Jin, Kyong HwanLee, DongwookYe, Jong Chul2017-02-232017-02-232017-02-23201610.1109/TCI.2016.2601296https://infoscience.epfl.ch/handle/20.500.14299/134719WOS:000390150200007Parallel MRI (pMRI) and compressed sensing MRI (CS-MRI) have been considered as two distinct reconstruction problems. Inspired by recent k-space interpolation methods, an annihilating filter-based low-rank Hankel matrix approach is proposed as a general framework for sparsity-driven k-space interpolation method which unifies pMRI and CS-MRI. Specifically, our framework is based on a novel observation that the transform domain sparsity in the primary space implies the low-rankness of weighted Hankel matrix in the reciprocal space. This converts pMRI and CS-MRI to a k-space interpolation problem using a structured matrix completion. Experimental results using in vivo data for single/multicoil imaging as well as dynamic imaging confirmed that the proposed method outperforms the state-of-the-art pMRI and CS-MRI.Annihilating filtercardinal splinecompressed sensingparallel MRIpyramidal representationstructured low rank block Hankel matrix completionwaveletstext::journal::journal article::research article