PIPN: Physics-inspired phase retrieval network for propagation-based X-ray phase-contrast imaging.

Journal: Optics letters
Published Date: Jan 15, 2026

Abstract

Propagation-based X-ray phase-contrast imaging (PB-XPCI) can produce high-resolution images of soft tissue. However, this usually requires extracting the phase shift from intensity measurement at a single propagation distance through phase retrieval-an underdetermined nonlinear inverse problem. Conventional single-distance phase retrieval methods usually rely on multiple approximation conditions. Deep learning (DL)-based phase retrieval methods often rely on high-quality data for training or lengthy physics model iterative computations to optimize network parameters. In order to surmount the aforementioned limitations, this study proposes a physics-inspired phase retrieval network for propagation-based X-ray phase-contrast imaging (PIPN) and an acceleration strategy for the PIPN. It can achieve phase retrieval based solely on a single approximation condition and a physics imaging model, without the need for any training data. Experiments demonstrate that the PIPN can quickly reconstruct high-quality retrieved phase projections by using the acceleration strategy, and remain stable under different propagation distances.

Authors

  • Ziyao Wang
    Department of Ophthalmology, Peking University Third Hospital, Beijing, China.
  • Xianqin Du
  • Yimin Li
    Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States.
  • Fangzhi Li
  • Chenyang Ma
    Shanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an, 710121, China; School of Computer Science & Technology, Xi'an University of Post & Telecommunications, Xi'an, 710121, China.
  • Dongjiang Ji
  • Xinyan Zhao
    School of Information, University of Michigan, Ann Arbor, Michigan, USA.
  • Yingran Tang
  • Chunhong Hu
    Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Yuqing Zhao
    Faculty of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming, Yunnan 650201, PR China. Electronic address: [email protected].

Keywords

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