Difference between revisions of "Two-tensor tractography in Slicer using Python and Teem"

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* BWH: Carl-Fredrik Westin
 
* BWH: Carl-Fredrik Westin
 
* BWH: Gordon Kindlmann
 
* BWH: Gordon Kindlmann
 +
* BWH: Nobuhiko Hata
  
  
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<h1>Objective</h1>
 
<h1>Objective</h1>
−
To finalize the integration of Two-Tensor Tractography into Slicer and to test it on diverse DWI datasets. Furthermore the optimal parameter settings to run the algorithm needs to be investigated.   
+
Our objective is to finalize the integration of the Two-Tensor Streamline Tractography method in [1] into Slicer and to test it on diverse DWI datasets. Furthermore the optimal parameter settings to run the algorithm needs to be investigated.   
  
  
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<h1>Approach, Plan</h1>
 
<h1>Approach, Plan</h1>
 +
We are integrating the Two-Tensor Streamline Tractography into Slicer. The complete details on the method are summarized in [1].
  
−
Our approach for comparing the locations of scar to sites of RF ablation is summarized in the ISMRM 2008 reference below. The main challenge to this approach is to measure the distance between each scarred pixel, and each RF ablation site, and then the distance from each RF ablation site, to the nearest scarred pixel.  <foo>.
+
The implementation of our project is carried out in Python. The Two-Tensor Tractography function calls are already implemented in the Teem library and can be accessed using the Python wrappings for Teem.
 +
 
 +
Before integrating the Two-Tensor Tractography algorithm into Slicer, the optimal parameter settings for the algorithm needs to be investigated.
 +
 
 +
Furthermore we need to find a way to parallelize the tractography algorithm.
 +
 
 +
Another plan is to apply a noise filter on the DWI's before applying the Two-Tensor Tractography algorithm, as this may lead to more accurate fiber results.
 +
 
 +
Further, we want to extend the Two-Tensor Tractography module by fiducical seeding in order to be able to do interactive tractography seeding by using a tracking device.
  
−
Our plan for the project week is to first try to measure the closest distances between MRI scar and Carto data <bar>,and then to measure distances between Carto data and closest scar.  We also wish to colorize the Carto surface, based on voltage data.  We also wish to streamline the MR angiography segmentation method.
 
 
</div>
 
</div>
  
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<h1>Progress</h1>
 
<h1>Progress</h1>
 +
* The implementation of the Two-Tensor Tractography is already finished.
 +
 +
* Extension of the Two-Tensor Tractography module by fiudical seeding.
 +
 +
* Working with Marco Ruiz on getting my module working with GWE (Grid Wizard Enterprise) ([http://www.na-mic.org/Wiki/index.php/Engineering:UCSD]) in order to parallelize the execution of my algorithm. Luca Antiga helped by enabling Python modules to run as command line modules without starting the GUI so they can be executed by GWE.
  
−
Software for the registration between electrophysiology Carto data and the MR angiogram has been implemented, using the ITK/VTK platform (see ISMRM 2008 abstract, Taclas et al, and figure above). This week we wrote code to quantitatively determine the distances between each ablation location, and the closest region of scar, and to determine the distances between each pixel of scar, and the nearest ablation point.  Therefore we accomplished our goal!
 
  
 
</div>
 
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===References===
 
===References===
 +
* [1] Qazi AA, Radmanesh A, O'Donnell L, Kindlmann G, Peled S, Whalen S, Westin CF, Golby AJ. Resolving crossings in the corticospinal tract by two-tensor streamline tractography: method and clinical assessment using fMRI. Neuroimage 2008
 +
* [2] Peled S, Friman O, Jolesz F, Westin CF. Geometrically constrained two-tensor model for crossing tracts in DWI. Magnetic Resonance Imaging 2006;24:1263-1270.

Latest revision as of 17:04, 9 January 2009

Home < Two-tensor tractography in Slicer using Python and Teem
Return to Project Week Main Page



Key Investigators

  • BWH: Madeleine Seeland
  • BWH: Carl-Fredrik Westin
  • BWH: Gordon Kindlmann
  • BWH: Nobuhiko Hata


Objective

Our objective is to finalize the integration of the Two-Tensor Streamline Tractography method in [1] into Slicer and to test it on diverse DWI datasets. Furthermore the optimal parameter settings to run the algorithm needs to be investigated.


Approach, Plan

We are integrating the Two-Tensor Streamline Tractography into Slicer. The complete details on the method are summarized in [1].

The implementation of our project is carried out in Python. The Two-Tensor Tractography function calls are already implemented in the Teem library and can be accessed using the Python wrappings for Teem.

Before integrating the Two-Tensor Tractography algorithm into Slicer, the optimal parameter settings for the algorithm needs to be investigated.

Furthermore we need to find a way to parallelize the tractography algorithm.

Another plan is to apply a noise filter on the DWI's before applying the Two-Tensor Tractography algorithm, as this may lead to more accurate fiber results.

Further, we want to extend the Two-Tensor Tractography module by fiducical seeding in order to be able to do interactive tractography seeding by using a tracking device.

Progress

  • The implementation of the Two-Tensor Tractography is already finished.
  • Extension of the Two-Tensor Tractography module by fiudical seeding.
  • Working with Marco Ruiz on getting my module working with GWE (Grid Wizard Enterprise) ([1]) in order to parallelize the execution of my algorithm. Luca Antiga helped by enabling Python modules to run as command line modules without starting the GUI so they can be executed by GWE.



References

  • [1] Qazi AA, Radmanesh A, O'Donnell L, Kindlmann G, Peled S, Whalen S, Westin CF, Golby AJ. Resolving crossings in the corticospinal tract by two-tensor streamline tractography: method and clinical assessment using fMRI. Neuroimage 2008
  • [2] Peled S, Friman O, Jolesz F, Westin CF. Geometrically constrained two-tensor model for crossing tracts in DWI. Magnetic Resonance Imaging 2006;24:1263-1270.
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