Low rank recovery with manifold smoothness prior: Theory and application to accelerated dynamic MRI

Abstract

We introduce a regularized optimization algorithm to jointly recover signals that live on a low dimensional smooth manifold. The regularization penalty is the nuclear norm of the gradients of the signals on the manifold. We use this algorithm to reconstruct free breathing dynamic cardiac CINE MRI data. A novel acquisition scheme was used to facilitate the… (More)
DOI: 10.1109/ISBI.2015.7163877

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Cite this paper

@article{Poddar2015LowRR, title={Low rank recovery with manifold smoothness prior: Theory and application to accelerated dynamic MRI}, author={Sunrita Poddar and Mathews Jacob}, journal={2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)}, year={2015}, pages={319-322} }