(k, q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior
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.
This work was supported by JHU start-up funds.
- 1.Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imag. Sci. 2(1), 183–202 (2009)Google Scholar
- 2.Candès, E.: Compressive sampling. In: Proceedings of the International Congress of Mathematics (2006)Google Scholar
- 4.Cheng, J., Shen, D., Basser, P.J., Yap, P.T.: Joint 6D kq space compressed sensing for accelerated high angular resolution diffusion MRI. In: Information Processing in Medical Imaging, pp. 782–793. Springer, New York (2015)Google Scholar
- 10.Schwab, E., Vidal, R., Charon, N.: Spatial-angular sparse coding for HARDI. In: Medical Image Computing and Computer Assisted Intervention, pp. 475–483. Springer, New York (2016)Google Scholar
- 11.Schwab, E., Vidal, R., Charon, N.: Efficient global spatial-angular sparse coding for diffusion MRI with separable dictionaries (2017). arXivGoogle Scholar
- 12.Sun, J., Sakhaee, E., Entezari, A., Vemuri, B.C.: Leveraging EAP-sparsity for compressed sensing of MS-HARDI in (k,q)-space. In: Information Processing in Medical Imaging, pp. 375–386. Springer, New York (2015)Google Scholar
- 14.Tristán-Vega, A., Westin, C.-F.: Probabilistic ODF estimation from reduced HARDI data with sparse regularization. In: Medical Image Computing and Computer Assisted Intervention, pp. 182–190. Springer, New York (2011)Google Scholar