HYR2PICS: Hybrid Regularized Reconstruction for combined Parallel Imaging and Compressive Sensing in MRI

Abstract

International audienceBoth parallel Magnetic Resonance Imaging~(pMRI) and Compressed Sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data in the $k$-space. So far, first attempts to combine sensitivity encoding (SENSE) imaging in pMRI with CS have been proposed in the context of Cartesian trajectories. Here, we extend these approaches to non-Cartesian trajectories by jointly formulating the CS and SENSE recovery in a hybrid Fourier/wavelet framework and optimizing a convex but nonsmooth criterion. On anatomical MRI data, we show that HYR$^2$PICS outperforms wavelet-based regularized SENSE reconstruction. Our results are also in agreement with the Transform Point Spread Function (TPSF) criterion that measures the degree of incoherence of $k$-space undersampling schemes