MRI super-resolution using 3D cycle-consistent generative adversarial network

High-resolution magnetic resonance imaging (MRI) provides detailed anatomical information critical for clinical application diagnosis. However, current MRIs are acquired at clinical resolutions due to the limit of physical, technological, and economic considerations. On the other hand, existing approaches require paired MRI images as training data, which are difficult to obtain on existing dataset...