a quantitative dti fiber tract analysis suite-898

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