X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module.

Journal: Journal of biomedical optics
PMID: 36818584

Abstract

SIGNIFICANCE: X-ray Cherenkov-luminescence tomography (XCLT) produces fast emission data from megavoltage (MV) x-ray scanning, in which the excitation location of molecules within tissue is reconstructed. However standard filtered backprojection (FBP) algorithms for XCLT sinogram reconstruction can suffer from insufficient data due to dose limitations, so there are limits in the reconstruction quality with some artifacts. We report a deep learning algorithm for XCLT with high image quality and improved quantitative accuracy.

Authors

  • Jinchao Feng
    Beijing Univ. of Technology, China.
  • Hu Zhang
    Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China.
  • Mengfan Geng
    Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing, China.
  • Hanliang Chen
    Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing, China.
  • Kebin Jia
    College of Information and Communication Engineering, Beijing University of Technology, Beijing, China. kebinj@bjut.edu.cn.
  • Zhonghua Sun
    Beijing Univ. of Technology, China.
  • Zhe Li
  • Xu Cao
  • Brian W Pogue
    Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin.

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