Projects:DiffusionGraphBasedConnectivity

Back to UNC Algorithms

Muti-directional graph propagation for Diffusion Imaging based Connectivity

This project focuses on connectivity measurements derived from diffusion imaging datasets in order to better understand cortical and subcortical white matter connectivity. Our research employs a novel, multi-directional graph propagation method that performs a fully deterministic, efficient and stable connectivity computation. The method handles crossing fibers and deals well with multiple seed regions. In addition to the analysis of these connectivity measures in describing brain pathology, they can also be used as scalar maps for use in DTI registration.

Description

Slicer visualization of genu connectivity map overlaid on streamline genu tracts

Publications

• NA-MIC Publication Database on Graph based connectivity

Key Investigators

• UNC Algorithms: Ipek Oguz, Alexis Boucharin, Martin Styner