Kyushu University Academic Staff Educational and Research Activities Database
List of Papers
Hidetaka Arimura Last modified date:2021.11.18

Professor / Department of Health Sciences, Medical Quantum Sciences / Department of Health Sciences / Faculty of Medical Sciences


Papers
1. Yunhao Cui, Hidetaka Arimura, Risa Nakano, Tadamasa Yoshitake, Yoshiyuki Shioyama, Hidetake Yabuuchi, Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks, Journal of Radiation Research, Volume 62, Issue 2, March 2021, Pages 346–355, https://doi.org/10.1093/jrr/rraa132, 2021.03, [URL].
2. Kenta Ninomiya, Hidetaka Arimura, Wai Yee Chan, Kentaro Tanaka, Shinichi Mizuno, Nadia Fareeda Muhammad Gowdh, Nur Adura Yaakup, Chong-Kin Liam, Chee-Shee Chai, Kwan Hoong Ng, Robust identification of EGFR mutated NSCLC patients from three countries using Betti numbers, PLOS ONE, January 11, 2021, https://doi.org/10.1371/journal.pone.0244354, 2021.01, [URL], We have proposed a novel robust radiogenomics approach to the identification of epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC) using Betti numbers (BNs).The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients.The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters..
3. Quoc Cuong Le, Hidetaka Arimura, Kenta Ninomiya, Yutaro Kabata, Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients, Scientific Reports 10, 21301 (2020), https://doi.org/10.1038/s41598-020-78338-7, 2020.12, [URL], This study demonstrated the usefulness of radiomic features based on the Hessian index of differential topology for the prediction of prognosis prior to treatment in head-and-neck (HN) cancer patients. The Hessian index, which can indicate tumor heterogeneity with convex, concave, and other points (saddle points), was calculated as the number of negative eigenvalues of the Hessian matrix at each voxel on computed tomography (CT) images. This result indicates that index features could provide more prognostic information than conventional features and further increase the prognostic value of clinical variables in HN cancer patients..
4. Hidemi KAMEZAWA, Hidetaka ARIMURA, Ryuji YASUMATSU, Kenta NINOMIYA, Shu HASEAI, Preoperative and non-invasive approach for radiomic biomarker-based prediction of malignancy grades in patients with parotid gland cancer in magnetic resonance images, Medical Imaging and Information Sciences 2020 Volume 37 Issue 4, 2020.12.
5. Taka-aki Hirose, Hidetaka Arimura, Kenta Ninomiya, Tadamasa Yoshitake, Jun-ichi Fukunaga, Yoshiyuki Shioyama, Radiomic prediction of radiation pneumonitis on pretreatment planning computed tomography images prior to lung cancer stereotactic body radiation therapy, Scientific Reports 10, 20424 (2020), https://doi.org/10.1038/s41598-020-77552-7, 2020.11, [URL], This study developed a radiomics-based predictive model for radiation-induced pneumonitis (RP) after lung cancer stereotactic body radiation therapy (SBRT) on pretreatment planning computed tomography (CT) images. The radiomic features calculated on pretreatment planning CT images could be predictive imaging biomarkers for RP after lung cancer SBRT.
6. Leni Aziyus Fitria, Freddy Haryanto, Hidetaka Arimura, Cui YunHao,Kenta Ninomiya, Risa Nakano, Mohammad Haekal, Yuni Warty, Umar Fauzi, Automated classification of urinary stones based on microcomputed tomography images using convolutional neural network, Physica Medica Vol.78, October 2020, Pages 201-208, https://doi.org/10.1016/j.ejmp.2020.09.007, 2020.10, [URL].
7. Toya R, Saito T, Matsuyama T, Kai Y, Shiraishi S, Murakami D, Yoshida R, Watakabe T, Sakamoto F, Tsuda N, Arimura H, Orita Y, Nakayama H, Oya N, Diagnostic Value of FDG-PET/CT for the Identification of Extranodal Extension in Patients With Head and Neck Squamous Cell Carcinoma., Anticancer Res. 2020;40(4):2073-2077., doi:10.21873/anticanres.14165, 2020.04.
8. Shu Haseai, Hidetaka Arimura, Kaori Asai, Tadamasa Yoshitake, Yoshiyuki Shioyama, Similar-cases-based planning approaches with beam angle optimizations using water equivalent path length for lung stereotactic body radiation therapy, Radiological Physics and Technology, https://doi.org/10.1007/s12194-020-00558-3, 2020.03, [URL].
9. Yudai Kai, Hidetaka Arimura, Kenta Ninomiya, Tetsuo Saito, Yoshinobu Shimohigashi, Akiko Kuraoka, Masato Maruyama, Ryo Toya, Natsuo Oya, Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy, Journal of Radiation Research,Volume 61, Issue 2, Pages 285–297, https://doi.org/10.1093/jrr/rrz105, 2020.03.
10. Taka-aki Hirose, Hidetaka Arimura, Jun-ichi Fukunaga, Saiji Ohga, Tadamasa Yoshitake, Yoshiyuki Shioyama, Observer Uncertainties of Soft Tissue-based Patient Positioning in IGRT, Journal of Applied Clinical Medical Physics, Volume 21, Issue 2, Pages: 73-81, https://doi.org/10.1002/acm2.12817, 2020.01, [URL].
