Evaluation of Fiber Clustering Methods for Enhanced DTI Visualization

1. CS690 Vis PapersDTI Tractography Background“Evaluation of Fiber Clustering Methods for Diffusion Tensor Imaging”“Fast and Reproducible Fiber Bundle Selection in DTI Visualization” Joshua New

2. Backgroundhttp://science.howstuffworks.com/mri1.htm • Atom’s nucleus precesses around an axis like a top • Main magnetic field aligns atoms’ axes (toward patient’s head or feet) • Opposing directions cancel each other out except for a few out of every million • Radio waves change precession of atoms

3. Backgroundhttp://science.howstuffworks.com/mri1.htm • Magnetic – 0.5-2 tesla (10K Gauss) machines on humans, up to 60 tesla used in research (resistive, permanent, and superconducting magnets with -452oF liquid He) • Resonance – a local radio frequency pulse precesses atoms in direction and frequency based upon magnetic field and type of tissue • Image – coils measure energy radiated in a “slice” as atoms drift back to their normal precession and convert through Fourier to an image

4. Backgroundhttp://science.howstuffworks.com/mri1.htm • Disadvantages: • Patients with pacemakers, claustrophobia, weight • Noise of continuous rapid hammering from current in wires being opposed by the main magnetic field • Must hold still for 20-90 minutes during scan • Artifacts from implants altering the magnetic field • Very expensive to own and operate • Typical voxel resolution is 2.5mm whereas human nerves have diameter 1-12μm: A-b 5-12μm (60m/s); A-d 2-5μm (5-25m/s); C 1μm (1m/s)

5. Backgroundhttp://science.howstuffworks.com/mri1.htm • Advantages: • Imaging of density is similar to X-rays • Slice direction: axial, sagittal, and coronal • Resolution for voxels 0.2-5mm per side (~2.5) • Non-invasive inspection of: multiple sclerosis, tumors, infections, torn ligaments, shoulder injuries, tendonitis, cysts, herniated disks, and stroke • Future of MRI • Wearable MRI devices • Modeling the brain

6. Background

7. Barycentric Space Extract Major Eigenvectors Background • Diffusion Tensor MRI • Diffusion – the process or condition of being spread about or scattered; disseminated • Tensor – mathematical generalization of a vector • DT-MRI shows direction and magnitude of fluid flow in the brain (brain is ~78% water)

8. Background VolumeNormalization Volume fMRI MRI Fiber Tracts DT Normalized Tracts Visualization

9. MRI Density Tensor at eachvoxel location Background

10. Background • Mat2img – data normalization (SPM2)

11. Fiber Tractography DT-MRI Seed Point

12. Vis Paper I Evaluation of Fiber Clustering Methods for Diffusion Tensor Imaging Bart Moberts* Anna Vilanova† Jarke J. van Wijk‡ Dept of Mathematics and Computer Science* ‡ Department of Biomedical Engineering † Technische Universiteit Eindhoven Eindhoven, The Netherlands

13. Vis Paper I • Data • 3 sets: 128x128x30 @ 1.8x1.8x3.0mm • Whole volume seeding using DTITool (ROI problem “user biased, not reproducible”) • 3500-5000 fibs15-20m on P4@2.5Ghz • Remove fibers shorterthan 20mm

14. Vis Paper I • Ground Truth for Clusters (define bundles) • 2 physicians from Máxima Med Center agree w/ classification • 6 bundles corpus callosum (cc)fornix (fx)cingulum (cgl, cgr)corona radiata (crl, crr) • Any fibers not labeledare not part of groundtruth BottomView Top View

15. Vis Paper I • Clustering Methods • Agglomerative hierarchical clustering (each fiber in own cluster then join most similar) • Single-link (min distance between a pair) • Complete-link (max dist between a pair) • *Weighted* average of max & min • Shared Nearest Neighbors (new to fibers) • k-nearest neighbor graph at each vertex • Edge weight based on number and ordering of shared neighbors (normalized distance?) • Cluster by removing edges below weight τ

