ISSN 1004-4140
CN 11-3017/P
Volume 31 Issue 3
May  2022
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WANG Z T, MAO T Y, ZHANG X, et al. Coded aperture computed tomography via generative adversarial U-net[J]. CT Theory and Applications, 2022, 31(3): 317-327. DOI: 10.15953/j.ctta.2021.070. (in Chinese).
Citation: WANG Z T, MAO T Y, ZHANG X, et al. Coded aperture computed tomography via generative adversarial U-net[J]. CT Theory and Applications, 2022, 31(3): 317-327. DOI: 10.15953/j.ctta.2021.070. (in Chinese).
shu

Coded Aperture Computed Tomography Via Generative Adversarial U-net

  • 1.

    Nanjing University of Posts and Telecommunications, Nanjing 210003, China School of Communications and Information engineering

  • 2.

    Nanjing University of Posts and Telecommunications, Nanjing 210003, China School of Geographic and Biologic Information

  • 2.

    Department of Neurosurgery, Rugao People’s Hospital, Rugao 226500, China

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  • Received Date: December 19, 2021
  • Accepted Date: February 24, 2022
  • Available Online: March 09, 2022
  • Published Date: May 22, 2022
    Graphical Abstract
    • Abstract
  • Generative adversarial U-net for coded aperture computed tomography (CT) is proposed in this paper to alleviate the tradeoff between the non-continuous sparse projections and the ill-posedness iterative reconstruction problem. A non-continuous sparse projection model is presented based on generative adversarial U-net and the corresponding joint penalty function is formulated. Simulations using real datasets show that CT images with 256×256 pixels can be reconstructed with peak signal-to-noise ration more than 30 dB at only 5% transmittance. Furthermore, the computational time in the reconstructions is reduced by two orders of magnitude when compared with the state-of-the-art iterative algorithms in coded aperture computed tomography.
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    • References (24)
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