a quantitative dti fiber tract analysis suite-898
DESCRIPTION
http://hdl.handle.net/1926/39TRANSCRIPT
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A Quantitative DTI Fiber Tract Analysis Suite
Presenter: Casey GoodlettIsabelle CorougeMatthieu Jomier
Guido Gerig
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Outline
• Motivation (introduction to DTI)• Open source technology• Tractography• Clustering and manual editing• Analysis of fiber tracts• Contributions to open source
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Acknowledgements
• NeuroLib developers– Pierre Fillard– Sylvain Gouttard– Matthieu Jomier– Isabelle Corouge– Clément Vachet– Rémi Jean– Casey Goodlett
• Supervisor– Guido Gerig
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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.
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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
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Processing Pipeline
MRI AcquisitionTensor Estimation
(FiberTracking)ROI Definition(InsightSNAP)
Tractography(FiberTracking)
Clustering(FiberViewer)
Manual Editing(FiberViewer)
Tract Analysis(FiberViewer)
Visualization(FiberViewer)
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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
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fiberviewer.avi
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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
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Clustering Example
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Fornix Clustering
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Manual Editing Tools
• Cutting fibers• Cluster selection• Fiber re-
parameterization
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Visualization
• Image visualization– DWI images– Derived properties (FA, MD, eigenvalues)– Tensor ellipsoids
• Fiber Visualization– Geometry– Derived properties– Full tensor information– Mean fiber
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Visualization• SOViewer• Levels of visualization
– Geometry
– Derived properties
– Tensors
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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
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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
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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
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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
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Questions/Comments
• For more information and the software http://www.ia.unc.edu/dev