https://www.slicer.org/w/api.php?action=feedcontributions&feedformat=atom&user=Tringo 2026-05-09T17:38:33Z User contributions MediaWiki 1.33.0 https://www.slicer.org/w/index.php?title=File:IJdata.tar.gz&diff=3019 2008-03-18T06:34:27Z

<p>Tringo: </p> <hr /> <div></div>

Tringo https://www.slicer.org/w/index.php?title=File:SlicerSTtutorial.ogg&diff=3018 2008-03-18T06:24:26Z

<p>Tringo: </p> <hr /> <div></div>

Tringo https://www.slicer.org/w/index.php?title=File:Slicer3STModule.ppt&diff=3017 2008-03-18T06:21:07Z

<p>Tringo: </p> <hr /> <div></div>

Tringo https://www.slicer.org/w/index.php?title=Modules:StochasticTractography-Documentation&diff=3016 2008-03-18T06:18:34Z

<p>Tringo: /* First Panel */</p> <hr /> <div>[[Image:Arcuatetractvolume2.png|thumb|Left|Volume Rendering of the stochastic field]]<br /> =Introduction=<br /> 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.<br /> <br /> This is an implementation of the algorithm described in this [http://slicer.spl.harvard.edu/pages/Special:PubDB_View?dspaceid=1087 paper].<br /> <br /> =Tutorials=<br /> *[[Media:IJdata.tar.gz|Training Dataset]]<br /> *[[Media:Slicer3STModule.ppt|Training Presentation]]<br /> *[[Media:SlicerSTtutorial.ogg|Training Webcast]] ([http://www.theora.org Theora format])</div>

Tringo https://www.slicer.org/w/index.php?title=Modules:StochasticTractography-Documentation&diff=3015 2008-03-18T06:17:01Z

<p>Tringo: /* Introduction */</p> <hr /> <div>[[Image:Arcuatetractvolume2.png|thumb|Left|Volume Rendering of the stochastic field]]<br /> =Introduction=<br /> 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.<br /> <br /> This is an implementation of the algorithm described in this [http://slicer.spl.harvard.edu/pages/Special:PubDB_View?dspaceid=1087 paper].<br /> <br /> =First Panel=</div>

Tringo https://www.slicer.org/w/index.php?title=Modules:StochasticTractography-Documentation&diff=3014 2008-03-18T06:16:46Z

<p>Tringo: /* Introduction */</p> <hr /> <div>[[Image:Arcuatetractvolume2.png|thumb|Left|Volume Rendering of the stochastic field]]<br /> =Introduction=<br /> The stochastic tractography filter extracts nerve fiber bundles from DWI images. Unlike streamline tractography, stochastic tractography uses a probabilistic framework to perform tractography. <br /> <br /> By incorporating uncertainty due to fiber crossings, imaging noise and resolution, stochastic tractography can robustly extract fiber bundles when streamline tractography cannot. <br /> <br /> 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.<br /> <br /> This is an implementation of the algorithm described in this [http://slicer.spl.harvard.edu/pages/Special:PubDB_View?dspaceid=1087 paper].<br /> <br /> =First Panel=</div>

Tringo