Difference between revisions of "2009 Winter Project Week StochasticTractography"

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|[[Image:NAMIC-SLC.jpg|thumb|320px|Return to [[2009_Winter_Project_Week|Project Week Main Page]] ]]
 
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===Key Investigators===
 
===Key Investigators===
* BWH: Marek Kubicki, Julien von Siebenthal
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* BWH: Marek Kubicki, Julien de Siebenthal
  
 
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<h1>Objective</h1>
 
<h1>Objective</h1>
Stochastic Tractography module has the ultimate goal to offer an estimation of the probability of white matter connection between two regions of the brain. For that, connectivity map is the fundamental output of the current algorithm implementation. It reveals given two regions the distribution of probability of connection per voxel.  
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Stochastic Tractography module has the ultimate goal to offer an estimation of the probability of white matter connection between two regions of the brain. For that, connectivity map is the fundamental output of the current algorythm implementation. It reveals given two regions the distribution of probability of connection per voxel.  
  
 
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<h1>Progress</h1>
 
<h1>Progress</h1>
The pipeline is implemented and fully functional. First test series show accurate adequacy between MatLAB and Python numerical computations of the Stochastic Tractography algorythm.
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The pipeline is implemented and functional. First test series show accurate adequacy between MatLAB and Python numerical computations of the Stochastic Tractography algorythm. We would like during NAMIC integrate already existing Python wrapping of the Rician and Eddy current correction filters. An ideal wrapping solution for Slicer is indeed still to be defined. Moreover, we would like also during NAMIC to find out an EPI correction filter or if needed prototype a new one.
  
  

Latest revision as of 16:51, 16 December 2008

Home < 2009 Winter Project Week StochasticTractography
St helix test 01 GUI.png



Key Investigators

  • BWH: Marek Kubicki, Julien de Siebenthal

Objective

Stochastic Tractography module has the ultimate goal to offer an estimation of the probability of white matter connection between two regions of the brain. For that, connectivity map is the fundamental output of the current algorythm implementation. It reveals given two regions the distribution of probability of connection per voxel.

Approach, Plan

A complete pipeline implementation was forseen in order to give to the researcher the best access to the different features Stochastic Tractography can offer. Based on the MatLAB implementation and constantly compared to it, a Python integration was devised. It offers as a proof of concept the possibility to directly prototype an algorithm with direct integration into the Slicer solution without using MatLAB. This should alleviate issues related to code translation from MatLAB to Slicer.

Progress

The pipeline is implemented and functional. First test series show accurate adequacy between MatLAB and Python numerical computations of the Stochastic Tractography algorythm. We would like during NAMIC integrate already existing Python wrapping of the Rician and Eddy current correction filters. An ideal wrapping solution for Slicer is indeed still to be defined. Moreover, we would like also during NAMIC to find out an EPI correction filter or if needed prototype a new one.



References