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Joshua New

CS690 Vis Papers DTI Tractography Background “Evaluation of Fiber Clustering Methods for Diffusion Tensor Imaging” “Fast and Reproducible Fiber Bundle Selection in DTI Visualization”. Joshua New. Background http://science.howstuffworks.com/mri1.htm.

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Joshua New

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  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

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