Motion-compensated data decomposition algorithm to accelerate dynamic cardiac MRI View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-05-31

AUTHORS

Azar Tolouee, Javad Alirezaie, Paul Babyn

ABSTRACT

ObjectivesIn dynamic cardiac magnetic resonance imaging (MRI), the spatiotemporal resolution is often limited by low imaging speed. Compressed sensing (CS) theory can be applied to improve imaging speed and spatiotemporal resolution. The combination of compressed sensing and low-rank matrix completion represents an attractive means to further increase imaging speed. By extending prior work, a Motion-Compensated Data Decomposition (MCDD) algorithm is proposed to improve the performance of CS for accelerated dynamic cardiac MRI.Materials and methodsThe process of MCDD can be described as follows: first, we decompose the dynamic images into a low-rank (L) and a sparse component (S). The L component includes periodic motion in the background, since it is highly correlated among frames, and the S component corresponds to respiratory motion. A motion-estimation/motion-compensation (ME-MC) algorithm is then applied to the low-rank component to reconstruct a cardiac motion compensated dynamic cardiac MRI.ResultsWith validations on the numerical phantom and in vivo cardiac MRI data, we demonstrate the utility of the proposed scheme in significantly improving compressed sensing reconstructions by minimizing motion artifacts. The proposed method achieves higher PSNR and lower MSE and HFEN for medium to high acceleration factors.ConclusionThe proposed method is observed to yield reconstructions with minimal spatiotemporal blurring and motion artifacts in comparison to the existing state-of-the-art methods. More... »

PAGES

33-47

References to SciGraph publications

  • 2009-04-03. Exact Matrix Completion via Convex Optimization in FOUNDATIONS OF COMPUTATIONAL MATHEMATICS
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    http://scigraph.springernature.com/pub.10.1007/s10334-017-0628-x

    DOI

    http://dx.doi.org/10.1007/s10334-017-0628-x

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1085727819

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/28569375


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