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A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix

K.H. Jin, D. Lee, J.C. Ye

IEEE Transactions on Computational Imaging, vol. 2, no. 4, pp. 480-495, December 2016.

Parallel 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.

AUTHOR="Jin, K.H. and Lee, D. and Ye, J.C.",
TITLE="A General Framework for Compressed Sensing and Parallel {MRI}
        Using Annihilating Filter Based Low-Rank {H}ankel Matrix",
JOURNAL="{IEEE} Transactions on Computational Imaging",

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