3D MRI super-resolution using convolutional generative adversarial network with gradient guidance

The application of MRI has been limited due to the restriction of imaging time and spatial resolution. Super-resolution is an important strategy in clinics to speed up MR imaging. In this work, we propose a novel GAN-based super-resolution method which incorporates gradient features to improve the recovery of local structures of the super-resolution images. Experiments on 3D MR Vessel Wall imaging demonstrate the superior performance of the proposed method.