Deep Radon Prior: A fully unsupervised framework for sparse-view CT reconstruction.

Journal: Computers in biology and medicine
PMID: 40056836

Abstract

BACKGROUND: Sparse-view computed tomography (CT) substantially reduces radiation exposure but often introduces severe artifacts that compromise image fidelity. Recent advances in deep learning for solving inverse problems have shown considerable promise in enhancing CT reconstruction; however, most approaches heavily rely on high-quality training datasets and lack interpretability.

Authors

  • Shuo Xu
    College of Economics and Management, Shandong University of Science and Technology, Qingdao 266590, China.
  • Jintao Fu
    Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China.
  • Yuewen Sun
    Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China.
  • Peng Cong
    Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China.
  • Xincheng Xiang
    Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China; Beijing Key Laboratory of Nuclear Detection Technology, Beijing, China. Electronic address: inetxxc@tsinghua.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals