Uncertainty quantification for CT dosimetry based on 10 281 subjects using automatic image segmentation and fast Monte Carlo calculations
Using newly available computational tools, this study has demonstrated the feasibility of conducting big data analysis towards CT dose data mining and uncertainty quantification. Results show that inter-individual variability is significant and can be taken into account in an effort to improve CT dosimetry.
Authors
- Zirui Ye (ORCID: https://orcid.org/0000-0002-6338-5484)
- Xie George Xu (ORCID: https://orcid.org/0000-0002-9512-0712)
- Bei Yao
- Yankui Chang (ORCID: https://orcid.org/0000-0001-9814-0332)
- Ripeng Wang
- Wei Wei (ORCID: https://orcid.org/0000-0003-4097-1592)
- Tao Li (ORCID: https://orcid.org/0000-0001-6689-2951)
- Yingming Zhao
- Zhi Chen (ORCID: https://orcid.org/0000-0002-2725-6239)
- Haoran Zheng (ORCID: https://orcid.org/0000-0001-7442-6299)
Institutions
- University of Science and Technology of China (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1002/mp.17796
- Citations
- 1
- Primary Topic
- Radiation Dose and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Natural Science Foundation of China
- University of Science and Technology of China
- Natural Science Foundation of Tibet Autonomous Region