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Proceedings Paper

Efficient, graph-based white matter connectivity from orientation distribution functions via multi-directional graph propagation
Author(s): Alexis Boucharin; Ipek Oguz; Clement Vachet; Yundi Shi; Mar Sanchez; Martin Styner
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Paper Abstract

The use of regional connectivity measurements derived from diffusion imaging datasets has become of considerable interest in the neuroimaging community in order to better understand cortical and subcortical white matter connectivity. Current connectivity assessment methods are based on streamline fiber tractography, usually applied in a Monte-Carlo fashion. In this work we present a novel, graph-based method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. The computation is based on a multi-directional graph propagation method applied to sampled orientation distribution function (ODF), which can be computed directly from the original diffusion imaging data. We show early results of our method on synthetic and real datasets. The results illustrate the potential of our method towards subjectspecific connectivity measurements that are performed in an efficient, stable and reproducible manner. Such individual connectivity measurements would be well suited for application in population studies of neuropathology, such as Autism, Huntington's Disease, Multiple Sclerosis or leukodystrophies. The proposed method is generic and could easily be applied to non-diffusion data as long as local directional data can be derived.

Paper Details

Date Published: 11 March 2011
PDF: 8 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79620S (11 March 2011); doi: 10.1117/12.878291
Show Author Affiliations
Alexis Boucharin, The Univ. of North Carolina at Chapel Hill (United States)
Ipek Oguz, The Univ. of North Carolina at Chapel Hill (United States)
Clement Vachet, The Univ. of North Carolina at Chapel Hill (United States)
Yundi Shi, The Univ. of North Carolina at Chapel Hill (United States)
Mar Sanchez, Emory Univ. (United States)
Martin Styner, The Univ. of North Carolina at Chapel Hill (United States)

Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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