Ultra-high field 7T MRI
scanners, while producing images with exceptional anatomical details, are cost
prohibitive and hence highly inaccessible. In this abstract, we propose a novel
deep learning network to synthesize 7T T1-weighted images from their 3T counterparts.
Our network jointly considers both spatial and wavelet domains to facilitate
learning for coarse to fine details.