Generalized reconstruction by inversion of coupled systems (GRICS) applied to parallel MRI

This paper presents a framework allowing parallel MRI to be optimized. Parallel imaging relies on good coil sensitivity map estimates. As these sensitivities are determined experimentally, errors may occur during their assessment, whether using prior calibration (due to patient motion between calibration and actual scan), or autocalibration (due to lower resolution, suboptimal estimates). Here we reformulate reconstruction as a coupled inverse problem, consisting of simultaneously solving the parallel imaging problem, based on SENSE algorithm, and minimizing the propagation of sensitivity map errors in that reconstruction. The problem is practically solved using a multiresolution fixed-point iterative method, producing both the reconstructed image and optimized sensitivity maps. The method was validated by comparing sensitivity maps and reconstructed images obtained by standard SENSE reconstruction, based on a reduced number of autocalibration signal (ACS) data, to those obtained by the proposed method, starting from the same ACS data as initial guess.