11. Yudai Kai, Hidetaka Arimura, Ryo Toya, Tetsuo Saito, Tomohiko Matsuyama, Yoshiyuki Fukugawa, Shinya Shiraishi, Yoshinobu Shimohigashi, Masato Maruyama, Natsuo Oya, Comparability of rigid image registration with deformable image registration for diagnostic position PET/CT images in delineation of gross tumor volumes in nasopharyngeal carcinoma radiotherapy planning: An observer study, Japanese Journal of Radiology, vol.38, pp256–264, https://doi.org/10.1007/s11604-019-00911-6, 2020.03, [URL].
12. Alamgir Hossain, Hidetaka ARIMURA, Fumio Kinoshita, Kenta Ninomiya, Sumiko Watanabe, Kenjiro Imada, Ryoma Koyanagi, Yoshinao Oda, Automated Approach for Estimation of Grade Groups for Prostate Cancer based on Histological Image Feature Analysis, The Prostate, Volume 80 Issue 3 Page 291-302 Published 2020, DOI:10.1002/pros.23943, 2020.02, [URL].
13. Kenta NINOMIYA, Hidetaka ARIMURA, Homological radiomics analysis for prognostic prediction in lung cancer patients, Physica Medica: European Journal of Medical Physics, Volume 69 Page 90-100, DOI:10.1016/j.ejmp.2019.11.026, 2020.01, [URL].
14. Motoki SASAHARA, Hidetaka ARIMURA, Kenta NINOMIYA, Takaaki HIROSE, Noriyuki NAGAMI, Yudai KAI, Yusuke SHIBAYAMA, Saiji OHGA, Junnichi FUKUNAGA , Machine-Learning-Based Framework for Estimation of Prostate Locations with Anatomical Feature Points on CBCT Images for Image-Guided Target-Based Patient Positioning in Prostate Cancer Radiotherapy, Medical Imaging and Information Sciences 2019 Volume 36 Issue 3 Pages 122-127 , https://doi.org/10.11318/mii.36.122 , 2019.10.
15. Tran Thi Thao Nguyen, Hidetaka Arimura, Ryosuke Asamura, Taka-aki Hirose, Saiji Ohga, Jun-ichi Fukunaga, Comparison of volumetric-modulated arc therapy and intensity-modulated radiation therapy prostate cancer plans accounting for cold spots, Radiol Phys Technol. 2019 Jun;12(2):137-148. Epub 2019 Feb 25., doi: 10.1007/s12194-019-00502-0, 2019.06.
16. Noriyuki Nagami, Hidetaka Arimura, Mazen Soufi, Mitsutoshi Ohishi, Takeshi Imaizumi, Yoshimasa Yamagushi, Kenta Ninomiya, Sunao Tokumaru, Shingo Toyama, Kanako Kawasaki, Aiko Kitazato, Satoshi Takita, Kouji Uba, Hiroyuki Irie, An approach for evaluation of delineation accuracy of GTV contours with considering interobserver variability in reference contours: Impact of MAR on radiation treatment planning, Medical Imaging and Information Sciences 2019 Volume 36 Issue 1 Pages 4-9 , https://doi.org/10.11318/mii.36.4, 2019.03.
17. Kenta NINOMIYA, Hidetaka ARIMURA, Motoki SASAHARA, Yudai KAI, Taka-aki HIROSE, Saiji OHGA, Feasibility of anatomical feature points for estimation of prostate locations in the Bayesian delineation frameworks for prostate cancer radiotherapy, Radiological Physics and Technology 2018, 11:434–444, https://doi.org/10.1007/s12194-018-0481-2, 2018.11.
18. Taka-aki Hirose, Hidetaka Arimura, Yusuke Shibayama, Jun-ichi Fukunaga, Saiji Ohga, Effect of accounting for interfractional CTV shape variations in PTV margins on prostate cancer radiation treatment plans, Physica Medica: European Journal of Medical Physics, October 2018, Volume 54, Pages 66–76, https://doi.org/10.1016/j.ejmp.2018.09.008, 2018.10.
19. Mazen Soufi, Hidetaka Arimura, Noriyuki Nagami, Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition-based radiomic features, Medical Physics 2018 Nov;45(11):5116-5128. Epub 2018 Oct 19., doi: 10.1002/mp.13202., 2018.10, Purpose : To identify the optimal mother wavelets in survival prediction of lung cancer patients using wavelet-decomposition-based (WDB) radiomic features in CT images.
Materials and Methods: CT images of patients with histologically confirmed non-small cell lung carcinomas (NSCLCs) in Training (Dataset T; n = 162) and validation (Dataset V; n = 143) datasets were analyzed for this study. The optimal mother wavelets were identified based on the impacts of the WDB radiomic features on the patient survival times. 432 three-dimensional WDB radiomic features were calculated from regions of interest (ROI) of 162 tumor contours. A Coxnet algorithm was used to select a subset of radiomic features (signature) based on the prediction of survival times with a 5-fold cross-validation. The impacts of the radiomic features on the patients’ survival times were assessed by using a multivariate Cox proportional hazard regression (MCPHR) model. The major contribution of this study was to identify optimal mother wavelets based on a maximization of a novel ranking index (RI) incorporating the Coxnet prediction error and the summation of the p-values of the radiomic features in the MCPHR model on Dataset T. The prognostic performance of the optimal mother wavelets was validated based on the concordance index (CI) of the MCPHR models when applied to Dataset V. The proposed approach was tested by using 31 mother wavelets from 6 wavelet families that were available in a commercially available software (Matlab® 2016b).
