Unsupervised Super Resolution of Diffusion Weighted Imaging Guided by High-Resolution Cross-Modality Prior

Motivation: Existing supervised super-resolution is challenging for Diffusion Weighted Imaging(DWI) due to acquisition. However, the feature of T2 weighted imaging(T2w) could be utilized as a prior for unsupervised training. Goal(s): To develop unsupervised super-resolution on DWI with the aid of high-resolution T2w images. Approach: A UNet architecture is designed to perform same-resolution domain adaptation. During inference, the high frequency feature of the T2w images are used to fuse with the low frequency feature of original DWI in k-space to reconstruct high-resolution DWI. Results: Our result shows improved SSIM score verified by paired student t-test. Our direct inference on HR DWI also exhibits improved sharpness. Impact: This pilot work demonstrated that HR images (T2w) can be domain-adapted to provide high frequency prior to unsupervised super-resolution tasks using computationally efficient DL models.