RAN R S,ZHANG S W,LI J,et al. A noise reduction method of low-dose CT images based on feature fusion[J]. Microelectronics & Computer,2024,41(5):11-21. doi: 10.19304/J.ISSN1000-7180.2023.0279
Citation: RAN R S,ZHANG S W,LI J,et al. A noise reduction method of low-dose CT images based on feature fusion[J]. Microelectronics & Computer,2024,41(5):11-21. doi: 10.19304/J.ISSN1000-7180.2023.0279

A noise reduction method of low-dose CT images based on feature fusion

  • Low-Dose CT (LDCT) is widely used in clinical diagnosis, but it also generates some irregular noise. The existing noise reduction methods often lack consideration of global feature information, as well as do not focus on edge feature information and the visual effect of the reconstructed image. Then, a feature fusion-based noise reduction method for low-dose CT images is proposed. Firstly, the excellent global receptive field of Transformer is used to extract the global feature information of the image, and the good local feature extraction ability of Convolutional Neural Network (CNN) is used to extract the local feature information of the image. The dimensional transformation idea is added to the Transformer module to better suppress the noise; a dense connection in CNN is used to reuse the feature information from the shallow network to the deep network, so as to achieve pre-fusion of features and preserve more feature information. An improved Sobel edge enhancement operator is used to enhance the model's ability to extract edge feature information. Finally, the feature information acquired by the Transformer module and the CNN module is fused and the reconstructed image is output. In addition, a multiscale composite loss function is designed to make the reconstructed image with better quality and visual effect after noise reduction. The experiments show that in the noise reduction experiments of AAPM-Mayo dataset, compared with the current mainstream LDCT image noise reduction methods, the method in this paper achieves a better noise reduction effect.
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