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Volume 31 Issue 6
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ZHU Y Z, LV Q W, GUAN Y, et al. Low-dose CT reconstruction based on deep energy models[J]. CT Theory and Applications, 2022, 31(6): 709-720. DOI: 10.15953/j.ctta.2021.077. (in Chinese).
Citation: ZHU Y Z, LV Q W, GUAN Y, et al. Low-dose CT reconstruction based on deep energy models[J]. CT Theory and Applications, 2022, 31(6): 709-720. DOI: 10.15953/j.ctta.2021.077. (in Chinese).
shu

Low-dose CT Reconstruction Based on Deep Energy Models

  • School of Information Engineering, Nanchang University, Nanchang 330036, China

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  • Received Date: December 22, 2021
  • Revised Date: February 13, 2022
  • Accepted Date: February 15, 2022
  • Available Online: April 29, 2022
  • Published Date: November 02, 2022
    Graphical Abstract
    • Abstract
  • Reducing the dose of computed tomography (CT) is essential for reducing the radiation risk in clinical applications. With the rapid development and wide application of deep learning, it has brought new directions for the development of low-dose CT imaging algorithms. Unlike most existing prior-driven algorithms that benefit from manually designed prior functions or supervised learning schemes, in this paper, we use an energy-based deep model to learn the prior knowledge of normal-dose CT, and then in the iterative reconstruction phase, we integrate data consistency as a conditional item into the iterative generation model of low-dose CT, and realize the low-dose CT reconstruction through the prior experience of iterative updating training of Langevin dynamics. The experimental results show that the proposed method hold excellent noise reduction and detail retention capabilities.
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    • References (34)
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    1. 吴凡，刘进，张意，陈阳，陆志凯. 面向CT成像的深度重建算法研究进展. 中国体视学与图像分析. 2022(04): 387-404 .

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