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dc.contributor.authorKopanoğlu, E.
dc.contributor.authorGüngör, Alper
dc.contributor.authorKılıç, Toygan
dc.contributor.authorSarıtaş, Emine Ülkü
dc.contributor.authorOğuz, Kader K.
dc.contributor.authorÇukur, Tolga
dc.contributor.authorGüven, H. E.
dc.date.accessioned2021-03-05T17:36:35Z
dc.date.available2021-03-05T17:36:35Z
dc.date.issued2020
dc.identifier.issn0952-3480
dc.identifier.urihttp://hdl.handle.net/11693/75857
dc.description.abstractMulti‐contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time‐efficient strategy to acquire high‐quality multi‐contrast images is to accelerate individual sequences and then reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause features that are unique to a subset of contrasts to leak into the other contrasts. Such leakage‐of‐features may appear as artificial tissues, thereby misleading diagnosis. The goal of this study is to develop a compressive sensing method for multi‐channel multi‐contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi‐channel multi‐contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single‐channel simulated and multi‐channel in‐vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. The proposed method demonstrates rapid convergence and improved image quality for both simulated and in‐vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage‐of‐features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms, thereby holding great promise for clinical use.en_US
dc.language.isoEnglishen_US
dc.source.titleNMR in Biomedicineen_US
dc.relation.isversionofhttps://dx.doi.org/10.1002/nbm.4247en_US
dc.subjectCompressive sensingen_US
dc.subjectJoint reconstructionen_US
dc.subjectLeakage-of-featuresen_US
dc.subjectMagnetic resonance imagingen_US
dc.subjectMulti contrasten_US
dc.subjectParallel imagingen_US
dc.subjectImage reconstructionen_US
dc.subjectJoint regularizationen_US
dc.titleSimultaneous use of individual and joint regularization terms in compressive sensing: joint reconstruction of multi‐channel multi‐contrast MRI acquisitionsen_US
dc.typeArticleen_US
dc.departmentAysel Sabuncu Brain Research Center (BAM)en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentInterdisciplinary Program in Neuroscience (NEUROSCIENCE)en_US
dc.departmentNational Magnetic Resonance Research Center (UMRAM)en_US
dc.citation.spage4247-16en_US
dc.citation.epage4247-1en_US
dc.citation.volumeNumber33en_US
dc.citation.issueNumber4en_US
dc.identifier.doi10.1002/nbm.4247en_US
dc.publisherWileyen_US
dc.contributor.bilkentauthorGüngör, Alper
dc.contributor.bilkentauthorKılıç, Toygan
dc.contributor.bilkentauthorSarıtaş, Emine Ülkü
dc.contributor.bilkentauthorOğuz, Kader K.
dc.contributor.bilkentauthorÇukur, Tolga
dc.embargo.release2021-04-01


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