Efficient, Graph-based White Matter Connectivity from Orientation Distribution Functions via Multi-directional Graph Propagation

• Type of Material: Conference Paper
• Authors: Alexis Boucharin, University of North Carolina; Ipek Oguz, University of North Carolina; Clement Vachet, University of North Carolina; Yundi Shi, University of North Carolina; Mar Sanchez, Emory University; Martin Styner, University of North Carolina
• Language: English
• Date: 2011-01-01
• Publisher: Emory University Libraries
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
• Final Published Version (URL): http://dx.doi.org/10.1117/12.878291
• Title of Journal or Parent Work: MEDICAL IMAGING 2011: IMAGE PROCESSING
• Conference or Event Name: Conference on Medical Imaging 2011 - Image Processing
• Volume: 7962
• Start Page: 79620S
• End Page: 79620S
• Grant/Funding Information: This work was funded by the NADIA U01-AA020022 Scientific Core, the NIH Program Project IP01DA022446-02 and the National Alliance for Medical Image Computing (NA-MIC) grant U54-EB005149.; We would like to further acknowledge the financial support of the NIH grant P50 MH078105-01A2S1, NIH STTR grant R41 NS059095, the UNC Neurodevelopmental Disorders Research Center HD 03110, the NIH grant RC1AA019211.
• Keywords: Radiology, Nuclear Medicine & Medical Imaging; Technology; Engineering; Life Sciences & Biomedicine; Engineering, Electrical & Electronic; Optics; Physics, Applied; Science & Technology; Physics; Physical Sciences
• Research Categories: Physics, Optics; Biophysics, Medical

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.