CycleGAN-based Degradation Enhancement for Unpaired Super-Resolution of Anisotropic Medical Images

Motivation: 3D medical images are often acquired with anisotropic volumes to reduce scan times. Super-resolution reconstruction to recover features in the low-resolution direction would improve visualisation and clinical accuracy. Goal(s): To train an unpaired super-resolution network for anisotropic 3D MRI and CT images. Approach: We propose that it is possible to leverage disjoint patches from the high-resolution (in-plane) data to increase the resolution of the low-resolution (through-plane) slices. Results: We demonstrate that our proposed modified CycleGAN architecture, performs better than the standard CycleGAN for super-resolution of MRI and CT data. Impact: Unpaired super-resolution reconstruction of anisotropic 3D medical images, enables accurate recovery of features in the low-resolution direction of MRI and CT data.