A physics-informed deep learning model for predicting beam dose distribution of intensity-modulated radiation therapy treatment plans
This study proposes a physics-informed deep learning network specifically for the task of beam dose prediction. Additionally, this study addresses the interpretability challenges in deep learning models by employing a crosshair sampling scheme to validate the relationships between input and output channels.
Authors
- Guorong Yao (ORCID: https://orcid.org/0000-0001-6856-1910)
- Jiazhou Wang (ORCID: https://orcid.org/0000-0002-2301-2034)
- Senxiang Yan (ORCID: https://orcid.org/0000-0001-7237-0826)
- Yuan‐Hua Chen
- Zhongjie Lu (ORCID: https://orcid.org/0000-0002-9648-1931)
- Zihan Sun (ORCID: https://orcid.org/0009-0001-2110-3856)
- Weigang Hu
Institutions
- Shanghai Medical College of Fudan University (CN)
- Fudan University Shanghai Cancer Center (CN)
- First Affiliated Hospital Zhejiang University (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1016/j.phro.2025.100779
- Citations
- 1
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.88
Funders
- National Natural Science Foundation of China