Difference between revisions of "Modules:StochasticTractography-Documentation"
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[[Image:Arcuatetractvolume2.png|thumb|Left|Volume Rendering of the stochastic field]] | [[Image:Arcuatetractvolume2.png|thumb|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. | ||
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| + | By incorporating uncertainty due to fiber crossings, imaging noise and resolution, stochastic tractography can robustly extract fiber bundles when streamline tractography cannot. | ||
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| + | 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. | ||
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This is an implementation of the algorithm described in this [http://slicer.spl.harvard.edu/pages/Special:PubDB_View?dspaceid=1087 paper]. | This is an implementation of the algorithm described in this [http://slicer.spl.harvard.edu/pages/Special:PubDB_View?dspaceid=1087 paper]. | ||
=First Panel= | =First Panel= | ||
Revision as of 06:16, 18 March 2008
Home < Modules:StochasticTractography-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.
