CALIBRATIONLESS OSCAR-BASED IMAGE RECONSTRUCTION IN COMPRESSED SENSING PARALLEL MRI

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 reconstruction with other calibrationless approaches based on group LASSO and its sparse variation as well as with the auto-calibrated method called l(1)-ESPIRiT. We demonstrate that OSCAR outperforms its competitors and provides similar results to l(1)-ESPIRiT. This suggests that the sensitivity maps are no longer required to perform combined CS and parallel imaging reconstruction.