Calibrationless Oscar-Based Image Reconstruction in Compressed Sensing Parallel MRI

Abstract

Reducing acquisition time is a crucial issue in MRI especially in the high resolution context. Compressed sensing has faced this problem for a decade. However, to maintain a high signal-to-noise ratio (SNR), CS must be combined with parallel imaging. This leads to harder reconstruction problems that usually require the knowledge of coil sensitivity profiles. In this work, we introduce a calibrationless image reconstruction approach that no longer requires this knowledge. The originality of this work lies in using for reconstruction a group sparsity structure (called OSCAR) across channels that handles SNR inhomogeneities across receivers. We compare this re-construction with other calibrationless approaches based on group-LASSO and its sparse variation as well as with the auto-calibrated method called $\ell_{1}$-ESPIRiT. We demonstrate that OSCAR outperforms its competitors and provides similar results to $\ell_{1}$-ESPIRiT. This suggests that the sensitivity maps are no longer required to per-form combined CS and parallel imaging reconstruction.