Unsupervised Super-Resolution of Diffusion-Weighted Images via Deep Diffusion Prior

Deep learning-based super-resolution (SR) has shown great potential in improving the resolution of diffusion-weighted imaging (DWI), which is useful in clinical diagnosis and neuroscience studies of white matter. However, most existing deep learning methods for DWI SR are supervised, relying on paired low-high resolution images, which can be in practice difficult to acquire. To address this limitation, we propose an unsupervised DWI SR model, called deep diffusion prior (DDP), to learn low-level features for effective resolution enhancement of DW images using only information from low-resolution (LR) images. We incorporate structural and angular information to improve SR performance. The former is provided by structural magnetic resonance images encoding rich anatomical information. The latter is formulated based on angular neighboring constraints in the diffusion wavevector space. Extensive experiments on data from the human connectome project (HCP) show that DDP is qualitatively and quantitatively superior to competing methods in the absence of paired HR DW images.