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Mitigating gyral bias in cortical tractography via asymmetric fiber orientation distributions

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dc.contributor.authorWu, Ye-
dc.contributor.authorHong, Yoonmi-
dc.contributor.authorFeng, Yianjing-
dc.contributor.authorShen, Dinggang-
dc.contributor.authorYap, Pew-Thian-
dc.date.accessioned2021-08-31T14:49:33Z-
dc.date.available2021-08-31T14:49:33Z-
dc.date.created2021-06-19-
dc.date.issued2020-01-
dc.identifier.issn1361-8415-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/58399-
dc.description.abstractDiffusion tractography in brain connectomics often involves tracing axonal trajectories across gray-white, matter boundaries in gyral blades of complex cortical convolutions. To date, gyral bias is observed in most tractography algorithms with streamlines predominantly terminating at gyral crowns instead of sulcal banks. This work demonstrates that asymmetric fiber orientation distribution functions (AFODFs), computed via a multi-tissue global estimation framework, can mitigate the effects of gyral bias, enabling fiber streamlines at gyral blades to make sharper turns into the cortical gray matter. We use ex-vivo data of an adult rhesus macaque and in-vivo data from the Human Connectome Project (HCP) to show that the fiber streamlines given by AFODFs bend more naturally into the cortex than the conventional symmetric FODFs in typical gyral blades. We demonstrate that AFODF tractography improves cortico-cortical connectivity and provides highly consistent outcomes between two different field strengths (3T and 7T). (C) 2019 Elsevier B.V. All rights reserved.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherELSEVIER-
dc.subjectIN-DIFFUSION MRI-
dc.subjectDENSITY-
dc.subjectCOEFFICIENT-
dc.subjectTRACKING-
dc.titleMitigating gyral bias in cortical tractography via asymmetric fiber orientation distributions-
dc.typeArticle-
dc.contributor.affiliatedAuthorShen, Dinggang-
dc.identifier.doi10.1016/j.media.2019.101543-
dc.identifier.scopusid2-s2.0-85073947903-
dc.identifier.wosid000501405800002-
dc.identifier.bibliographicCitationMEDICAL IMAGE ANALYSIS, v.59-
dc.relation.isPartOfMEDICAL IMAGE ANALYSIS-
dc.citation.titleMEDICAL IMAGE ANALYSIS-
dc.citation.volume59-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaRadiology, Nuclear Medicine & Medical Imaging-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryEngineering, Biomedical-
dc.relation.journalWebOfScienceCategoryRadiology, Nuclear Medicine & Medical Imaging-
dc.subject.keywordPlusIN-DIFFUSION MRI-
dc.subject.keywordPlusDENSITY-
dc.subject.keywordPlusCOEFFICIENT-
dc.subject.keywordPlusTRACKING-
dc.subject.keywordAuthorDiffusion MRI-
dc.subject.keywordAuthorTractography-
dc.subject.keywordAuthorGyral bias-
dc.subject.keywordAuthorAsymmetric fiber orientation distribution-
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