Motion Adaptive Patch-Based Low-Rank Approach for Compressed Sensing Cardiac Cine MRI

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dc.contributor.authorYoon, Huisuko
dc.contributor.authorKim, Kyung Sangko
dc.contributor.authorKim, Danielko
dc.contributor.authorBresler, Yoramko
dc.contributor.authorYe, Jong-Chulko
dc.date.accessioned2015-01-29T07:00:13Z-
dc.date.available2015-01-29T07:00:13Z-
dc.date.created2014-12-18-
dc.date.created2014-12-18-
dc.date.created2014-12-18-
dc.date.issued2014-11-
dc.identifier.citationIEEE TRANSACTIONS ON MEDICAL IMAGING, v.33, no.11, pp.2069 - 2085-
dc.identifier.issn0278-0062-
dc.identifier.urihttp://hdl.handle.net/10203/193810-
dc.description.abstractOne of the technical challenges in cine magnetic resonance imaging (MRI) is to reduce the acquisition time to enable the high spatio-temporal resolution imaging of a cardiac volume within a short scan time. Recently, compressed sensing approaches have been investigated extensively for highly accelerated cine MRI by exploiting transform domain sparsity using linear transforms such as wavelets, and Fourier. However, in cardiac cine imaging, the cardiac volume changes significantly between frames, and there often exist abrupt pixel value changes along time. In order to effectively sparsify such temporal variations, it is necessary to exploit temporal redundancy along motion trajectories. This paper introduces a novel patch-based reconstruction method to exploit geometric similarities in the spatio-temporal domain. In particular, we use a low rank constraint for similar patches along motion, based on the observation that rank structures are relatively less sensitive to global intensity changes, but make it easier to capture moving edges. A Nash equilibrium formulation with relaxation is employed to guarantee convergence. Experimental results show that the proposed algorithm clearly reconstructs important anatomical structures in cardiac cine image and provides improved image quality compared to existing state-of-the-art methods such as k-t FOCUSS, k-t SLR, and MASTeR.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.subjectK-T FOCUSS-
dc.subjectDYNAMIC MRI-
dc.subjectIMAGE-RECONSTRUCTION-
dc.subjectSIGNAL RECONSTRUCTION-
dc.subjectSPIRAL CT-
dc.subjectSPARSE-
dc.subjectALGORITHM-
dc.subjectREGULARIZATION-
dc.subjectREPRESENTATION-
dc.subjectOPTIMIZATION-
dc.titleMotion Adaptive Patch-Based Low-Rank Approach for Compressed Sensing Cardiac Cine MRI-
dc.typeArticle-
dc.identifier.wosid000344589200001-
dc.identifier.scopusid2-s2.0-84908576216-
dc.type.rimsART-
dc.citation.volume33-
dc.citation.issue11-
dc.citation.beginningpage2069-
dc.citation.endingpage2085-
dc.citation.publicationnameIEEE TRANSACTIONS ON MEDICAL IMAGING-
dc.identifier.doi10.1109/TMI.2014.2330426-
dc.contributor.localauthorYe, Jong-Chul-
dc.contributor.nonIdAuthorKim, Daniel-
dc.contributor.nonIdAuthorBresler, Yoram-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorCompressed sensing dynamic magnetic resonance imaging (MRI)-
dc.subject.keywordAuthorgeneralized Huber approximation-
dc.subject.keywordAuthormultiple object functions-
dc.subject.keywordAuthorNash equilibrium-
dc.subject.keywordAuthoroverlapped patches-
dc.subject.keywordAuthorpatch-based low-rank-
dc.subject.keywordAuthorproximal mapping-
dc.subject.keywordAuthorrank penalty-
dc.subject.keywordAuthorrelaxation-
dc.subject.keywordPlusK-T FOCUSS-
dc.subject.keywordPlusDYNAMIC MRI-
dc.subject.keywordPlusIMAGE-RECONSTRUCTION-
dc.subject.keywordPlusSIGNAL RECONSTRUCTION-
dc.subject.keywordPlusSPIRAL CT-
dc.subject.keywordPlusSPARSE-
dc.subject.keywordPlusALGORITHM-
dc.subject.keywordPlusREGULARIZATION-
dc.subject.keywordPlusREPRESENTATION-
dc.subject.keywordPlusOPTIMIZATION-
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