Neural architecture search with Deep Radon Prior for sparse-view CT image reconstruction.

Journal: Medical physics
Published Date: Feb 10, 2025

Abstract

BACKGROUND: Sparse-view computed tomography (CT) reduces radiation exposure but suffers from severe artifacts caused by insufficient sampling and data scarcity, which compromise image fidelity. Recent advancements in deep learning (DL)-based methods for inverse problems have shown promise for CT reconstruction but often require high-quality paired datasets and lack interpretability.

Authors

  • Jintao Fu
    Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China.
  • Peng Cong
    Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China.
  • Shuo Xu
    College of Economics and Management, Shandong University of Science and Technology, Qingdao 266590, China.
  • Jiahao Chang
    Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China.
  • Ximing Liu
    Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China.
  • Yuewen Sun
    Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China.

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