Multi-scale residual denoising GAN model for producing super-resolution CTA images

Abstract

Computed tomography angiography (CTA) is one of the salient radiological techniques in the virtualization and diagnosis of cerebral vascular diseases. However, there are various obstacles to the acquisition of highly legible CTA images, such as the lack of high-resolution CT scanners in community hospitals. And it is time-consuming for radiologists to perform CTA post-processing. These predicaments that medical institutions face make it necessary to automatically covert cerebrovascular images of low resolution to high-quality ones by means of artificial intelligence systems. In this paper, we propose a deep learning technique to improve the resolution of blurred CTA images. We develop MRDGAN, a novel generative adversarial network (GAN) model, to address the outstanding problems in CTA images such as high-frequency noise information (black pixels) and the scarcity of useful information (blood vessel pixels). We introduce spatial and channel attention into MRDGAN’s generator to facilitate feature extraction and incorporate a multi-scale residual block and a noise reduction block to retain micro vessels’ information and eliminate the noise in the generated images. Experiment results show that the CTA images generated by our model MRDGAN outperform the state-of-the-art models SRGAN and ESRGAN in terms of quality and quantity—MRDGAN obtains the highest score (35.89) in peak signal-to-noise ratio, showing a great potential as a low-cost solution of acquiring high-resolution CTA images.