Efficient Diffusion Connectivity via Multidirectional Fstar
- UNC: Alexis Boucharin, Clément Vachet, Yundi Shi, Martin Styner
- Emory University: Mar Sanchez
In this work, we present a novel method for computing global connectivity to a given source region, which is more resilient to noise, based on a deterministic computation, converges faster and is able to solve crossing fibers issues. Our method is well equipped to perform quantitative investigations of neural development, both for healthy populations and for clinical studies.
Starting from acquired DWI data, we first estimate the local orientation distribution function (ODF) with existing, standard methods. The ODF provide a voxel-wise continuous distribution of the diffusion properties. This continuous distribution is then sharpened and finally sampled using an icosahedron spherical subdivision allowing for a high number of directions (100-200) per voxel. The main part of the algorithm performs a multi-directional F-star graph algorithm that uses the local diffusion information to compute global probabilities. At each step of the computation the current, cumulative connectivity is propagated within a 26 neighborhood via the sampled diffusion directions. Finally the global connectivity probability is obtained at each voxel by integrating locally over the cumulative connectivity along each diffusion direction. This enables us also to track the most probable path from any voxel to the source region.
We continued the tests on real datasets (rodents, primates and humans) and analyzed them. We also started to write a technical paper, and its presentation, about the method for a later submission.