| Citation: | HAN X L，SHANGGUAN H，ZHANG X，et al. A low-dose CT image denoising method based on artifact estimation[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（2）：491-502 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0263
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Low-dose CT (LDCT) contains abundant tissue structure, pathological information and noise artifacts with extremely irregular distribution.These two different types of information have comparable amplitude distributions. Therefore, the LDCT denoising task is prone to some problems, such as insufficient feature extraction, insufficient network sensitivity to the directional characteristics of noise artifacts, and excessive smoothing of the denoising results. In response to the above problems, this work uses the U-Net network as the basic model of the denoising network, and designs a LDCT denoising network based on artifact estimation. The proposed network mainly includes two parts: the main feature extraction network and the direction-sensitive attention sub-network.Firstly, to better use the differences between various scale features and increase the efficiency of feature extraction, we add a dense feature improvement module to the codec U-Net structure. Secondly, we design a direction-sensitive attention subnetwork to improve the sensitivity of the denoising network to the direction characteristics of the noise artifacts. Finally, to ensure the stability of network training, we utilize a variety of loss functions to optimize the network training process. The experimental results show that the proposed algorithm is superior to other mainstream LDCT denoising algorithms in terms of visual effects and quantitative indicators.
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