Results: The optimal mother wavelets were Symlet 5 and Biorthogonal 2.6 at 128 re-quantization levels, which yielded RIs of 4.27±0.29 (3 features) and 6.50±0.50 (5 features), respectively. The CIs of the MCPHR models of Symlet 5 were 0.66±0.03 (Dataset T) and 0.64±0.00 (Dataset V), whereas those of Biorthogonal 2.6 were 0.68±0.03 (Dataset T) and 0.62±0.02 (Dataset V). The radiomic signatures included the GLRLM-based HLH gray level non-uniformity feature that demonstrated statistically significant differences in stratifying patients with better and worse prognoses in Datasets T and V.
Conclusion: This study has revealed the potential of Symlet and Biorthogonal mother wavelets in the survival prediction of lung cancer patients by using WDB radiomic features in CT images..
20. Satoshi Yoshidome, Hidetaka Arimura, Kotaro Terashima, Masakazu Hirakawa, Taka-aki Hirose, Junichi Fukunaga, Yasuhiko Nakamura, Hiroshi Honda, Automated and robust estimation framework for lung tumor location in kilovolt cone-beam computed tomography images for target-based patient positioning in lung stereotactic body radiotherapy, Medical Imaging and Information Sciences 2018 Vol. 35 No.3, pp. 48-54, https://doi.org/10.11318/mii.35.48, 2018.09.
21. Hidemi Kamezawa, Hidetaka Arimura, Hiroyuki Arakawa, Noboru Kameda, Investigation for a practical patient dose index for assessment of patient organ dose from cone-beam computed tomography in radiation therapy using a Monte Carlo simulation, Radiation Protection Dosimetry, Volume 181, Issue 4, 1 November 2018, Pages 333–342, https://doi.org/10.1093/rpd/ncy032, 2018.03.
22. Mohammad Haekal, Hidetaka Arimura, Taka-aki Hirose, Yusuke Shibayama, Saiji Ohga, Junichi Fukunaga, Yoshiyuki Umezu, Hiroshi Honda, Tomonari Sasaki, Computational analysis of interfractional anisotropic shape variations of the rectum in prostate cancer radiation therapy, Physica Medica: European Journal of Medical Physics, Volume 46, February 2018, Pages 168-179, https://doi.org/10.1016/j.ejmp.2017.12.019, 2018.02.
23. Mazen Soufi, Hidetaka Arimura, Takahiro Nakamoto, Taka-aki Hirose, Saiji Ohga, Yoshiyuki Umezu, Hiroshi Honda, Tomonari Sasaki, Exploration of temporal stability and prognostic power of radiomic features based on electronic portal imaging device images, Physica Medica: European Journal of Medical Physics, Volume 46, February 2018, Pages 32-44, https://doi.org/10.1016/j.ejmp.2017.11.037, 2018.02.
24. Yasuo Kawata, Hidetaka Arimura, Koujiro Ikushima, Ze Jin, Kento Morita, Chiaki Tokunaga, Hidetake Yabu-uchi, Yoshiyuki Shioyama, Tomonari Sasaki, Hiroshi Honda, Masayuki Sasaki, Impact of Pixel-based Machine-Learning Techniques on Automated Frameworks for Delineation of Gross Tumor Volume Regions for Stereotactic Body Radiation Therapy, Physica Medica: European Journal of Medical Physics Volume 42, October 2017, Pages 141-149, 2017.10.
25. Noriyuki Kadoya, Kumiko Karasawa, Iori Sumida, Hidetaka Arimura, Yasumasa Kakinohana, Shigeto Kabuki, Hajime Monzen, Teiji Nishio, Hiroki Shirato, Syogo Yamada, Educational outcomes of a medical physicist program over the past 10 years in Japan, Journal of Radiation Research 1-6. DOI: https://doi.org/10.1093/jrr/rrx016 , https://doi.org/10.1093/jrr/rrx016 , 2017.04.
26. Yusuke Shibayama, Hidetaka Arimura, Taka-aki Hirose, Takahiro Nakamoto, Tomonari Sasaki, Saiji Ohga, Norimasa Matsushita, Yoshiyuki Umezu, Yasuhiko Nakamura, Hiroshi Honda, Investigation of interfractional shape variations based on statistical point distribution model for prostate cancer radiation therapy, Medical Physics, Volume 44, Issue 5, Pages 1837–1845, DOI:10.1002/mp.12217, First published: 20 April 2017, 10.1002/mp.12217, 2017.05, Purpose: The setup errors and organ motion errors pertaining to clinical target volume (CTV) have been considered as two major causes of uncertainties in the determination of the CTV-to-planning target volume (PTV) margins for prostate cancer radiation treatment planning. We based our study on the assumption that interfractional target shape variations are not negligible as another source of uncertainty for the determination of precise CTV-to-PTV margins. Thus, we investigated the interfractional shape variations of CTVs based on a point distribution model (PDM) for prostate cancer radiation therapy.
