Optimization of sparse-view CT reconstruction based on convolutional neural network.

Journal: Medical physics
PMID: 39894762

Abstract

BACKGROUND: Sparse-view CT shortens scan time and reduces radiation dose but results in severe streak artifacts due to insufficient sampling data. Deep learning methods can now suppress these artifacts and improve image quality in sparse-view CT reconstruction.

Authors

  • Liangliang Lv
    School of Nuclear Science and Technology, Lanzhou University, Lanzhou, China.
  • Chang Li
    Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China.
  • Wenjing Wei
    School of Nuclear Science and Technology, Lanzhou University, Lanzhou, China.
  • Shuyi Sun
    Department of Industrial Engineering and Management, Business School, Sichuan University, Chengdu 610064, China; Department of Industrial Systems Engineering & Management, National University of Singapore, Singapore 119260, Singapore.
  • Xiaoxuan Ren
    Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States.
  • Xiaodong Pan
    School of Nuclear Science and Technology, Lanzhou University, Lanzhou, China.
  • Gongping Li
    School of Nuclear Science and Technology, Lanzhou University, Lanzhou, China.

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