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Low-dose computed tomography (LDCT) imaging has attracted tremendous attention because it reduces the potential cancer risk for patients by decreasing the radiation dose. However, reducing the radiation dose may cause image quality degradation due to the introduction of noise and artifacts. The details of pathological information mainly exist in the high-frequency domain of LDCT image. Therefore, some useful details may be lost or destroyed while removing the noise and artifacts. To address this problem, we propose a high-frequency sensitive generative adversarial network (HFSGAN). The new generator includes two sub-networks. One is the high-frequency domain U-Net, which is specially designed to deal with the high-frequency components decomposed from LDCT image. The other is image space U-Net, which is used to process information from the whole image of LDCT. In addition, the discriminator in HFSGAN adopts an inception module to increase the receptive field and width of network, and to extract the multi-scale features of the true and false images. The experiments show that the proposed network preserves more texture details of denoised image while removing noise and artifacts. Compared with the state-of-the-art networks, the proposed denoising method achieves better performance both quantitatively and visually. INDEX TERMS Low-dose CT, image denoising, GAN, U-Net, inception module.

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BACKGROUND AND OBJECTIVE: Since low-dose computed tomography (LDCT) images typically have higher noise that may affect accuracy of disease diagnosis, the objective of this study is to develop and evaluate a new artifact-assisted feature fusion attention (AAFFA) network to extract and reduce image artifact and noise in LDCT images. METHODS: In AAFFA network, a feature fusion attention block is constructed for local multi-scale artifact feature extraction and progressive fusion from coarse to fine. A multi-level fusion architecture based on skip connection and attention modules is also introduced for artifact feature extraction. Specifically, long-range skip connections are used to enhance and fuse artifact features with different depth levels. Then, the fused shallower features enter channel attention for better extraction of artifact features, and the fused deeper features are sent into pixel attention for focusing on the artifact pixel information. Last, an artifact channel is designed to provide rich artifact features and guide the extraction of noise and artifact features. The AAPM LDCT Challenge dataset is used to train and test the network. The performance is evaluated using both visual observation and quantitative metrics including peak signal-noise-ratio (PSNR), structural similarity index (SSIM) and visual information fidelity (VIF). RESULTS: Using AAFFA network improves the averaged PSNR/SSIM/VIF values of AAPM LDCT images from 43.4961, 0.9595, 0.3926 to 48.2513, 0.9859, 0.4589, respectively. CONCLUSIONS: The proposed AAFFA network enables to effectively reduce noise and artifacts while preserving object edges. Assessment of visual quality and quantitative index demonstrates the progressive improvement compared with other image denoising methods.