Spatio-Angular Convolutions for Super-resolution in Diffusion MRI
This work addresses the need for faster high-resolution diffusion MRI scans, which is incremental as it builds upon existing parametric convolution frameworks.
The authors tackled the problem of long scanning times in diffusion MRI by proposing a novel angular super-resolution approach based on parametric continuous convolutions, achieving competitive performance with significantly fewer parameters and demonstrating generalization to clinically relevant downstream analyses.
Diffusion MRI (dMRI) is a widely used imaging modality, but requires long scanning times to acquire high resolution datasets. By leveraging the unique geometry present within this domain, we present a novel approach to dMRI angular super-resolution that extends upon the parametric continuous convolution (PCConv) framework. We introduce several additions to the operation including a Fourier feature mapping, global coordinates, and domain specific context. Using this framework, we build a fully parametric continuous convolution network (PCCNN) and compare against existing models. We demonstrate the PCCNN performs competitively while using significantly less parameters. Moreover, we show that this formulation generalises well to clinically relevant downstream analyses such as fixel-based analysis, and neurite orientation dispersion and density imaging.