Bayesian sparse regularized reconstruction in parallel MRI with sensitivity matrix imprecision

Parallel MRI is a fast imaging technique that allows reconstruction of full Field-of-View (FoV) images based on under-sampled k-space data acquired using multiple receiver coils with complementary sensitivity profiles. It enables the acquisition of highly resolved images either in space or time, which is of particular interest in applications like functional neuroimaging. These features are counterbalanced by a degraded SNR and the presence of artifacts that depend on the reconstruction algorithm. These artifacts are mainly caused by acquisition noise and imprecisions in the sensitivity matrices, which are a priori estimated. In this paper, we present a novel method for parallel MRI Bayesian regularized reconstruction while accounting for sensitivity maps imperfections and correcting them. The proposed method is validated on realistic simulated data and results show the outperformance of our method even compared to regularized techniques.