Dilated Residual Convolutional Neural Networks for Low-Dose CT Image Denoising

X-ray computed tomography (CT) imaging, which uses X-ray to acquire image data, is widely used in medicine. High X-ray doses may be harmful to the patient's health. Therefore, X-ray doses are often reduced at the expense of reduced quality of CT images. This paper presents a convolutional neural network model for low-dose CT image denoising, inspired by a recently introduced dialated residual network for despeckling of synthetic aparture radar images (SAR-DRN). In particular, batch normalization is added to some layers of SAR-DRN in order to adapt SAR-DRN for low-dose CT denoising. In addition, a preprocessing layer and a post-processing one are added in order to improve the receptive field and to reduce computational time. Moreover, the perceptual loss combined with MSE one are used in the training phase so that the proposed denoising model can preserve more subtle details of denoised images. Experimental results show that the proposed model can denoise low-dose CT images efficiently as compared to some state-of-the-art methods.