A Dual-Channel Network Based GAN for Low-Dose CT Image Denoising

Denoising of low-dose computed tomography (LDCT)images is a hot issue in the field of medical images. In this paper, a dual-channel denoising network based on GAN architecture is proposed to solve the key problems of image blur and loss of detail structure in the process of LDCT denoising. In the generator part, a content learning channel and a noise learning channel are used to learn images at the same time. In the content learning channel, an improved U-Net network is used to learn the image content to generate a normal dose computed tomography (NDCT) image. In the noise learning channel, firstly, wavelet transform is used to obtain high-frequency components, and then the improved DnCNN network is used to learn the noise. Finally, the NDCT is obtained. Through the fusion layer, the results of the two are fused to obtain a better image. Through the ablation implementation and compared with the conventional methods, the method suggested in this research has a better denoising effect and a certain suppression effect on the blurring of detail structures.