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

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第一作者： Sho,Ozaki

第一单位： Graduate School of Medicine, University of Tokyo, Tokyo, Japan.

作者： Sho,Ozaki ^[1] ; Shizuo,Kaji ^[2] ; Kanabu,Nawa ^[3] ; Toshikazu,Imae ^[3] ; Atsushi,Aoki ^[3] ; Takahiro,Nakamoto ^[4] ; Takeshi,Ohta ^[3] ; Yuki,Nozawa ^[3] ; Hideomi,Yamashita ^[3] ; Akihiro,Haga ^[5] ; Keiichi,Nakagawa ^[1]

作者单位： Graduate School of Medicine, University of Tokyo, Tokyo, Japan. ^[1] Institute of Mathematics for Industry, Kyushu University, Motooka, Nishi-ku, Fukuoka, Japan. ^[2] Department of Radiology, University of Tokyo Hospital, Tokyo, Japan. ^[3] Department of Biological Science and Engineering, Faculty of Health Sciences, Hokkaido University, Kita-ku, Sapporo, Hokkaido, Japan. ^[4] Graduate School of Biomedical Science, Tokushima University, Tokushima, Japan. ^[5]

关键词 computed tomography cross-modality conversion deep learning training data reduction

医学主题词锥束计算机体层摄影术(Cone-Beam Computed Tomography);头颈部肿瘤(Head and Neck Neoplasms);人类(Humans);图像处理, 计算机辅助(Image Processing, Computer-Assisted);放射治疗计划, 计算机辅助(Radiotherapy Planning, Computer-Assisted);体层摄影术, X线计算机(Tomography, X-Ray Computed)

DOI 10.1002/mp.15626

PMID 35315529

发布时间 2022-06-10

基金项目

19K08093/Japan Society for the Promotion of Science 20K08073/Japan Society for the Promotion of Science 21K12121/Japan Society for the Promotion of Science 18K15625/Japan Society for the Promotion of Science 19K08201/Japan Society for the Promotion of Science 20H04278/Japan Society for the Promotion of Science

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