Low-dose CT Denoising with Dilated Residual Network

Abstract

Low-dose Computed Tomography (CT) is considered a solution for reducing the risk of X-ray radiation; however, lowering the X-ray current results in a degraded reconstructed image. To improve the quality of the image, different noise removal techniques have been proposed. Con- volutional neural networks also have shown promising results in denoising the low-dose CT images. In this paper, a deep residual network with dilated convolution is proposed. The identity mappings pass the signal to the higher layers and improve the performance of the network and its training time. Moreover, employing dilated convolution helps to increase the receptive field faster. Dilated convolution makes it possible to achieve good results with fewer layers and less computational costs. The proposed network learns end to end mapping from low-dose to normal-dose CT images.