Cross-modality 3D MRI synthesis via cycle-guided denoising diffusion probability model
The proposed CG-DDPM provides a robust and efficient solution for cross-modality MRI synthesis, offering improved accuracy, stability, and clinical applicability compared with existing methods. This approach has the potential to streamline MRI-based workflows, enhance diagnostic imaging, and support precision treatment planning in medical physics and radiation oncology.
Authors
- Richard L. J. Qiu (ORCID: https://orcid.org/0000-0002-7877-1900)
- Tonghe Wang (ORCID: https://orcid.org/0000-0001-9021-1204)
- Shaoyan Pan (ORCID: https://orcid.org/0009-0007-1040-0189)
- Mingzhe Hu (ORCID: https://orcid.org/0000-0001-7523-8927)
- David S. Yu (ORCID: https://orcid.org/0000-0002-2781-5379)
- Hui Mao (ORCID: https://orcid.org/0000-0002-0147-6022)
- Chih‐Wei Chang (ORCID: https://orcid.org/0000-0002-3818-4381)
- Junbo Peng (ORCID: https://orcid.org/0000-0002-5095-3016)
- Xiaofeng Yang
- Justin R. Roper (ORCID: https://orcid.org/0009-0004-1729-4444)
Institutions
- Memorial Sloan Kettering Cancer Center (US)
- Emory University (US)
- Piedmont Cancer Institute (US)
Publication Details
- Journal
- Journal of Medical Imaging
- Published
- 2025-11-24
- DOI
- https://doi.org/10.1117/1.jmi.12.6.064003
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00