2008 Winter Project Week:Particle Correspondence DTI

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Home < 2008 Winter Project Week:Particle Correspondence DTI
Particles on an inflated cortex - the color map shows the local sulcal depth measurements


Key Investigators

  • UNC: Ipek Oguz, Martin Styner
  • Utah: Josh Cates, Tom Fletcher, Ross Whitaker
  • GE: Xiaodong Tao
  • Kitware: Sebastien Barre

Objective

We are working on a general framework for computing cortical correspondence in a population-based manner. This project is extending the entropy based particle system by letting the correspondence optimization driven by local measurements besides the spatial location of surface points.

The goal for the project week is to incorporate probabilistic connectivity maps computed from DTI images.

See our Project Page for more details.

Approach, Plan

Our approach is described by the references below. The main objective for this project week is to compute probabilistic connectivity maps from DTI images in a format that can be used in our entropy based particle framework. We also want to evaluate the resulting correspondence maps using fMRI data.

Progress

We have started developing two Slicer 3 modules, one for the particle correspondence and for the preprocessing/postprocessing that interfaces with the correspondence module. Both modules should be ready within next few weeks. We also have made some initial progress in using the Stochastic Tractography method for obtaining connectivity maps to be incorporated with the cortical correspondence.



References

  • Ipek Oguz, Joshua Cates, Thomas Fletcher, Ross Whitaker, Derek Cool, Stephen Aylward, Martin Styner, "Entropy-Based Particle Systems and Local Features for Cortical Correspondence Optimization," submitted to ISBI 2008.
  • J. Cates, T. Fletcher, and R. Whitaker, "Entropy-based particle systems for shape correspondence," Mathematical Foundations of Computational Anatomy Workshop, MICCAI 2006, pp. 90–99, Oct. 2006.
  • J. Cates, T. Fletcher, M. Styner, M. Shenton, and R. Whitaker, “Shape modeling and analysis with entropy-based particle systems,” in IPMI, 2007, pp. 333–345.