Deep learning‐based Monte Carlo dose prediction for heavy‐ion online adaptive radiotherapy and fast quality assurance: A feasibility study
By extracting relevant parameters of radiotherapy plans, the CAM-CHD U-Net model can directly and accurately predict independent MC dose, and has a high gamma passing rate with the ground truth dose (the dose obtained after a complete MC simulation). Our workflow enables the implementation of heavy ion OART, and the predicted MCDose can be used for rapid QA of HIT.
Authors
- Ying Luo (ORCID: https://orcid.org/0000-0002-3987-6961)
- Haibo Peng (ORCID: https://orcid.org/0000-0001-9117-0854)
- Xinyang Zhang (ORCID: https://orcid.org/0009-0003-7419-328X)
- Yuanyuan Ma (ORCID: https://orcid.org/0000-0003-2146-7040)
- Yiheng Liu (ORCID: https://orcid.org/0000-0002-8201-7943)
- Qiang Li (ORCID: https://orcid.org/0000-0003-0096-7679)
- Hui Zhang (ORCID: https://orcid.org/0000-0001-6825-5649)
- Jian Wang (ORCID: https://orcid.org/0000-0003-3742-9671)
- Yazhou Li (ORCID: https://orcid.org/0000-0002-7780-2998)
- Wei Wu (ORCID: https://orcid.org/0000-0001-9382-4946)
- Pengbo He
- Xinguo Liu (ORCID: https://orcid.org/0000-0002-2084-4310)
- Rui He (ORCID: https://orcid.org/0000-0003-4494-9106)
Institutions
- Chinese Academy of Sciences (CN)
- Lanzhou University of Technology (CN)
- Gansu Provincial Hospital (CN)
- Institute of Modern Physics (CN)
- University of Chinese Academy of Sciences (CN)
- Putian University (CN)
- Xi'an Jiaotong University (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-01-27
- DOI
- https://doi.org/10.1002/mp.17628
- Citations
- 3
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 5.65
Funders
- National Key Research and Development Program of China