajoh en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=TanabeYoshinori en-aut-sei=Tanabe en-aut-mei=Yoshinori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=E. Al-HammadWlla en-aut-sei=E. Al-Hammad en-aut-mei=Wlla kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=NakamitsuYuki en-aut-sei=Nakamitsu en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=ShimizuYudai en-aut-sei=Shimizu en-aut-mei=Yudai kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=SugimotoKohei en-aut-sei=Sugimoto en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=SugiantoIrfan en-aut-sei=Sugianto en-aut-mei=Irfan kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=O. BamgboseBabatunde en-aut-sei=O. Bamgbose en-aut-mei=Babatunde kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= affil-num=1 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=2 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=4 en-affil=Division of Clinical Radiology Service, Okayama Central Hospital kn-affil= affil-num=5 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=7 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=8 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=9 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University kn-affil= affil-num=10 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=11 en-affil=Department of Radiological Technology, Graduate School of Health Sciences, Okayama University, Okayama 700‑8558, Japan kn-affil= affil-num=12 en-affil=Department of Oral and Maxillofacial Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences kn-affil= affil-num=13 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University, Okayama, 770‑8558, Japan kn-affil= affil-num=14 en-affil=Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=15 en-affil=Department of Oral Radiology, Faculty of Dentistry, Hasanuddin University kn-affil= affil-num=16 en-affil=Department of Oral Diagnostic Sciences, Faculty of Dentistry, Bayero University kn-affil= en-keyword=MAVRIC SL kn-keyword=MAVRIC SL en-keyword=metal artifacts kn-keyword=metal artifacts en-keyword=implant kn-keyword=implant en-keyword=phantom kn-keyword=phantom en-keyword=MRI kn-keyword=MRI END start-ver=1.4 cd-journal=joma no-vol=23 cd-vols= no-issue= article-no= start-page=e13817 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2022 dt-pub=20221124 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Validation of pencil beam scanning proton therapy with multi-leaf collimator calculated by a commercial Monte Carlo dose engine en-subtitle= kn-subtitle= en-abstract= kn-abstract=This study aimed to evaluate the clinical beam commissioning results and lateral penumbra characteristics of our new pencil beam scanning (PBS) proton therapy using a multi-leaf collimator (MLC) calculated by use of a commercial Monte Carlo dose engine. Eighteen collimated uniform dose plans for cubic targets were optimized by the RayStation 9A treatment planning system (TPS), varying scan area, modulation widths, measurement depths, and collimator angles. To test the patient-specific measurements, we also created and verified five clinically realistic PBS plans with the MLC, such as the liver, prostate, base-of-skull, C-shape, and head-and-neck. The verification measurements consist of the depth dose (DD), lateral profile (LP), and absolute dose (AD). We compared the LPs and ADs between the calculation and measurements. For the cubic plans, the gamma index pass rates (gamma-passing) were on average 96.5% +/- 4.0% at 3%/3 mm for the DD and 95.2% +/- 7.6% at 2%/2 mm for the LP. In several LP measurements less than 75 mm depths, the gamma-passing deteriorated (increased the measured doses) by less than 90% with the scattering such as the MLC edge and range shifter. The deteriorated gamma-passing was satisfied by more than 90% at 2%/2 mm using uncollimated beams instead of collimated beams except for three planes. The AD differences and the lateral penumbra width (80%-20% distance) were within +/- 1.9% and +/- 1.1 mm, respectively. For the clinical plan measurements, the gamma-passing of LP at 2%/2 mm and the AD differences were 97.7% +/- 4.2% on average and within +/- 1.8%, respectively. The measurements were in good agreement with the calculations of both the cubic and clinical plans inserted in the MLC except for LPs less than 75 mm regions of some cubic and clinical plans. The calculation errors in collimated beams can be mitigated by substituting uncollimated beams. en-copyright= kn-copyright= en-aut-name=TominagaYuki en-aut-sei=Tominaga en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=SakuraiYusuke en-aut-sei=Sakurai en-aut-mei=Yusuke kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=MiyataJunya en-aut-sei=Miyata en-aut-mei=Junya kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=HaradaShuichi en-aut-sei=Harada en-aut-mei=Shuichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=AkagiTakashi en-aut-sei=Akagi en-aut-mei=Takashi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil=Division of Radiological Technology, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=2 en-affil=Department of Radiotherapy, Medical Co. Hakuhokai, Osaka Proton Therapy Clinic kn-affil= affil-num=3 en-affil=Division of Radiological Technology, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=4 en-affil=Hyogo Ion Beam Medical Support kn-affil= affil-num=5 en-affil=Hyogo Ion Beam Medical Support kn-affil= affil-num=6 en-affil=Division of Radiological Technology, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= en-keyword=commissioning kn-keyword=commissioning en-keyword=lateral penumbra kn-keyword=lateral penumbra en-keyword=multi-leaf collimator kn-keyword=multi-leaf collimator en-keyword=pencil beam scanning kn-keyword=pencil beam scanning en-keyword=proton therapy kn-keyword=proton therapy END start-ver=1.4 cd-journal=joma no-vol=74 cd-vols= no-issue=5 