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 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.

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).

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.

Progress

The implementation of the Two-Tensor Tractography is already finished. Currently I'm testing the algorithm with different parameter settings.

Extension of the Two-Tensor Tractography module by fiudical seeding.

Plan: use Grid Wizard Enterprise tool (with the help of Marco Ruiz) to parallelize the execution of my 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
• [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.