Two-tensor tractography in Slicer using Python and Teem

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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].

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.

Besides the integration of 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 (one idea is to use IPython).

Progress

The integration of the Two-Tensor Tractography into Slicer is finished. Currently I'm testing the algorithm with different parameter settings and try to find a way to parallelize the algorithm.


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

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