16. Good Incomplete #Bndls Incorrect Good #Clstrs c b Vis Paper I • Clustering Validation • Rand Index (normalized goodness) • Adjust for agreement by chance • assuming hypergeometric distribution yields • use supported by Milligan & Cooper

17. Vis Paper I • Results(Oops) • Explanations • Rand on level of fiber, not on level of bundles (high AR when CC is complete); Normalized AR (NAR) • Incorrectness more detrimental than incompletenessWeighted NAR (WNAR); optimal 75% correctness

18. /Min Dist /Avg Dist /Max Dist Vis Paper I • One equation to rule them all • Results(again)

19. Clusters Fig 1d Fig 1b

20. Vis Paper I • Summary Quotes • Difference in clustering quality between the hierarchical single-link method and SSN method is minimal • Values of [the SSN] parameters did not show any relation with the optimal clusterings • [In relation to α=0.75] This experiment was too small to be statistically significant

21. Vis Paper I • Other Quotes from the paper • α=0.75 does make a difference • Clustering obtained by cutting the dendogram at the level of 141 clusters • Optimal parameter settings for the first data set… • OVERFITTING! • Suggestions • Cluster based on fiber’s median vertex position • Better yet: why not use a weighted voting of all clustering algorithms?

22. Vis Paper II “Fast and Reproducible Fiber Bundle Selection in DTI Visualization” Jorik Blaas*, Charl P. Botha*, Bart Peters †, Frans M. Vos ‡; ‡ ‡ and Frits H. Post* *Data Visualization Group, Delft University of Technology † Psychiatric Centre, Academic Medical Centre, Amsterdam ‡ Quantitative Imaging Group, Delft University of Technology ‡ ‡ Dept. of Radiology, Academic Medical Centre, Amsterdam The Netherlands

23. Vis Paper II • Motivation • Interactive bundle selection by brain experts, supported by real-time visualization • Fiber selections be reproducible (different experts achieve the same results) • Method • Fiber vertices in kd-tree split atvert median in given direction • Convex polyhedron coverage • Vertices linked to fibers

24. Vis Paper II • Method Details • Polyhedron as intersection of half-spaces • Node of kd-tree fullyinside, fully outside, orpartially inside • Inside (all Bbox cornerscontained by P) • Outside (a halfspace ofP contains no pts) • Partial (neither, recurse)

25. Vis Paper II • Implementation • Multiple P-tests as bit vector, logical AND of multiple boxes (fibers go through all boxes) • Also NOT a box’s bit to eliminate fibers (pruning) • Bounding boxes freely positioned, rotated, and resized (polyhedron, so don’t have to be axis-aligned) • TEEM used for preprocessing fiber tractography • Support progressive update for high frame rate • Customizable user interface • C++ Windows&Linux (few external libraries)

26. Vis Paper II • Validation “Fast and Reproducible” • Real-time selection and rendering • Pm 1.6Ghz @ [1.0,2.0]M fib/secP4 3Ghz @ [1.5,3.5]M fib/sec • Previous work with general collision detection libs 1.6Ghz @ [80,220]K fib/sec • Stable average FA over selected regions • 2 users, 10 datasets, l/r cingulum @ 2m each • Nonparametric Spearman correlation • left .903, right .976, two-tailed significance 0.001

27. Vis Paper II The Coolest Part

28. Vis Paper III (why not?) A System for Comparative Visualization of Brain Nerve Fiber Tracts Joshua R. New†, Jian Huang†, and Zhaohua Ding‡ †Department of Computer Science, The University of Tennessee, Knoxville, TN ‡ Vanderbilt University Institute of Imaging Science, Nashville, TN

29. Vis Paper III PreviousVis04(1 BBox) ThemVis05(3 BBox) Us Vis05 Reject (10 features = 3.3 BBox) ?

30. Vis Paper III FiberRenderer – 4.8K fibers; 350.3K verts