(k,q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior

TL;DR

This paper introduces a unified (k,q)-compressed sensing framework for dMRI that jointly exploits spatial-angular sparsity, enabling more accurate reconstructions with significantly less data, thus accelerating imaging.

Contribution

It proposes a novel joint sparsity model and an adapted FISTA algorithm for large-scale reconstruction, outperforming existing methods in dMRI signal recovery.

Findings

Achieves accurate reconstructions with only 2-4% of (k,q)-space sampling.

Outperforms state-of-the-art methods in phantom and real HARDI data.

Enables potential new levels of dMRI acceleration.

Abstract

Advanced diffusion magnetic resonance imaging (dMRI) techniques, like diffusion spectrum imaging (DSI) and high angular resolution diffusion imaging (HARDI), remain underutilized compared to diffusion tensor imaging because the scan times needed to produce accurate estimations of fiber orientation are significantly longer. To accelerate DSI and HARDI, recent methods from compressed sensing (CS) exploit a sparse underlying representation of the data in the spatial and angular domains to undersample in the respective k- and q-spaces. State-of-the-art frameworks, however, impose sparsity in the spatial and angular domains separately and involve the sum of the corresponding sparse regularizers. In contrast, we propose a unified (k,q)-CS formulation which imposes sparsity jointly in the spatial-angular domain to further increase sparsity of dMRI signals and reduce the required subsampling rate. To efficiently solve this large-scale global reconstruction problem, we introduce a novel adaptation of the FISTA algorithm that exploits dictionary separability. We show on phantom and real HARDI data that our approach achieves significantly more accurate signal reconstructions than the state of the art while sampling only 2-4% of the (k,q)-space, allowing for the potential of new levels of dMRI acceleration.