Difference between revisions of "Efficient Diffusion Connectivity via Multidirectional Fstar"
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| − | <h3> | + | <h3>Objective</h3> |
| − | + | 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. | |
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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. | 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. | ||
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Our method has been successfully tested on synthetic datasets such as unique fibers, crossing fibers, or loop situations. Tests on real datasets, primate and human, are currently in progress. | Our method has been successfully tested on synthetic datasets such as unique fibers, crossing fibers, or loop situations. Tests on real datasets, primate and human, are currently in progress. | ||
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Revision as of 14:09, 16 June 2010
Home < Efficient Diffusion Connectivity via Multidirectional FstarKey Investigators
- Alexis Boucharin, Clément Vachet, Yundi Shi, Mar Sanchez, Martin Styner
Project
Objective
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.
Approach, Plan
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.
Progress
Our method has been successfully tested on synthetic datasets such as unique fibers, crossing fibers, or loop situations. Tests on real datasets, primate and human, are currently in progress.
