A Quantitative DTI Fiber Tract Analysis Suite-898

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http://hdl.handle.net/1926/39

http://hdl.handle.net/1926/39

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  • 1. A Quantitative DTI Fiber Tract Analysis Suite Presenter: Casey Goodlett Isabelle Corouge Matthieu Jomier Guido Gerig
  • 2. Outline
    • Motivation (introduction to DTI)
    • Open source technology
    • Tractography
    • Clustering and manual editing
    • Analysis of fiber tracts
    • Contributions to open source
  • 3. Acknowledgements
    • NeuroLib developers
      • Pierre Fillard
      • Sylvain Gouttard
      • Matthieu Jomier
      • Isabelle Corouge
      • Clément Vachet
      • Rémi Jean
      • Casey Goodlett
    • Supervisor
      • Guido Gerig
  • 4. Open Software Libraries
    • ITK
    • NA-MIC/ITK Sandbox
    • VTK
    • SOViewer (J. Jomier)
    • QT ®
    • SNAP (Yushkevich)
    Qt and the Qt logo are trademarks of Trolltech in Norway, the United States and other countries.
  • 5. DTI Fundamentals
    • DTI is a measure of the diffusion properties of water in the brain
    • Diffusion is estimated as a 3x3 symmetric positive-definite matrix which is the covariance of 3D gaussian brownian motion
    Isotropic Anisotropic Images from S. Mori
  • 6. Processing Pipeline MRI Acquisition Tensor Estimation (FiberTracking) ROI Definition (InsightSNAP) Tractography (FiberTracking) Clustering (FiberViewer) Manual Editing (FiberViewer) Tract Analysis (FiberViewer) Visualization (FiberViewer)
  • 7. Tensor Estimation and Tractography
    • Tensor Estimation is explicit (7 images)
    • External Tool used to estimate more images for now (Xiadong Tao)
    • Pathways generated from forward integration through tensor field
    • Backward tracking used to improve the stability of tracking
    • ROI Specification via InsightSNAP
  • 8.  
  • 9.  
  • 10. Clustering Fiber Tracts
    • Hierarchical Agglomerative clustering
      • Distance Metrics
        • Hausdorff
        • Mean
        • Length
        • Center of Gravity
      • Threshold determines cluster membership
    • Spectral Clustering using Normalized Cut
      • Graph-theoretic approach
  • 11. Clustering Example
  • 12. Fornix Clustering
  • 13. Manual Editing Tools
    • Cutting fibers
    • Cluster selection
    • Fiber re-parameterization
  • 14. Visualization
    • Image visualization
      • DWI images
      • Derived properties (FA, MD, eigenvalues)
      • Tensor ellipsoids
    • Fiber Visualization
      • Geometry
      • Derived properties
      • Full tensor information
      • Mean fiber
  • 15. Visualization
    • SOViewer
    • Levels of visualization
      • Geometry
      • Derived properties
      • Tensors
  • 16. Analysis of Fiber Tracts
    • Fibers are used as a coordinate system for computing the statistics of DTI data
    • Process
      • Establish an origin
      • Reparameterize fibers
      • Interpolation via Riemannian metric (Fletcher et al)
      • Average tensor data at corresponding arc length
    • Properties to analyze
      • Full tensor
      • Derived properties
  • 17.  
  • 18.  
  • 19. Applications
    • Currently being assessed in neurodevelopmental study of children (normal, at-risk for autism, at-risk for schizophrenia)
    • Evaluations being performed by other labs into possible studies using tract-oriented statistics
  • 20. Benefits from Open Source
    • Large body of readily available image and geometry processing algorithms
    • Common data format for processing intensity and diffusion images as well as tube spatial objects
    • High quality flexible visualization methods
  • 21. Contributions to Open Source
    • Executables freely available
    • FiberTracking is available in NeuroLib CVS
    • FiberViewer source available shortly
    • Clustering algorithm in sandbox
    • Inter-operability with other tools
    • Fiber analysis modules will be contributed through NAMIC
  • 22. Questions/Comments
    • For more information and the software http://www.ia.unc.edu/dev