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Volume 31 Issue 6
Nov.  2022
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WEI Y L, YANG Z Y, XIA W J, et al. Low-dose CT denoising based on subspace projection and edge enhancement[J]. CT Theory and Applications, 2022, 31(6): 721-729. DOI: 10.15953/j.ctta.2022.108. (in Chinese).
Citation: WEI Y L, YANG Z Y, XIA W J, et al. Low-dose CT denoising based on subspace projection and edge enhancement[J]. CT Theory and Applications, 2022, 31(6): 721-729. DOI: 10.15953/j.ctta.2022.108. (in Chinese).
shu

Low-dose CT Denoising Based on Subspace Projection and Edge Enhancement

  • 1.

    Sichuan University, Chengdu 610065, China School of Computer Science

  • 2.

    Sichuan University, Chengdu 610065, China School of Cyber Science and Engineering

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  • Received Date: June 04, 2022
  • Accepted Date: July 12, 2022
  • Available Online: July 19, 2022
  • Published Date: November 02, 2022
    Graphical Abstract
    • Abstract
  • Low-dose computed tomography (CT) is a relatively safe method for disease screening. But low-dose CT images often contain severe noise and artifacts, which seriously affect the subsequent diagnosis. To solve this problem, this paper proposes a subspace projection and edge enhancement network (SPEENet). SPEENet hold an architecture of autoencoder, including two main modules: dual stream encoder and decoder. The dual stream encoder can be divided into two parts: noise image coding stream and edge information coding stream. The noise image coding stream removes the noise and artifacts in low-dose CT images by using the image features extracted from the low-dose CT images. The edge information coding stream mainly focuses on the edge information of low-dose CT images and fully utilize the edge information to preserve the structures. In order to make full use of the encoder features, this paper introduces the noise basis projection module to establish a basis based on the features of encoder and decoder, and uses this basis to project the features extracted by the encoder into the corresponding subspace to obtain better feature representation. In this paper, experiments are conducted on the public database to verify the effectiveness. The experimental results show that SPEENet can achieve better denoising performance than other low-dose CT denoising networks.
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    • References (30)
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