Bayesian Reconstruction of Undersampled Multicoil Hardi

High-angular-resolution diffusion imaging (HARDI) relies on multicoil acquisitions for clinical applications. HARDI scan time can be reduced by undersampling the set of gradient directions. Typical methods for undersampled HARDI reconstruction use two-stage schemes that first take scanner-reconstructed magnitude images for the acquired directions, and then fit a model to the reconstructed under-sampled diffusion signals, where they assume a Gaussian noise model that behaves poorly for high b values or high noise. In contrast, we propose a novel Bayesian framework for undersampled-HARDI reconstruction that directly fits to multicoil data. We use magnitude images per coil and a Rician noise model to bypass complicated phase-related artifacts and accurately reconstruct at large b values. We use a sparse dictionary prior on the diffusion signal across directions, and a multiscale wavelet regularity on each diffusion weighted image. Results on simulated and clinical HARDI data show that our method improves over the state of the art.