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dc.contributor.authorAganj, Iman
dc.date.accessioned2020-03-11T10:17:46Z
dc.date.issued2018-11-08
dc.identifier.citationAganj, Iman. 2018. Automatic Verification of the Gradient Table in Diffusion-Weighted MRI Based on Fiber Continuity. Scientific Reports 8, no. 1: 16541.en_US
dc.identifier.issn2045-2322en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:42615134*
dc.description.abstractIn diffusion-weighted magnetic resonance imaging (dMRI), the coordinate systems where the image and the diffusion gradients are represented may be inconsistent, thus impacting the quality of subsequent fiber tracking and connectivity analysis. Empirical verification of the reconstructed fiber orientations and subsequent correction of the gradient table (by permutation and flipping), both time- consuming tasks, are therefore often necessary. To save manual labor in studies involving dMRI, we introduce a new automatic gradient-table verification approach, which we propose to include in the dMRI processing pipeline. To that end, we exploit the concept of fiber continuity – the assumption that, in the fibrous tissue (such as the brain white matter), fiber bundles vary smoothly along their own orientations. Our tractography-free method tries all possible permutation and flip configurations of the gradient table, and in each case, assesses the consistency of the reconstructed fiber orientations with fiber continuity. Our algorithm then suggests the correct gradient table by choosing the configuration with the most consistent fiber orientations. We validated our method in 185 experiments on human brain dMRI data form three public data sources. The proposed algorithm identified the correct permutation and flip configuration for the gradient table in all the experiments.en_US
dc.language.isoen_USen_US
dc.publisherSpringer Science and Business Media LLCen_US
dash.licenseLAA
dc.subjectMultidisciplinaryen_US
dc.titleAutomatic Verification of the Gradient Table in Diffusion-Weighted MRI Based on Fiber Continuityen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalScientific Reportsen_US
dash.depositing.authorAganj, Iman
dc.date.available2020-03-11T10:17:46Z
dash.affiliation.otherHarvard Business Schoolen_US
dc.identifier.doi10.1038/s41598-018-34940-4
dc.source.journalSci Rep
dash.source.volume8;1
dash.contributor.affiliatedAganj, Iman


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