Materials and Methods: To quantify the shape variations of CTVs, the PDM was applied for the contours of 4 types of CTV regions (low-risk, intermediate- risk, high-risk CTVs, and prostate plus entire seminal vesicles), which were delineated by considering prostate cancer risk groups on planning computed tomography (CT) and cone beam CT (CBCT) images of 73 fractions of 10 patients. The standard deviations (SDs) of the interfractional random errors for shape variations were obtained from covariance matrices based on the PDMs, which were generated from vertices of triangulated CTV surfaces. The correspondences between CTV surface vertices were determined based on a thin plate spline robust point matching algorithm. The systematic error for shape variations was defined as the average deviation between surfaces of an average CTV and planning CTVs, and the random error as the average deviation of CTV surface vertices for fractions from an average CTV surface.
Results: The means of the SDs of the systematic errors for the 4 types of CTVs ranged from 1.0 to 2.0 mm along the anterior direction, 1.2 to 2.6 mm along the posterior direction, 1.0 to 2.5 mm along the superior direction, 0.9 to 1.9 mm along the inferior direction, 0.9 to 2.6 mm along the right direction, and 1.0 to 3.0 mm along the left direction. Concerning the random errors, the means of the SDs ranged from 0.9 to 1.2 mm along the anterior direction, 1.0 to 1.4 mm along the posterior direction, 0.9 to 1.3 mm along the superior direction, 0.8 to 1.0 mm along the inferior direction, 0.8 to 0.9 mm along the right direction, and 0.8 to 1.0 mm along the left direction.
Conclusions: Since the shape variations were not negligible for intermediate and high risk CTVs, they should be taken into account for the determination of the CTV-to-PTV margins in radiation treatment planning of prostate cancer..
27. Takahiro Nakamoto, Hidetaka Arimura, KENICHI MOROOKA, Yasuhiko Nakamura, Tomonari Sasaki, Taka-aki Hirose, Yoshiyuki Umedu, Hiroshi Honda, Hideki Hirata, A framework for estimating four-dimensional dose distributions during stereotactic body radiation therapy based on a 2D/3D registration technique with an adaptive transformation parameter approach, Medical Imaging and Information Sciences, Vol. 33(2016) No. 3, p.48-56, DOI:doi.org/10.11318/mii.33.48, 2016.10.
28. Koujiro Ikushima, Hidetaka Arimura, Ze Jin, Hidetake Yabu-uchi, Jumpei Kuwazuru, Yoshiyuki Shioyama, Tomonari Sasaki, Hiroshi Honda, Masayuki Sasaki, Computer-assisted framework for machine-learning-based delineation of GTV regions on datasets of planning CT and PET/CT images, Journal of Radiation Research, Volume 58, Issue 1,123-134, Published: 23 January 2017, DOI: 10.1093/jrr/rrw082, DOI 10.1093/jrr/rrw082, 2017.01.
29. Mazen Soufi, Hidetaka Arimura, Katsumasa Nakamura, Fauzia P. Lestari, Freddy Haryanto, Taka-aki Hirose, Yoshiyuki Umedu, Yoshiyuki Shioyama, Fukai Toyofuku, Feasibility of Differential Geometry-Based Features in Detection of Anatomical Feature Points on Patient Surfaces in Range Image-Guided Radiation Therapy, International Journal of Computer Assisted Radiology and Surgery, November 2016, Volume 11, Issue 11, pp 1993–2006, First Online: 13 June 2016, DOI:10.1007/s11548-016-1436-x, 2016.11.
30. Yoshifumi Oku, Hidetaka Arimura, Tran Thi Thao Nguyen, Yoshiyuki Hiraki, Masahiko Toyota, Yasumasa Saigo, Takashi Yoshiura, Hideki Hirata, Investigation of whether in-room CT-based adaptive intracavitary brachytherapy for uterine cervical cancer is robust against interfractional location variations of organs and/or applicators, Journal of Radiation Research (2016) 57 (6): 677-683, 2016 Jun 13, Published: 02 December 2016, DOI:10.1093/jrr/rrw043, 2016.11.
31. Hidemi Kamezawa, Hidetaka Arimura, Katsutoshi Shirieda, Noboru Kameda, Masafumi Ohki, Feasibility of patient dose reduction based on various noise suppression filters for cone-beam computed tomography in an image-guided patient positioning system, Physics in Medicine and Biology 2016 May 7;61(9):3609-36. Doi: 10.1088/0031-9155/61/9/3609 (Epub 2016 Apr 11), 2016.05.
32. Ze Jin, Hidetaka Arimura, Shingo Kakeda, Fumio Yamashita, Makoto Sasaki, Yukunori Korogi, An Ellipsoid Convex Enhancement Filter for Detection of Asymptomatic Intracranial Aneurysm Candidates in CAD Frameworks, Medical Physics Vol.43, No.2, 951 (2016); http://dx.doi.org/10.1118/1.4940349 (Jan. 28, 2016), 2016.01.
33. Noriyuki Kadoya, Kumiko Karasawa, Iori Sumida, Hidetaka Arimura, Syogo Yamada, The current status of education and career paths of students after completion of medical physicist programs in Japan: a survey by the Japanese Board for Medical Physicist Qualification, Radiological Physics and Technology, DOI 10.1007/s12194-015-0317-2, DOI 10.1007/s12194-015-0317-2, 2015.06.
