Artifact and Detail Attention Generative Adversarial Networks for Low-Dose CT Denoising

• Published in: IEEE transactions on medical imaging 2021-12, Vol.40 (12), p.3901-3918
• Main Authors: Zhang, Xiong, Han, Zefang, Shangguan, Hong, Han, Xinglong, Cui, Xueying, Wang, Anhong
• Format: Article
• Language: English
• Subjects: Algorithms; Computed tomography; Convolution; Feature extraction; Generative adversarial networks; Generators; image denoising; Image edge detection; Image Processing, Computer-Assisted; Low-dose CT; Medical imaging; multi-channel generator; Noise; Noise reduction; Receptive field; Res2Net discriminator; Signal-To-Noise Ratio; Tomography, X-Ray Computed; X-ray imaging
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2021.3101616

Generative adversarial networks are being extensively studied for low-dose computed tomography denoising. However, due to the similar distribution of noise, artifacts, and high-frequency components of useful tissue images, it is difficult for existing generative adversarial network-based denoising networks to effectively separate the artifacts and noise in the low-dose computed tomography images. In addition, aggressive denoising may damage the edge and structural information of the computed tomography image and make the denoised image too smooth. To solve these problems, we propose a novel denoising network called artifact and detail attention generative adversarial network. First, a multi-channel generator is proposed. Based on the main feature extraction channel, an artifacts and noise attention channel and an edge feature attention channel are added to improve the denoising network's ability to pay attention to the noise and artifacts features and edge features of the image. Additionally, a new structure called multi-scale Res2Net discriminator is proposed, and the receptive field in the module is expanded by extracting the multi-scale features in the same scale of the image to improve the discriminative ability of discriminator. The loss functions are specially designed for each sub-channel of the denoising network corresponding to its function. Through the cooperation of multiple loss functions, the convergence speed, stability, and denoising effect of the network are accelerated, improved, and guaranteed, respectively. Experimental results show that the proposed denoising network can preserve the important information of the low-dose computed tomography image and achieve better denoising effect when compared to the state-of-the-art algorithms.