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Abstract #3792

Iterative Hard Thresholding and Matrix Shrinkage (IHT+MS) for Low-Rank Recovery of K-T Undersampled MRI Data

Mark Chiew1, Karla L. Miller1, Peter J. Koopmans2, Elizabeth M. Tunnicliffe3, Stephen M. Smith1, Thomas Blumensath4

1FMRIB Centre, University of Oxford, Oxford, United Kingdom; 2Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands; 3AVIC, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom; 4ISVR, University of Southampton, Southampton, Hampshire, United Kingdom

In matrices that are low rank or approximately so, matrix completion strategies can be used to recover data in the presence of undersampling. Here we present a novel algorithm, iterative hard thresholding + matrix shrinkage (IHT+MS), for the recovery of low rank approximations to k-t undersampled MRI data. Performance of the IHT+MS algorithm is compared to other matrix completion techniques in retrospectively undersampled cardiac cine and fMRI data. Results indicate that good reconstruction fidelity is observed in both cardiac and fMRI data, even at high undersampling factors, and that IHT+MS produces the best results in many cases.