Difference between revisions of "2009 Summer Project Week Slicer3 Fibre Dispersion"
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Our code for computing these measures is already written in Python, and our objective is to create a new python module in Slicer3 that will make these tools available to the community. | Our code for computing these measures is already written in Python, and our objective is to create a new python module in Slicer3 that will make these tools available to the community. | ||
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Our method will be published in MICCAI 2009 and is already implemented in Python. | Our method will be published in MICCAI 2009 and is already implemented in Python. | ||
| + | During the programming week, we integrated the implementation of our method with Slicer as a Python module. We still need to improve the user interface of the module. For the future, a method should be developed to facilitate the interaction between Nrrd objects in Teem and numpy arrays in Python. | ||
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| + | ==References== | ||
| + | *Savadjiev P, Kindlmann G, Bouix S, Shenton ME, Westin C-F. Local white matter geometry indices from diffusion tensor gradients. In Proc. MICCAI 2009, In press. | ||
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Latest revision as of 20:30, 26 June 2009
Home < 2009 Summer Project Week Slicer3 Fibre Dispersion
Key Investigators
- BWH: Peter Savadjiev, Carl-Fredrik Westin, Sylvain Bouix
Objective
We have developed a method (based on Teem) for computing geometrical properties of white matter fibres directly from diffusion tensor fields. This method is decribed in a paper that will appear in MICCAI 2009. The key idea is to isolate the portion of the gradient of the tensor field corresponding to local variation in tensor orientation, and to project it onto a coordinate frame of tensor eigenvectors. The resulting eigenframe-centered representation makes it possible to define scalar geometrical measures that describe the underlying white matter fibres, directly from the diffusion tensor field and its gradient, without requiring prior tractography. We define two new scalar measures of (1) fibre dispersion and (2) fibre curving, which are illustrated with various examples in our MICCAI paper.
Our code for computing these measures is already written in Python, and our objective is to create a new python module in Slicer3 that will make these tools available to the community.
Approach, Plan
We will follow the standard procedure for creating Python modules for Slicer3.
Progress
Our method will be published in MICCAI 2009 and is already implemented in Python.
During the programming week, we integrated the implementation of our method with Slicer as a Python module. We still need to improve the user interface of the module. For the future, a method should be developed to facilitate the interaction between Nrrd objects in Teem and numpy arrays in Python.
References
- Savadjiev P, Kindlmann G, Bouix S, Shenton ME, Westin C-F. Local white matter geometry indices from diffusion tensor gradients. In Proc. MICCAI 2009, In press.
