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pubmed-article:21095705pubmed:abstractTextDynamic imaging methods based on the Partially Separable Functions (PSF) model have been used to perform ungated cardiac MRI, and the critical parameter determining the quality of the reconstructed images is the order, L, of the PSF model. This work extends previous methods by increasing L in the cardiac region to improve the ability of the PSF model to represent complex spatiotemporal signals. The resulting higher order PSF model is fit to sparse (k, t)-space data using spatial-spectral support, spatial-eigenbasis support, and spectral sparsity constraints. This new method is demonstrated in the context of 2D first-pass perfusion MRI in a healthy rat heart.lld:pubmed
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pubmed-article:21095705pubmed:authorpubmed-author:YeQingQlld:pubmed
pubmed-article:21095705pubmed:authorpubmed-author:HoChienClld:pubmed
pubmed-article:21095705pubmed:authorpubmed-author:ZhangHaosenHlld:pubmed
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pubmed-article:21095705pubmed:volume2010lld:pubmed
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pubmed-article:21095705pubmed:pagination2833-6lld:pubmed
pubmed-article:21095705pubmed:dateRevised2011-9-26lld:pubmed
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pubmed-article:21095705pubmed:year2010lld:pubmed
pubmed-article:21095705pubmed:articleTitleFirst-pass perfusion cardiac MRI using the Partially Separable Functions model with generalized support.lld:pubmed
pubmed-article:21095705pubmed:affiliationDepartment of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 1406 West Green Street, Urbana, IL 61801, USA.lld:pubmed
pubmed-article:21095705pubmed:publicationTypeJournal Articlelld:pubmed
pubmed-article:21095705pubmed:publicationTypeResearch Support, N.I.H., Extramurallld:pubmed