ADMM-TransNet: ADMM-Based Sparse-View CT Reconstruction Method Combining Convolution and Transformer Network.

Journal: Tomography (Ann Arbor, Mich.)
PMID: 40137562

Abstract

BACKGROUND: X-ray computed tomography (CT) imaging technology provides high-precision anatomical visualization of patients and has become a standard modality in clinical diagnostics. A widely adopted strategy to mitigate radiation exposure is sparse-view scanning. However, traditional iterative approaches require manual design of regularization priors and laborious parameter tuning, while deep learning methods either heavily depend on large datasets or fail to capture global image correlations.

Authors

  • Sukai Wang
    School of Computer Science and Technology, North University of China, Taiyuan 030051, China.
  • Xueqin Sun
    Shanxi Key Laboratory of Intelligent Detection Technology and Equipment, North University of China, Taiyuan 030051, China.
  • Yu Li
    Department of Public Health, Shihezi University School of Medicine, 832000, China.
  • Zhiqing Wei
    Shanxi Key Laboratory of Intelligent Detection Technology and Equipment, North University of China, Taiyuan 030051, China.
  • Lina Guo
    Shanxi Key Laboratory of Intelligent Detection Technology and Equipment, North University of China, Taiyuan 030051, China.
  • Yihong Li
    Department of Data Science and Big Data Technology, Hainan University, Haikou 570228, China.
  • Ping Chen
    Department of Infectious Diseases, Renmin Hospital of Wuhan University, Wuhan 430060, China.
  • Xuan Li
    College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, China.

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