A machine learning based dual‐energy CT elemental decomposition method and its physical‐biological impacts on carbon ion therapy
The ML-DECT method provided precise estimation of physical density and elemental mass ratios of human tissues. Compared with the PA-DECT method, the ML-DECT method displayed stronger robustness to image noise.
Authors
- Li‐Sheng Geng (ORCID: https://orcid.org/0000-0002-5626-0704)
- Cheng Chang (ORCID: https://orcid.org/0000-0002-0361-2438)
- Yibao Zhang (ORCID: https://orcid.org/0000-0003-3658-0687)
- Chao Yang (ORCID: https://orcid.org/0000-0002-9024-4177)
- Chong Bang Xu (ORCID: https://orcid.org/0000-0002-3956-4925)
- Weiguang Li
- Kai‐Wen Li (ORCID: https://orcid.org/0000-0001-6603-9205)
- Yan Li (ORCID: https://orcid.org/0000-0002-6336-0981)
- Shutong Yu
- Mingqing Wang (ORCID: https://orcid.org/0009-0003-9988-1337)
Institutions
- International Atomic Energy Agency (AT)
- Peking University (CN)
- China Aerospace Science and Technology Corporation (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
- 2025-09-01
- DOI
- https://doi.org/10.1002/mp.18082
- Citations
- 1
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.67