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A physics-informed deep learning model for predicting beam dose distribution of intensity-modulated radiation therapy treatment plans.

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第一作者： Zihan,Sun

第一单位： Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.;Zhejiang University Cancer Center, Hangzhou, China.

作者： Zihan,Sun ^[1] ; Yongheng,Yan ^[1] ; Yuanhua,Chen ^[1] ; Guorong,Yao ^[1] ; Jiazhou,Wang ^[2] ; Weigang,Hu ^[2] ; Zhongjie,Lu ^[1] ; Senxiang,Yan ^[1]

作者单位： Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.;Zhejiang University Cancer Center, Hangzhou, China. ^[1] Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.;Department of Oncology, Shanghai Medical College Fudan University, Shanghai 200032, China.;Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, China. ^[2]

关键词 Beam dose prediction Interpretable deep-learning model Physics-informed network

DOI 10.1016/j.phro.2025.100779

PMID 40502721

发布时间 2025-06-13

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Physics and imaging in radiation oncology

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A physics-informed deep learning model for predicting beam dose distribution of intensity-modulated radiation therapy treatment plans.

Physics and imaging in radiation oncology

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Physics and imaging in radiation oncology

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A physics-informed deep learning model for predicting beam dose distribution of intensity-modulated radiation therapy treatment plans.

Physics and imaging in radiation oncology

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Physics and imaging in radiation oncology

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