34. Takahiro Nakamoto, Hidetaka Arimura, Katsumasa Nakamura, Yoshiyuki Shioyama, Asumi Mizoguchi, Taka-aki Hirose, Hiroshi Honda, Yoshiyuki Umedu, Yasuhiko Nakamura, Hideki Hirata, A computerized framework for monitoring four-dimensional dose distributions during stereotactic body radiation therapy using a portal dose image-based 2D/3D registration approach, Computerized Medical Imaging and Graphics, DOI: http://dx.doi.org/10.1016/j.compmedimag.2014.12.003, March 2015 Volume 40, Pages 1–12 , 2015.03.
35. Satoshi Yoshidome, Hidetaka Arimura, Katsumasa Nakamura, Yoshiyuki Shioyama, Kazushige Atsumi, Hideki Yoshikawa, Kei Nishikawa, Hideki Hirata, Feasibility study of automated framework for estimating lung tumor locations for target-based patient positioning in stereotactic body radiotherapy, doi.org/10.1155/2015/653974, BioMed Research International, Article ID 653974, 2014.12.
36. Ze Jin, Hidetaka Arimura, Yoshiyuki Shioyama, Katsumasa Nakamura, Jumpei Kuwazuru, Taiki Magome, Hidetake Yabuuchi, Hiroshi Honda, Hideki Hirata, Masayuki Sasaki, Computer-Assisted Delineation of Lung Tumor Regions in Treatment Planning CT Images with PET/CT Image Sets Based on an Optimum Contour Selection Method, Journal of Radiation Research, 10.1093/jrr/rru056, 2014 Nov;55(6):1153-62, 2014.11.
37. Natsuo Tomita, Takeshi Kodaira, Teruki Teshima, Kazuhiko Ogawa, Yu Kumazaki, Chikako Yamauchi, Takafumi Toita, Takashi Uno, Minako Sumi, Hiroshi Onishi, Masahiro Kenjo, Katsumasa Nakamura, Hodaka Numasaki, Masahiko Koizumi, Yuki Otani, Naoto Shikama, Naoki Nakamura, Kiyotomo Matsugi, Hidetaka Arimura, Yoshiyuki Shioyama, Japanese Structure Survey of High-precision Radiotherapy in 2012 Based on Institutional Questionnaire about the Patterns of Care, Jpn J Clin Oncol., 10.1093/jjco/hyu041, 44(6)579–586, 2014.04.
38. Hidetaka Arimura, Genyu Kakiuchi, Yoshiyuki Shioyama, Shin-ichi Minohara, Takahiro Nakamoto, Katsumasa Nakamura, Hiroshi Honda, Mutsumi Tashiro, Tatsuaki Kanai, Hideki Hirata, Quantitative evaluation of the robustness of beam directions based on power spectral analysis of water-equivalent path length image in charged particle therapy, International Journal of Intelligent Computing in Medical Sciences and Image Processing, Vol. 6, No.1, 1-16, 2014.07.
39. Taiki Magome, Hidetaka Arimura, Yoshiyuki Shioyama, Katsumasa Nakamura, Hiroshi Honda, Hideki Hirata, Similar-case-based optimization of beam arrangements in stereotactic body radiotherapy for assisting treatment planners, BioMed Research International, Volume 2013 (2013), Article ID 309534, 10 pages, http://dx.doi.org/10.1155/2013/309534, 2013.11.
40. Taiki Magome, Hidetaka Arimura, Yoshiyuki Shioyama, Asumi Mizoguchi, Chiaki Tokunaga, Katsumasa Nakamura, Hiroshi Honda, Masafumi Ohki, Fukai Toyofuku, Hideki Hirata, Computer-aided beam arrangement based on similar cases in radiation treatment planning databases for stereotactic lung radiation therapy., Journal of Radiation Research, 10.1093/jrr/rrs123, 54, 3, 569-577, 2013.05.
41. Arimura H, Itano W, Shioyama Y, Matsushita N, Magome T, Yoshitake T, Anai S, Nakamura K, Yoshidome S, Yamagami A, Honda H, Ohki M, Toyofuku F, Hirata H., Computerized estimation of patient setup errors in portal images based on localized pelvic templates for prostate cancer radiotherapy, Journal of Radiation Research, 1;53(6):961-72, 2012.11.
42. Kawata H, Arimura H, Suefuji H, Ohkura S, Saida Y, Nashiki K, Hayashida K, Kawahara T, Ohishi A, Hayabuchi N., Automated Estimation of Number of Implanted Iodine-125 Seeds for Prostate Brachytherapy based on Two-View Analysis of Pelvic Radiographs, Journal of Radiation Research, 53(5):742-52, 2012.09.
43. Kuwazuru J, Arimura H, Kakeda S, Yamamoto D, Magome T, Yamashita Y, Ohki M, Toyofuku F, Korogi Y., Automated Detection of Multiple Sclerosis Candidate Regions in MR Images: False-Positive Removal with Use of an ANN-controlled Level Set Method, Radiological Physics and Technology , 5(1):105-13., 2012.01.
