Difference between revisions of "Modules:StochasticTractography-Documentation"
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| − | [[Image:Arcuatetractvolume2.png|thumb|Left|Volume Rendering of the stochastic field]] | + | [[Documentation|Return to Slicer Documentation]] |
| + | [[Image:Arcuatetractvolume2.png|thumb|350px|Left|Volume Rendering of the stochastic field]] | ||
=Introduction= | =Introduction= | ||
| − | + | The stochastic tractography filter extracts nerve fiber bundles from DWI images. Unlike streamline tractography, stochastic tractography uses a probabilistic framework to perform tractography. By incorporating uncertainty due to fiber crossings, imaging noise and resolution, stochastic tractography can robustly extract fiber bundles when streamline tractography cannot. The tracts generated by the stochastic tractography filter can be used to generate a connectivity probability image, which can be used to study connectivity between different regions of the brain. | |
| − | = | + | This is an implementation of the algorithm described in this [http://slicer.spl.harvard.edu/pages/Special:PubDB_View?dspaceid=1087 paper]. |
| + | |||
| + | =Tutorials= | ||
| + | *[[Media:IJdata.tar.gz|Training Dataset]] | ||
| + | *[[Media:Slicer3STModule.ppt|Training Presentation]] | ||
| + | *[[Media:SlicerSTtutorial.ogg|Training Webcast]] ([http://www.theora.org Theora format]) | ||
Latest revision as of 18:52, 10 April 2008
Home < Modules:StochasticTractography-DocumentationReturn to Slicer Documentation
Introduction
The stochastic tractography filter extracts nerve fiber bundles from DWI images. Unlike streamline tractography, stochastic tractography uses a probabilistic framework to perform tractography. By incorporating uncertainty due to fiber crossings, imaging noise and resolution, stochastic tractography can robustly extract fiber bundles when streamline tractography cannot. The tracts generated by the stochastic tractography filter can be used to generate a connectivity probability image, which can be used to study connectivity between different regions of the brain.
This is an implementation of the algorithm described in this paper.
