Wavelet-based parallel MRI regularization using bivariate sparsity promoting priors

Parallel magnetic resonance imaging (pMRI) relying on multiple receiver coils has emerged as a powerful 3D imaging technique for reducing scanning time or increasing spatial or temporal resolution. The acquired k-space is subsampled, and full field of view (FoV) images are then reconstructed from the acquired aliased data by applying methods such as the SENSE algorithm. However, reconstructed images using SENSE may suffer from several kinds of artifacts mainly because of noise and inaccurate sensitivity profiles. In this paper, we propose a regularized SENSE reconstruction method in which the regularization takes place in the wavelet transform domain. More precisely, a Bayesian strategy is adopted by introducing a bivariate prior to model the complex-valued signal. Experiments on synthetic data and real T1-weighted MRI images at 1.5 Tesla magnetic field show that the proposed method provides improved reconstruction.