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http://hdl.handle.net/1926/39
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A Quantitative DTI Fiber Tract Analysis Suite
Presenter: Casey GoodlettIsabelle CorougeMatthieu Jomier
Guido Gerig
Outline
• Motivation (introduction to DTI)• Open source technology• Tractography• Clustering and manual editing• Analysis of fiber tracts• Contributions to open source
Acknowledgements
• NeuroLib developers– Pierre Fillard– Sylvain Gouttard– Matthieu Jomier– Isabelle Corouge– Clément Vachet– Rémi Jean– Casey Goodlett
• Supervisor– Guido Gerig
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.
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
Processing Pipeline
MRI AcquisitionTensor Estimation
(FiberTracking)ROI Definition(InsightSNAP)
Tractography(FiberTracking)
Clustering(FiberViewer)
Manual Editing(FiberViewer)
Tract Analysis(FiberViewer)
Visualization(FiberViewer)
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
fiberviewer.avi
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
Clustering Example
Fornix Clustering
Manual Editing Tools
• Cutting fibers• Cluster selection• Fiber re-
parameterization
Visualization
• Image visualization– DWI images– Derived properties (FA, MD, eigenvalues)– Tensor ellipsoids
• Fiber Visualization– Geometry– Derived properties– Full tensor information– Mean fiber
Visualization• SOViewer• Levels of visualization
– Geometry
– Derived properties
– Tensors
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
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
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
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
Questions/Comments
• For more information and the software http://www.ia.unc.edu/dev