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

From NAMIC Wiki
Jump to: navigation, search
Line 27: Line 27:
 
<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 this project week will focus on the following:
 
We are integrating the Two-Tensor Streamline Tractography into Slicer. The complete details on the method are summarized in [1]. Our approach for this project week will focus on the following:
−
Our approach for the Two-Tensor Tracto
 
  
−
. The computation of the rotation tangents is already implemented in the Teem library. The implementation of our project is to be carried out in Python, using the novel Python wrappings for the Teem library.
+
The implementation of our project is carried out in Python. The Two-Tensor Tractography calls are already implemented in the Teem library and can be accessed using the Python wrappings for the Teem.  
  
−
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>.
 
−
 
−
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>
  
Line 40: Line 36:
 
<h1>Progress</h1>
 
<h1>Progress</h1>
  
−
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>
 
</div>

Revision as of 03:45, 16 December 2008

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


Objective

Our objective is 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.


Approach, Plan

We are integrating the Two-Tensor Streamline Tractography into Slicer. The complete details on the method are summarized in [1]. Our approach for this project week will focus on the following:

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

Progress



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

Retrieved from "https://www.na-mic.org/w/index.php?title=Two-tensor_tractography_in_Slicer_using_Python_and_Teem&oldid=33653"