ACCELERATED PARALLEL MAGNETIC RESONANCE IMAGING RECONSTRUCTION USING JOINT ESTIMATIONWITH A SPARSE SIGNAL MODEL

Accelerating magnetic resonance imaging (MRI) by re-ducing the number of acquired k-space scan lines benefits conventional MR imaging significantly by decreasing the time subjects remain in the magnet. In this paper, we formu-late a novel method for Joint estimation from Undersampled LinEs in Parallel MRI (JULEP) that simultaneously calibrates the GeneRalized Autocalibrating Partially Parallel Acquisi-tions (GRAPPA) reconstruction kernel and reconstructs the full multi-channel k-space. We employ a joint sparsity signal model for the channel images in conjunction with observation models for both the acquired data and GRAPPA reconstructed k-space. We demonstrate using real MRI data that JULEP outperforms conventional GRAPPA reconstruction at high levels of undersampling, increasing peak-signal-to-noise ra-tio by up to 10 dB. Index Terms — Magnetic resonance imaging, image re-construction, parallel imaging, sparsity, Bayesian estimation 1