Improving the diagnostic performance of computed tomography angiography for intracranial large arterial stenosis by a novel super-resolution algorithm based on multi-scale residual denoising generative adversarial network.

Background: Computed tomography angiography (CTA) is very popular because it is characterized by rapidity and accessibility. However, CTA is inferior to digital subtraction angiography (DSA) in the diagnosis of intracranial artery stenosis or occlusion. DSA is an invasive examination, so we optimized the quality of cephalic CTA images.