article-no= start-page=415 end-page=422 dt-received= dt-revised= dt-accepted= dt-pub-year=2020 dt-pub=202010 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Oblique Surface Dose Calculation in High-Energy X-ray Therapy en-subtitle= kn-subtitle= en-abstract= kn-abstract=During radiation therapy, incident radiation oblique to the skin surface is high and may cause severe skin damage. Understanding the dose of radiation absorbed by the skin is important for predicting skin damage due to radiation. In this study, we used a high-energy (4 MV) X-ray system and an optically stimulated luminescence dosimeter (OSLD) that was developed for personal exposure dosimetry. We determined the dose variation and angular dependence, which are the characteristics of a small OSLD required to derive the calculation formula for the oblique surface dose. The dose variation was determined using the coefficient of variation. The maximum coefficient of variation for 66 small-field OSLDs was 1.71%. The angular dependence, obtained from the dose ratio of the dosimeter in the vertical direction, had a maximum value of 1.37. We derived a new equation in which the oblique surface dose can be calculated within the error range of −7.7-5.1%. en-copyright= kn-copyright= en-aut-name=NarihiroNaomasa en-aut-sei=Narihiro en-aut-mei=Naomasa kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=TakedaYoshihiro en-aut-sei=Takeda en-aut-mei=Yoshihiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= affil-num=1 en-affil=Graduate School of Health Sciences, Department of Radiological Technology, Okayama University kn-affil= affil-num=2 en-affil=Department of Graduate School of Interdisciplinary Sciences and Engineering in Health Systems, Okayama University kn-affil= affil-num=3 en-affil=Graduate School of Health Sciences, Department of Radiological Technology, Okayama University kn-affil= en-keyword=optically stimulated luminescent dosimeter kn-keyword=optically stimulated luminescent dosimeter en-keyword=radiotherapy kn-keyword=radiotherapy en-keyword=oblique surface dose kn-keyword=oblique surface dose en-keyword=high-energy X-ray therapy kn-keyword=high-energy X-ray therapy en-keyword=angular dependence kn-keyword=angular dependence END start-ver=1.4 cd-journal=joma no-vol=21 cd-vols= no-issue=2 article-no= start-page=89 end-page=97 dt-received= dt-revised= dt-accepted= dt-pub-year=2020 dt-pub=20200120 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Impact of patient positioning uncertainty in noncoplanar intracranial stereotactic radiotherapy en-subtitle= kn-subtitle= en-abstract= kn-abstract=The aim of this study is to evaluate the patient positioning uncertainty in noncoplanar stereotactic radiosurgery or stereotactic radiotherapy (SRS/SRT) for intracranial lesions with the frameless 6D ExacTrac system. In all, 28 patients treated with SRS/SRT of 70 treatment plans at our institution were evaluated in this study. Two X-ray images with the frameless 6D ExacTrac system were first acquired to correct (XC) and verify (XV) the patient position at a couch angle of 0o. Subsequently, the XC and XV images were also acquired at each planned couch angle for using noncoplanar beams to detect position errors caused by rotating a couch. The translational XC and XV shift values at each couch angle were calculated for each plan. The percentages of the translational XC shift values within 1.0 mm for each planned couch angle for using noncoplanar beams were 77.86%, 72.26%, and 98.47% for the lateral, longitudinal, and vertical directions, respectively. Those within 2.0 mm were 98.22%, 97.96%, and 99.75% for the lateral, longitudinal, and vertical directions, respectively. The maximum absolute values of the translational XC shifts among all planned couch angles for using noncoplanar beams were 2.69, 2.45, and 2.17 mm for the lateral, longitudinal, and vertical directions, respectively. The overall absolute values of the translational XV shifts were less than 1.0 mm for all directions except for one case in the longitudinal direction. The patient position errors were detected after couch rotation for using noncoplanar beams, and they exceeded a planning target volume (PTV) margin of 1.0-2.0 mm used commonly in SRS/SRT treatment. These errors need to be corrected at each planned couch angle, or the PTV margin should be enlarged. en-copyright= kn-copyright= en-aut-name=TanakaYoshihiro en-aut-sei=Tanaka en-aut-mei=Yoshihiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=OitaMasataka en-aut-sei=Oita en-aut-mei=Masataka kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=InomataShinichiro en-aut-sei=Inomata en-aut-mei=Shinichiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=FuseToshiaki en-aut-sei=Fuse en-aut-mei=Toshiaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=AkinoYuichi en-aut-sei=Akino en-aut-mei=Yuichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=ShimomuraKohei en-aut-sei=Shimomura en-aut-mei=Kohei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= affil-num=1 en-affil=Department of Radiation Therapy,Japanese Red Cross Society Kyoto Daiichi Hospital kn-affil= affil-num=2 en-affil=Department of Healthcare Sciences,Graduate School of Interdisciplinary Scienceand Engineering in Health Systems,Okayama University kn-affil= affil-num=3 en-affil=Department of Radiation Therapy,Japanese Red Cross Society Kyoto Daiichi Hospital kn-affil= affil-num=4 en-affil=Department of Radiation Therapy,Japanese Red Cross Society Kyoto Daiichi Hospital kn-affil= affil-num=5 en-affil=Oncology Center, Osaka University Hospital kn-affil= affil-num=6 en-affil=Kyoto College of Medical Science kn-affil= en-keyword=IGRT kn-keyword=IGRT en-keyword=noncoplanar radiotherapy kn-keyword=noncoplanar radiotherapy en-keyword=patient positioning uncertainty kn-keyword=patient positioning uncertainty en-keyword=SRS kn-keyword=SRS en-keyword=SRT kn-keyword=SRT END