Modules:StochasticTractography-Documentation - Revision history

Kikinis at 18:52, 10 April 2008

2008-04-10T18:52:54Z

Kikinis at 18:52, 10 April 2008

2008-04-10T18:52:05Z

Tringo: /* First Panel */

2008-03-18T06:18:34Z

First Panel

Tringo: /* Introduction */

2008-03-18T06:17:01Z

Introduction

Tringo: /* Introduction */

2008-03-18T06:16:46Z

Introduction

Kikinis: /* Introduction */

2008-02-06T18:07:25Z

Introduction

Kikinis at 13:59, 6 February 2008

2008-02-06T13:59:50Z

Kikinis at 13:47, 6 February 2008

2008-02-06T13:47:21Z

New page

=Introduction=
This is an implementation of the algorithm desctibed in this [http://slicer.spl.harvard.edu/pages/Special:PubDB_View?dspaceid=1087 paper].

=First Panel=

← Older revision		Revision as of 18:52, 10 April 2008
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		+	[[Documentation\|Return to Slicer Documentation]]
	[[Image:Arcuatetractvolume2.png\|thumb\|350px\|Left\|Volume Rendering of the stochastic field]]		[[Image:Arcuatetractvolume2.png\|thumb\|350px\|Left\|Volume Rendering of the stochastic field]]
	=Introduction=		=Introduction=

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-[[Image:Arcuatetractvolume2.png|thumb|Left|Volume Rendering of the stochastic field]]
+[[Image:Arcuatetractvolume2.png|thumb|350px|Left|Volume Rendering of the stochastic field]]
 =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.

← Older revision		Revision as of 06:18, 18 March 2008
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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~~=	+	=Tutorials=
		+	*[[Media:IJdata.tar.gz\|Training Dataset]]
		+	*[[Media:Slicer3STModule.ppt\|Training Presentation]]
		+	*[[Media:SlicerSTtutorial.ogg\|Training Webcast]] ([http://www.theora.org Theora format])

@@ Line 1: / Line 1: @@
 [[Image:Arcuatetractvolume2.png|thumb|Left|Volume Rendering of the stochastic field]]
 =Introduction=
-The stochastic tractography filter extracts nerve fiber bundles from DWI images.  Unlike streamline tractography, stochastic tractography uses a probabilistic framework to perform tractography.
+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].
 =First Panel=

@@ Line 1: / Line 1: @@
 [[Image:Arcuatetractvolume2.png|thumb|Left|Volume Rendering of the stochastic field]]
 =Introduction=
 This is an implementation of the algorithm described in this [http://slicer.spl.harvard.edu/pages/Special:PubDB_View?dspaceid=1087 paper].
 =First Panel=