Abstract
In order to suppress noise and artifacts in low-dose computed tomography (LDCT), various deep learning techniques, especially encoder-decoder networks, have been introduced to improve the quality of LDCT images. However, in the encoder-decoder convolutional neural network, fixed-size convolution kernel, continuous down-sampling operation, and the mean square error (MSE) objective function are used, which cause problems such as low utilization of image information, image information loss, and over-smoothing of denoised image. To improve the quality of reconstructed CT images, in this paper, a LDCT image denoising network based on residual multi-scale feature extraction and hybrid loss function is proposed. On the one hand, the multi-scale feature extraction module is designed and introduced into the residual connection to improve the utilization of image feature information; on the other hand, zero padding is used to solve the information loss problem caused by continuous down-sampling operations, and batch normalization (BN) layer is used to alleviate the over-fitting problem caused by network deepening. In addition, a hybrid loss function consisting of MSE loss, structural similarity (SSIM) loss, and perceptual loss is introduced to generate denoised images with high relevance to human perception. Experimental results show that the proposed algorithm can not only improve the quality of denoised images, but also greatly improve the computational speed compared with the state of the art algorithms.
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Funding
This work was supported in part by the Natural Science Foundation of Shanxi Province under Grant 202203021222015, in part by the State Council and the central government guide local funds of China under Grant YDZX20201400001547, in part by the postgraduate practice and innovation program of Shanxi Province under Grant 2023SJ011.
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AMH develop the idea and accomplished the manuscript writing and performed the experiments. All authors accomplished the manuscript revising. All authors read and approved the final manuscript.
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Jia, L., Huang, A., He, X. et al. A residual multi-scale feature extraction network with hybrid loss for low-dose computed tomography image denoising. SIViP 18, 1215–1226 (2024). https://doi.org/10.1007/s11760-023-02809-3
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DOI: https://doi.org/10.1007/s11760-023-02809-3