44. Anai S, Arimura H, Nakamura K, Araki F, Matsuki T, Yoshikawa H, Yoshidome S, Shioyama Y, Honda H, Ikeda N., Estimation of focal and extra-focal radiation profiles based on Gaussian modeling in medical linear accelerators, Radiological Physics and Technology, 4(2):173-9, 2011.07.
45. Atsumi K, Shioyama Y, Arimura H, Terashima K, Matsuki T, Ohga S, Yoshitake T, Nonoshita T, Tsurumaru D, Ohnishi K, Asai K, Matsumoto K, Nakamura K, Honda H., Esophageal Stenosis Associated with Tumor Regression in Radiation Therapy for Esophageal Cancer: Frequency and Prediction, International Journal of Radiation Oncology, Biology, Physics, 1;82(5):1973-80, 2012.04.
46. Magome T, Arimura H, Kakeda S, Yamamoto D, Kawata Y, Yamashita Y, Higashida Y, Toyofuku F, Ohki M, Korogi Y., Automated segmentation method of white matter and gray matter regions with multiple sclerosis lesions in MR images., Radiological Physics and Technology, Volume 4, Issue 1 (2011), 61-72, 2011.01, 多発性硬化症患者の脳萎縮の程度を定量的に評価することは非常に難しい.そこで,本研究では,脳萎縮の程度を定量的に評価するために,頭部MR画像から多発性硬化症病変部を含む大脳白質・灰白質領域を自動抽出する手法を世界で初めて開発した.その結果,提案手法が多発性硬化症における大脳白質・灰白質領域を自動抽出するために有用であり,多発性硬化症患者の萎縮を評価するための,ツールとして役立つ可能性を示した..
47. Yamamoto D, Arimura H, Kakeda S, Magome T, Yamashita Y, Toyofuku F, Ohki M, Higashida Y, Korogi Y., Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: False positive reduction scheme consisted of rule-based, level set method, and support vector machine, Computerized Medical Imaging and Graphics, 34,404-413, 2010.05.
48. Kawata Y, Arimura H, Yamashita Y, Magome T, Ohki M, Toyofuku F, Higashida Y, Tsuchiya K., Computer-Aided Evaluation Method of White Matter Hyperintensities Related to Subcortical Vascular Dementia Based on Magnetic Resonance Imaging, Computerized Medical Imaging and Graphics, 34,370-376, 2010.05.
49. Yoshitake T, Shioyama Y, Nakamura K, Ohga S, Nonoshita T, Ohnishi K, Terashima K, Arimura H, Hirata H, Honda H., A clinical evaluation of visual feedback-guided breath-hold reproducibility of tumor location, Physics in Medicine and Biology, 54, 7171-7182, 2009.11.
50. Arimura H, MagomeT, Yamashita Y, Yamamoto D., Computer-aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images, Algorithms, 2009, 2(3), 925-952, 2009.07.
51. Yoshiura T, Noguchi T, Hiwatashi A, Togao O, Yamashita K, Nakao T, Nagao E, Kumazawa S, Arimura H, Honda H., Age-Related Microstructural Changes in Subcortical White Matter During Postadolescent Periods in Men Revealed by Diffusion-Weighted MR Imaging., Human Brain Mapping, 30(10):3142-50, 2009.10.
52. Arimura H, Egashira Y, Shioyama Y, Nakamura K, Yoshidome S, Anai S, Nomoto S, Honda H, Toyofuku F, Higashida Y, Onizuka Y, Terashima H., Computerized method for estimation of the location of a lung tumor on EPID cine images without implanted markers in stereotactic body radiotherapy, Physics in Medicine and Biology, 54, 665-677, 2009.02.
53. Yoshidome S, Arimura H, Kakeda S, Korogi Y, Tsuchiiya K, Katsuragawa S, Doi K, Performance evaluation of a CAD scheme for detection of intracranial aneurysms in MRA images based on a cross-validation test with four different databases. , International Journal of Computer Assisted Radiology and Surgery(CARS) , Vol.3(Suppl 1), S203-204, 2008.07.
54. Yamamoto D, Arimura H, Kakeda S, Magome T, Yamashita Y, Ohki M, Toyofuku F, Higashida Y, Korogi Y, Computer-aided detection of multiple sclerosis lesions based on three types of blain MR images, International Journal of Computer Assisted Radiology and Surgery(CARS), Vol.3(Suppl 1), S202-203, 2008.07.
55. Arimura H, Yoshiura T, Kumazawa S, Mihara F, Honda H, TanakaK, Koga H, Sakai S, Toyofuku F, Higashida Y., Computerized method for classification of patients with Alzheimer's disease based on segmentation of serebral cortical regions including hippocampal regions. , International Journal of Computer Assisted Radiology and Surgery(CARS), Vol.3(Suppl1), S201-202, 2008.07.
56. Yamashita Y, Arimura H, Tsuchiya K., Computer-aided Detection of Ischemic Lesions related to Subcortical Vascular Dementia on Magnetic Resonance Images., Academic Radiology, 15:978-985, 2008.06.
57. Yoshiura T, Kumazawa S, Noguchi T, Hiwatashi A, Togao O, Yamashita K, Arimura H, Higashida Y, Toyofuku F, Mihara F, Honda H., MR Tractography Based on Directional Diffusion Function:Validation in Somatotopic Organization of the Pyramidal Tract., Academic Radiology, Vol. 15, Issue 2, 186-192 , 2008.03.
