Accelerated diffusion spectrum imaging via compressed sensing for the human connectome project

Diffusion Spectrum Imaging (DSI) has been developed as a model-free approach to solving the so called multiple-fibers-per- voxel problem in diffusion MRI. However, inferring heterogeneous microstructures of an imaging voxel rapidly remains a challenge in DSI because of extensive sampling requirements in a Cartesian grid of q-space. In this study, we propose compressed sensing based diffusion spectrum imaging (CS-DSI) to significantly reduce the number of diffusion measurements required for accurate estimation of fiber orientations. This method reconstructs each diffusion propagator of an MR data set from 100 variable density undersampled diffusion measurements minimizing the l1-norm of the finite-differences (i.e.,anisotropic total variation) of the diffusion propagator. The proposed method is validated against a ground truth from synthetic data mimicking the FiberCup phantom, demonstrating the robustness of CS-DSI on accurately estimating underlying fiber orientations from noisy diffusion data. We demonstrate the effectiveness of our CS-DSI method on a human brain dataset acquired from a clinical scanner without specialized pulse sequences. Estimated ODFs from CS-DSI method are qualitatively compared to those from the full dataset (DSI203). Lastly, we demonstrate that streamline tractography based on our CS-DSI method has a comparable quality to conventional DSI203. This illustrates the feasibility of CS-DSI for reconstructing whole brain white-matter fiber tractography from clinical data acquired at imaging centers, including hospitals, for human brain connectivity studies.