Difference between revisions of "2010 Winter Project Week Stochastic Tractography"

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==Key Investigators==
 
==Key Investigators==
* UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
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* Previously: Julien von Siebenthal
* Utah: Tom Fletcher, Ross Whitaker
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* BWH: Andrew Rausch, Marek Kubicki
 
 
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<h3>Objective</h3>
 
<h3>Objective</h3>
We are developing methods for analyzing diffusion tensor data along fiber tracts. The goal is to be able to make statistical group comparisons with fiber tracts as a common reference frame for comparison.
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Test new Stochastic Tractography algorithm created by Julien
 
 
 
 
 
 
 
 
 
 
 
 
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<h3>Approach, Plan</h3>
 
 
 
Our approach for analyzing diffusion tensors is summarized in the IPMI 2007 reference below.  The main challenge to this approach is <foo>.
 
 
 
Our plan for the project week is to first try out <bar>,...
 
 
 
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<h3>Progress</h3>
 
Software for the fiber tracking and statistical analysis along the tracts has been implemented. The statistical methods for diffusion tensors are implemented as ITK code as part of the [[NA-MIC/Projects/Diffusion_Image_Analysis/DTI_Software_and_Algorithm_Infrastructure|DTI Software Infrastructure]] project. The methods have been validated on a repeated scan of a healthy individual. This work has been published as a conference paper (MICCAI 2005) and a journal version (MEDIA 2006). Our recent IPMI 2007 paper includes a nonparametric regression method for analyzing data along a fiber tract.
 
 
 
 
 
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Andrew is currently updating the tutorial for this new method. Current progress can be found here: [[File:Stochastic Jan09.ppt| stochastic tutorial powerpoint]]
  
 
==References==
 
==References==

Revision as of 23:53, 5 January 2010

Home < 2010 Winter Project Week Stochastic Tractography

Key Investigators

  • Previously: Julien von Siebenthal
  • BWH: Andrew Rausch, Marek Kubicki

Objective

Test new Stochastic Tractography algorithm created by Julien

Andrew is currently updating the tutorial for this new method. Current progress can be found here: File:Stochastic Jan09.ppt

References