ISSN 1004-4140
CN 11-3017/P
Volume 31 Issue 6
Nov.  2022
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QIAO Y Y, QIAO Z W. Low-dose CT image reconstruction method based on CNN and transformer coupling network[J]. CT Theory and Applications, 2022, 31(6): 697-707. DOI: 10.15953/j.ctta.2022.114. (in Chinese)
Citation: QIAO Y Y, QIAO Z W. Low-dose CT image reconstruction method based on CNN and transformer coupling network[J]. CT Theory and Applications, 2022, 31(6): 697-707. DOI: 10.15953/j.ctta.2022.114. (in Chinese)

Low-dose CT Image Reconstruction Method Based on CNN and Transformer Coupling Network

doi: 10.15953/j.ctta.2022.114
  • Received Date: 2022-06-12
  • Accepted Date: 2022-07-27
  • Rev Recd Date: 2022-07-26
  • Available Online: 2022-08-12
  • Publish Date: 2022-12-01
  • Under the condition that the number of projection angles is constant, reducing the radiation dose under each angle is an effective way to realize low-dose CT. However, the reconstructed images obtained through this method can be very noisy. At present, the deep learning image denoising method represented by convolutional neural networks (CNN) has become a classical method for low-dose CT image denoising. Inspired by the good performance of transformer in computer vision tasks, this paper proposes a CNN transformer coupling network (CTC) to further improve the performance of CT image denoising. CTC network makes comprehensive use of local information association ability of CNN and global information capture ability of transformer, constructs eight core network blocks composed of CNN components and an improved transformer component, which are interconnected based on residual connection mechanism and information reuse mechanism. Compared with the existing four denoising networks, CTC network demonstrate better denoising ability and can realize high-precision low-dose CT image reconstruction.

     

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