58. Kakeda S, Korogi Y, Arimura H, Hirai T, Katsuragawa S, Aoki T, Doi K., Diagnostic Accuracy and Reading Time to Detect Intracranial Aneurysms on MR Angiography Using a Computer-Aided Diagnosis System., American Journal of Roentgenology, Vol. 190, 459-465, 2008.03.
59. Arimura H, Yoshiura, Kumazawa S, Tanaka K, Koga H, Mihara F, Honda H, Sakai S, Toyofuku F, Higashida Y., Automated Method for Identification of Patients With Alzheimer’s Disease Based on Three-dimensional MR Images., Academic Radiology, Vol.15, No.3, 274-284, 2008.03.
60. Yamashita K, Yoshiura T, Arimura H, Yamashita Y, Mihara F, Noguchi T, Hiwatashi A, Togao O, Kumazawa S, Higashida Y, Honda.H., Performance evaluation of radiologists with artificial neural network for differential diagnosis of intra-axial cerebral tumors on MR images., American Journal Neuroradiology, 29:1153-1158, 2008.03.
61. Arimura H, Yoshiura T, Kumazawa S, Tanaka K, Koga H, Mihara F, Honda H, Sakai S, Toyofuku F, Higashida Y., Computer-aided diagnostic method for classification of Alzheimer's disease with atrophic image features on MR images., SPIE Proceedings, Vol.6915, 69151P-1-8, 2008.02.
62. Toyofuku F, Tokumori K, Higashida Y, Arimura H, Morishita J, Ohki M., Bone mineral imaging using a digital magnification mammography system., SPIE Proceedings, Vol.6915, 691358-1-8, 2008.02.
63. Arimura H, Yoshidome S, Anai S, Shioyama Y, Nakamura K, Nomoto S, Honda H, Onizuka Y, Terashima H., Automated Method for Recognition of a Tumor Displacement on EPID Without Implanted Markers in Stereotactic Radiotherapy., International Journal of Radiation Oncology Biology Physics, Vol.69, Issue3, S685-S686, 2007.11.
64. Arimura H, Yoshiura T, Kumazawa S, Tanaka T, Koga H, Sakai S, Mihara F, Honda H, Toyofuku F, Higashida Y., SVM-based Method for Detection of Alzheimer’s Disease with Atrophic Image Features on MR images, Proceedings of ISICE, 231-234, 2007.09.
65. Arimura H, Yamashita Y, Tsuchiya K., Computerized Detection of Ischemic Lesions related to Subcortical Vascular Dementia on Magnetic Resonance Images, Proceedings of ISICE 2007, 235-238, 2007.09.
66. Arimura H, Anai S, Yoshidome S, Nakamura K, Shioyama Y, Nomoto S, Honda H, Onizuka Y, Terashima H., Computerized method for measurement of displacement vectors of target positions on EPID cine images in stereotactic radiotherapy, SPIE Proceedings, Vol.6512, 65121U-1-8, 2007.05.
67. Kumazawa S, Yoshiura T, Arimura H, Mihara F, Honda H, Higashida Y, Toyofuku F., Estimation of white matter connectivity based on a three-dimensional directional diffusion function in diffusion tensor MRI, Medical Physics, Vol.33, No.12, 4643-4652, 2006.12.
68. Kumazawa S, Yoshiura T, Arimura H, Mihara F, Honda H, Higashida Y, Toyofuku F., Estimation of white matter tracts based on a directional diffusion function in DT-MRI, IFMBE Proceedings, Vol.14, 2433-2436, 2006.08.
69. Arimura H, Yoshiura T, Kumazawa S, Koga H, Sakai S, Mihara F, Honda H, Toyofuku F, Higashida Y., Automated Volumetric Measurement of Cerebrospinal Fluid in Sulci and Lateral Ventricles Based on 3-D MR images, IFMBE Proceedings, Vol.14, 2184-2187, 2006.08.
70. Kumazawa S, Yoshiura T, Arimura H, Mihara F, Honda H, Higashida Y, Toyofuku F., Visualization of white matter tracts using a directional diffusion function based tractography in DT-MRI, International Journal of Computer Assisted Radiology and Surgery, Vol.1,Supplement,1, 7-9, 2006.06.
71. Arimura H, Li Q, Korogi Y, Hirai T, Katsuragawa S, Yamashita Y, Tsuchiya K, Doi K., Improvement and evaluation of computerized method for detection of intracranial aneurysms for 3-D MR angiography, International Journal of Computer Assisted Radiology and Surgery, Vol.1,Supplement,1, 385-386, 2006.06.
72. Arimura H, Yoshiura T, Kumazawa S, Koga H, Sakai S, Mihara F, Honda H, Ohki M, Toyofuku F, Higashida Y., Automated method for measurement of cerebral cortical thickness for 3-D MR Images, International Journal of Computer Assisted Radiology and Surgery, Vol.1,Supplement,1, 19-20, 2006.06.
73. Arimura H, Yoshiura T, Kumazawa S, Koga H, Sakai S, Mihara F, Honda H, Ohki M, Toyofuku F, Higashida Y., Computerized Method for Automated Measurement of Thickness of Cerebral Cortex for 3-D MR Images, SPIE Proceedings, Vol.6144, 1239-1246, 2006.05.
