Comparison of methodological uncertainties in tissue parameter estimation for carbon ion dose calculation
By more accurately modeling the relationship between CT numbers and tissue parameters, ML-DECT substantially reduces uncertainty in both input estimation and subsequent dose calculation. These findings support its integration into Monte Carlo-based carbon ion treatment planning workflows, contributing to more quantitative and uncertainty-aware quality assurance.
Authors
- Mingqing Wang (ORCID: https://orcid.org/0000-0003-1933-1566)
- Yibao Zhang (ORCID: https://orcid.org/0000-0003-3658-0687)
- Yan Li (ORCID: https://orcid.org/0000-0002-1768-7246)
- Shutong Yu
- Chong Xu
- Lisi Geng
- Cheng Chang
- Chao Yang
- Yan Chen
- Weiguang Li
- Kai‐Wen Li
Institutions
- Peking University (CN)
- China Aerospace Science and Technology Corporation (CN)
- Mianyang Third People's Hospital (CN)
- Peking University Cancer Hospital (CN)
- Zhejiang Cancer Hospital (CN)
- Institute of Modern Physics (CN)
- Peking University Third Hospital (CN)
- Beihang University (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-12-25
- DOI
- https://doi.org/10.1002/mp.70252
- Primary Topic
- Radiation Therapy and Dosimetry
- Type
- article
- Field-Weighted Citation Impact
- 0.00