Deep Radon Prior: A fully unsupervised framework for sparse-view CT reconstruction
The introduction of DRP represents a significant advancement in sparse-view CT imaging by leveraging the inherent deep self-correlation of the Radon domain, enabling effective cooperation with neural network manifolds for image reconstruction. This paradigm shift toward fully unsupervised learning offers a scalable and insightful approach to medical imaging, potentially redefining the landscape of CT reconstruction.
Authors
- Shuo Xu (ORCID: https://orcid.org/0009-0001-2827-3858)
- Yuewen Sun (ORCID: https://orcid.org/0009-0007-1512-8188)
- Xincheng Xiang (ORCID: https://orcid.org/0009-0003-2067-7115)
- Peng Cong
- J. L. Fu (ORCID: https://orcid.org/0009-0000-1860-6092)
Institutions
- University of Science and Technology of China (CN)
- Key Laboratory of Nuclear Radiation and Nuclear Energy Technology (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2025-03-09
- DOI
- https://doi.org/10.1016/j.compbiomed.2025.109853
- Citations
- 4
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- Tsinghua University