Development and evaluation of deep learning models for estimating the organ at‐risk dose constraint from two‐dimensional cine magnetic resonance imaging scans during irradiation
Our results demonstrated that deep learning can effectively predict the OAR dose constraints during irradiation. However, it is noteworthy that these results show only a limited improvement and are constrained by several limitations.
Authors
- Keiichi Jingu (ORCID: https://orcid.org/0000-0002-7032-1577)
- Noriyoshi Takahashi (ORCID: https://orcid.org/0000-0002-7653-0046)
- Wingyi Lee
- Hisamichi Takagi (ORCID: https://orcid.org/0000-0003-2389-2577)
- Yoshiyuki Katsuta (ORCID: https://orcid.org/0000-0001-5984-9226)
- Shohei Tanaka (ORCID: https://orcid.org/0000-0002-4257-5342)
- Noriyuki Kadoya (ORCID: https://orcid.org/0000-0001-5018-3800)
- Kazuhiro Arai
- T. Hoshino (ORCID: https://orcid.org/0009-0004-3716-1817)
- Yushan Xiao
Institutions
- Tohoku University (JP)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1002/acm2.70403
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Japan Society for the Promotion of Science