This document summarizes a quantitative diffusion tensor imaging (DTI) fiber tract analysis software suite. It outlines the motivation, open source technologies used, tractography and clustering algorithms, manual editing tools, analysis of fiber tracts, applications, and contributions to open source. The suite processes DTI data through tensor estimation, tractography, hierarchical clustering of fibers, manual editing, visualization, and analysis of fiber properties along tracts. It is being used to study neurodevelopment and is available as open source software.

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

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

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