Deep learning-based prediction of Monte Carlo dose distribution for heavy ion therapy
The proposed CHD U-Net model can predict MCDose in a few seconds and outperforms two alternative DL models. The predicted dose can replace TPSDose in HIT clinical process due to its MC simulation accuracy, thus improving the accuracy of dose calculation and providing a valuable reference for quality assurance.
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)
- Guosheng Shen
- Hui Zhang (ORCID: https://orcid.org/0000-0001-6825-5649)
- Jian Wang (ORCID: https://orcid.org/0000-0003-3742-9671)
- Qiang Li (ORCID: https://orcid.org/0000-0002-0618-5515)
- Yazhou Li (ORCID: https://orcid.org/0000-0002-7780-2998)
- 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)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2025-03-05
- DOI
- https://doi.org/10.1016/j.phro.2025.100735
- Citations
- 2
- Primary Topic
- Radiation Therapy and Dosimetry
- Type
- article
- Field-Weighted Citation Impact
- 3.04