Spatio-Angular Convolutions for Super-resolution in Diffusion MRI

Matthew Lyon, Paul Armitage, Mauricio A Álvarez

Keywords: Diffusion MRI, super-resolution, image synthesis, conditional image synthesis, continuous convolution, parametric continuous convolution

TL;DR: We extend upon the parametric continuous convolution framework to develop a network well suited for diffusion MRI super-resolution

Abstract: 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' co-ordinates, 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 fewer 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.

Submission Number: 13443