(k,q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior
This work addresses the underutilization of advanced dMRI techniques like DSI and HARDI due to slow scans, offering a method to accelerate them for medical imaging applications, though it appears incremental as it builds on existing compressed sensing frameworks.
The paper tackled the problem of long scan times in advanced diffusion MRI by proposing a unified compressed sensing formulation that imposes joint sparsity in spatial-angular domains, achieving significantly more accurate signal reconstructions while sampling only 2-4% of the (k,q)-space.
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.