Dosimetric evaluations using cycle-consistent generative adversarial network synthetic CT for MR-guided adaptive radiation therapy
Accuracy of deep learning based synthetic CT generation using setup scans on MR-Linacs was adequate for dose calculation/optimization. This can enable MR-only treatment planning workflows on MR-Linacs, thereby increasing the efficiency of simulation and adaptive planning for MRgRT.
Authors
- Gabriel L. Asher
- Temiloluwa Prioleau (ORCID: https://orcid.org/0000-0001-6750-202X)
- G.S. Gill
- Bassem I. Zaki
- Brady Hunt (ORCID: https://orcid.org/0000-0002-1143-7668)
- Rongxiao Zhang (ORCID: https://orcid.org/0000-0002-0423-7828)
- Yuting Li (ORCID: https://orcid.org/0000-0002-6716-358X)
- Yue Yan
- Charles R. Thomas
- Gregory A. Russo
- Shiru Wang (ORCID: https://orcid.org/0009-0009-1585-482X)
Institutions
- Dartmouth College (US)
- Dartmouth–Hitchcock Medical Center (US)
- The University of Texas MD Anderson Cancer Center (US)
- University of Missouri (US)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-09-29
- DOI
- https://doi.org/10.3389/fonc.2025.1672778
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Dartmouth College