  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.
