Using a patient‐specific diffusion model to generate CBCT‐based synthetic CTs for CBCT‐guided adaptive radiotherapy
Our proposed PFS-LDMs significantly improve CBCT image quality with increased HU accuracy and fewer artifacts, thus better capturing inter-fraction anatomical changes. This lays the groundwork for enabling CBCT-based ART, which could enhance clinical efficiency and achieve personalized high-precision treatment by accounting for inter-fraction anatomical changes.
Authors
- Joseph W. Shelton (ORCID: https://orcid.org/0000-0003-0426-8197)
- Richard L. J. Qiu (ORCID: https://orcid.org/0000-0002-7877-1900)
- Xuxin Chen (ORCID: https://orcid.org/0000-0001-8872-8992)
- Tonghe Wang (ORCID: https://orcid.org/0000-0001-9021-1204)
- Aparna H. Kesarwala (ORCID: https://orcid.org/0000-0001-8297-9643)
- Chih‐Wei Chang (ORCID: https://orcid.org/0000-0002-3818-4381)
- Xiaoqian Chen (ORCID: https://orcid.org/0000-0002-2673-2977)
- Xiaofeng Yang
Institutions
- Memorial Sloan Kettering Cancer Center (US)
- Emory University (US)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1002/mp.17463
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.62
Funders
- Memorial Sloan-Kettering Cancer Center
- National Institutes of Health