TomoGRAF: An X-ray physics-driven generative radiance field framework for extremely sparse view CT reconstruction.

Journal: PloS one
Published Date: Aug 22, 2025

Abstract

OBJECTIVES: Computed tomography (CT) provides high spatial-resolution visualization of 3D structures for various applications. Traditional analytical/iterative CT reconstruction algorithms require hundreds of angular samplings, a condition may not be met practically for physical and mechanical limitations. Sparse view CT reconstruction has been proposed using constrained optimization and machine learning methods with varying success, less so for ultra-sparse view reconstruction. Neural radiance field (NeRF) is a powerful tool for reconstructing and rendering 3D natural scenes from sparse views, but its direct application to 3D medical image reconstruction has been minimally successful due to the differences in photon transportation and available prior information between optic and X-ray.

Authors

  • Di Xu
    School of Chemistry and Chemical Engineering, Chongqing University of Science & Technology, Chongqing, 401331, China. xdcq86@163.com.
  • Yang Yang
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
  • Hengjie Liu
    Radiation Oncology, University of California, Los Angeles, United States.
  • Qihui Lyu
    Department of Radiation Oncology, University of California Los Angeles, Los Angeles, CA 90095, United States of America.
  • Martina Descovich
    UCSF Department of Radiation Oncology, San Francisco, California 94115.
  • Dan Ruan
    Departments of Radiation Oncology, Biomedical Physics and Bioengineering, UCLA, Los Angeles, CA, 90095, USA.
  • Ke Sheng
    Department of Radiation Oncology, University of California-Los Angeles, Los Angeles, CA, 90095, USA.

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