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dc.contributor.authorŞenel, L. Keremen_US
dc.contributor.authorKılıç, Toyganen_US
dc.contributor.authorGüngör, Alperen_US
dc.contributor.authorKopanoğlu, Emreen_US
dc.contributor.authorGüven, H. Emreen_US
dc.contributor.authorSarıtaş, Emine U.en_US
dc.contributor.authorKoç, Aykuten_US
dc.contributor.authorÇukur, Tolgaen_US
dc.date.accessioned2020-02-04T11:43:11Z
dc.date.available2020-02-04T11:43:11Z
dc.date.issued2019en_US
dc.identifier.issn0278-0062
dc.identifier.urihttp://hdl.handle.net/11693/53054
dc.description.abstractA central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techniques can alleviate this limitation via randomly undersampled acquisitions. A frequent sampling strategy is to prescribe for each acquisition a different random pattern drawn from a common sampling density. However, naive random patterns often contain gaps or clusters across the acquisition dimension that, in turn, can degrade reconstruction quality or reduce scan efficiency. To address this problem, a statistically segregated sampling method is proposed for multiple-acquisition MRI. This method generates multiple patterns sequentially while adaptively modifying the sampling density to minimize k-space overlap across patterns. As a result, it improves incoherence across acquisitions while still maintaining similar sampling density across the radial dimension of k-space. Comprehensive simulations and in vivo results are presented for phase-cycled balanced steady-state free precession and multi-echo $\text{T}_{\text{2}}$ -weighted imaging. Segregated sampling achieves significantly improved quality in both Fourier and compressed-sensing reconstructions of multiple-acquisition datasets.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Transactions on Medical Imagingen_US
dc.relation.isversionofhttps://doi.org/10.1109/TMI.2019.2892378en_US
dc.subjectSampling patternen_US
dc.subjectIncoherenceen_US
dc.subjectk-space coverageen_US
dc.subjectVariable densityen_US
dc.subjectMultiple acquisitionen_US
dc.subjectCompressed sensingen_US
dc.titleStatistically segregated k-space sampling for accelerating multiple-acquisition MRIen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentNational Magnetic Resonance Research Center (UMRAM)en_US
dc.citation.spage1701en_US
dc.citation.epage1714en_US
dc.citation.volumeNumber38en_US
dc.citation.issueNumber7en_US
dc.identifier.doi10.1109/TMI.2019.2892378en_US
dc.publisherIEEEen_US
dc.contributor.bilkentauthorŞenel, L. Kerem
dc.contributor.bilkentauthorKılıç, Toygan
dc.contributor.bilkentauthorGüngör, Alper
dc.contributor.bilkentauthorKopanoğlu, Emre
dc.contributor.bilkentauthorGüven, H. Emre
dc.contributor.bilkentauthorSarıtaş, Emine U.
dc.contributor.bilkentauthorKoç, Aykut
dc.contributor.bilkentauthorÇukur, Tolga
dc.identifier.eissn1558-254X


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