Difference between revisions of "Modules:StochasticTractography-Documentation-3.4"

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* Step3 : create a DTI (Diffusion Tensor Image) tensor with standard measures like fractional anisotropy (FA), mode and trace,
 
* Step3 : create a DTI (Diffusion Tensor Image) tensor with standard measures like fractional anisotropy (FA), mode and trace,
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** produce different measures based on the tensor like fractional anistropy (FA), mode and trace
 
{|
 
{|
 
|[[Image:tensor2.png|thumb|500px|Tensor step]]
 
|[[Image:tensor2.png|thumb|500px|Tensor step]]
 
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** produce different measures based on the tensor like fractional anistropy (FA), mode and trace
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{|
 
|[[Image:fa2.png|thumb|500px|Tensor step: FA]]
 
|[[Image:fa2.png|thumb|500px|Tensor step: FA]]
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* Step4 : run tractography and produce connection maps in case 2 ROIs are given
 
* Step4 : run tractography and produce connection maps in case 2 ROIs are given
 
** showing union and intersection of both maps from region A to region B and B to A
 
** showing union and intersection of both maps from region A to region B and B to A
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** showing only tracts connecting A to region B and B to A
 
{|
 
{|
 
|[[Image:AorB2.png|thumb|500px|Union of A to B and B to A]]
 
|[[Image:AorB2.png|thumb|500px|Union of A to B and B to A]]
 
|[[Image:AandB2.png|thumb|500px|Intersection of A to B and B to A]]
 
|[[Image:AandB2.png|thumb|500px|Intersection of A to B and B to A]]
 
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|}
** showing only tracts connecting A to region B and B to A
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{|
 
|[[Image:connectAB2.png|thumb|500px|ROI filtering]]
 
|[[Image:connectAB2.png|thumb|500px|ROI filtering]]

Revision as of 19:41, 15 April 2009

Home < Modules:StochasticTractography-Documentation-3.4
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Module Name

Stochastic Tractography

Corpus callosum with stochastic tractography
Corpus callosum lateral projections

General Information

Module Type & Category

Type: Interactive

Category: DTI

Authors, Collaborators & Contact

  • Author: Julien von Siebenthal
  • Contributor: Steve Pieper
  • Contact: jvs@bwh.harvard.edu

Module Description

As a main purpose, the stochastic tractography module helps to evaluate connectivity between two regions of the Grey Matter of the brain using ROIs (Region Of Interest) as inputs. These ROIs define grey matter regions ensuring a specific neurophysiological function. Extensively, study involving more than two regions could still be done by pairing the regions two by two and computing them separetely to finally gather the results.

Usage

  • want to study fiber paths from a single region of interest (ROI)
  • want to evaluate connectivity between two ROIs


Description

Stochastic tractography panel

The stochastic tractography module interface implements the following workflow:

  • Step 1 : smooth using a Half Width Full Maximum gaussian filter,
Smoothing step
  • Step2 : generate a brain mask,
Brain mask step
  • Step3 : create a DTI (Diffusion Tensor Image) tensor with standard measures like fractional anisotropy (FA), mode and trace,
    • produce different measures based on the tensor like fractional anistropy (FA), mode and trace
Tensor step
Tensor step: FA
Tensor step: mode
Tensor step: trace
  • Step4 : run tractography and produce connection maps in case 2 ROIs are given
    • showing union and intersection of both maps from region A to region B and B to A
    • showing only tracts connecting A to region B and B to A
Union of A to B and B to A
Intersection of A to B and B to A
ROI filtering
ROI filtering from A to B
ROI filtering from B to A

Quick Tour of Features and Use

List all the panels in your interface, their features, what they mean, and how to use them. For instance:

  • IO panel:

IOmenu.png

  • Smoothing panel:

Smoothmenu.png

  • Brain Mask panel:

Maskmenu.png

  • Diffusion Tensor panel:

Tensormenu.png

  • Tractography panel:

Tractomenu.png

  • Connectivity Map panel:

Connectmenu.png

Development

Dependencies

Volumes

Known bugs

Follow this link to the Slicer3 bug tracker.


Usability issues

Follow this link to the Slicer3 bug tracker. Please select the usability issue category when browsing or contributing.

Source code & documentation

More Information

Acknowledgment

National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149 (to Ron Kikinis, Marek Kubicki).

References