74. Kumazawa S, Yoshiura T, Arimura H, Mihara F, Honda H, Higashida Y, Toyofuku F., White Matter Fiber Tractography based on a Directional Diffusion Field in Diffusion Tensor MRI, SPIE Proceedings, Vol.6144, 1260-1267, 2006.05.
75. Arimura H, Li Q, Korogi Y, Hirai T, Katsuragawa S, Yamashita Y, Tsuchiya K, Doi K, Computerized detection of intracranial aneurysms for 3D MR angiography:Feature extraction of small protrusions based on a shape-based difference image technique, Medical Physics, Vol.33, No.2, 394-401, 2006.02.
76. Li F, Arimura H, Suzuki K, Shiraishi J, Li Q, Abe H, Engelmann R, Sone S, MacMahon H, Doi K, Computer-Aided Detection for Peripheral Lung Cancers Missed at CT: ROC Analyses without and with localization, Radiology, 10.1148/radiol.2372041555, 237, 2, 684-690, Vol. 237, pp. 684 –690, 2005.11.
77. Hirai T, Korogi Y, Arimura H, Katsuragawa S, Kitajima M, Yamura M, Yamashita Y, Doi K, Intracranial Aneurysms at MR Angiography: Effect of Computer-aided Diagnosis on Radiologists’ Detection Performance, Radiology, 10.1148/radiol.2372041734, 237, 2, 605-610, Vol. 237, pp. 605 –610, 2005.11.
78. Arimura H, Li Q, Korogi Y, Hirai T, Abe H, Yamashita Y, Katsuragawa S, Ikeda R, and Doi K, CAD Scheme for Detection of Intracranial Aneurysms in MRA based on 3D Analysis of Vessel Skeletons and Enhanced Aneurysms, SPIE Proc., 10.1117/12.593574, 5747, 967-974, Vol. 5747, pp. 967-974, 2005.05.
79. Arimura H, Li Q, Korogi Y, Hirai T, Abe H, Yamashita Y, Katsuragawa S, Ikeda R, and Doi K, Automated computerized scheme for detection of unruptured intracranial aneurysms in three-dimensional MRA, Academic Radiology, 10.1016/j.acra.2004.07.011, 11, 10, 1093-1104, Vol.11, [10], 1093-1104, 2004.10.
80. Arimura H, Katsuragawa S, Suzuki K, Li F, Shiraishi J, Sone S, and Doi K, Computerized scheme for automated detection of lung nodules in low-dose CT images for lung cancer screening, Academic Radiology, 11, [6], pp. 617-629, 2004.06.
81. Li Q, Arimura H, and Doi K, Selective Enhancement Filters for Lung Nodules, Intracranial Aneurysms, and Breast Microcalcifications, International Congress Series, 1268, pp. 929-934, 2004.06.
82. Arimura H, Li Q, Korogi Y, Hirai T, Abe H, Yamashita Y, Katsuragawa S, Ikeda R, and Doi K, Development of CAD scheme for automated detection of intracranial aneurysms in magnetic resonance angiography, International Congress Series, 10.1016/j.ics.2004.03.102, 1268, 1015-1020, 1268, pp. 1015-1020, 2004.06.
83. Kubota H, Asai Y, Ozaki Y, Arimura H, Matsumoto M, and Kanamori H, Edge enhancement effect of vision on X-ray radiographs made using screen-film systems, Imaging Science Journal, 51, 13-20, 2003.01.
84. Arimura H, Katsuragawa S, Li Q, Ishida T, and Doi K, Development of a computerized method for identifying the posteroanterior and lateral views of chest radiographs by use of a template matching technique, Medical Physics, 29, [7], 1556-1561, 2002.07.
85. H. Arimura, S. Katsuragawa, T. Ishida, N. Oda, H. Nakata, and K. Doi, Performance evaluation of an advanced method for automatic identification
of view positions of chest radiographs by use of a large database, SPIE Proceedings, 4684, pp. 308-315, 2002.05.
86. I. Kawashita, K. Maeda, H. Arimura, K. Morikawa, T. Ishida, Development of an Automated method for Evaluation of sharpness of Digital radiographs using edge method, SPIE Proceedings, 4320 pp.1〜8, 2001.05.
87. K. Maeda, H. Arimura, I. Kawashita, K. Morikawa, T. Ishida, H. Kanamori, and M. Matsumoto, Influence of scattered x rays on the sharpness of image signal produced by a CR system, SPIE Proceedings, 4682, pp. 675-682, 2002.05.
88. Arimura H, Date T, Morikawa K, Kubota H, Matsumoto M, and Kanamori H, Effect of scattered x rays on image signal of radiograph, Proceedings of 10th International Congress of The International Radiation Protection Association, P-7-33: pp. 1-7, 2000.05.
89. H. Arimura, T. Date, K. Morikawa, H. Kubota, M. Matsumoto, H. Kanamori, Influence of the scattered x-rays on object sharpness of radiograph, SPIE Proceedings, 3977, pp.506〜513, 2000.05.
90. Arimura H, Kubota H, Matsumoto M, and Kanamori H, Proportionality between Wiener spectra of quantum mottle and the squares of modulation transfer functions, Physics in Medicine and Biology, 4, [5], pp.1337〜1352, 1999.05.