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Connecting and merging fibres: Pathway extraction by combining probability maps

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dc.contributor.author Kreher B.
dc.contributor.author Schnell S.
dc.contributor.author Mader I.
dc.contributor.author Il'yasov K.
dc.contributor.author Hennig J.
dc.contributor.author Kiselev V.
dc.contributor.author Saur D.
dc.date.accessioned 2018-09-18T20:14:06Z
dc.date.available 2018-09-18T20:14:06Z
dc.date.issued 2008
dc.identifier.issn 1053-8119
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/137805
dc.description.abstract Probability mapping of connectivity is a powerful tool to determine the fibre structure of white matter in the brain. Probability maps are related to the degree of connectivity to a chosen seed area. In many applications, however, it is necessary to isolate a fibre bundle that connects two areas. A frequently suggested solution is to select curves, which pass only through two or more areas. This is very inefficient, especially for long-distance pathways and small areas. In this paper, a novel probability-based method is presented that is capable of extracting neuronal pathways defined by two seed points. A Monte Carlo simulation based tracking method, similar to the Probabilistic Index of Connectivity (PICo) approach, was extended to preserve the directional information of the main fibre bundles passing a voxel. By combining two of these extended visiting maps arising from different seed points, two independent parameters are determined for each voxel: the first quantifies the uncertainty that a voxel is connected to both seed points; the second represents the directional information and estimates the proportion of fibres running in the direction of the other seed point (connecting fibre) or face a third area (merging fibre). Both parameters are used to calculate the probability that a voxel is part of the bundle connecting both seed points. The performance and limitations of this DTI-based method are demonstrated using simulations as well as in vivo measurements. © 2008 Elsevier Inc. All rights reserved.
dc.relation.ispartofseries NeuroImage
dc.subject Anisotropic diffusion
dc.subject Connectivity
dc.subject DTI
dc.subject MRI
dc.subject WM
dc.title Connecting and merging fibres: Pathway extraction by combining probability maps
dc.type Article
dc.relation.ispartofseries-issue 1
dc.relation.ispartofseries-volume 43
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 81
dc.source.id SCOPUS10538119-2008-43-1-SID52049121589


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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