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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

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• 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

Backgroundhttp://science.howstuffworks.com/mri1.htm

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• 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

Backgroundhttp://science.howstuffworks.com/mri1.htm

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• 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)

Backgroundhttp://science.howstuffworks.com/mri1.htm

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• 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

Backgroundhttp://science.howstuffworks.com/mri1.htm

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Background

JN 04/21/23

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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)

110

110

000Extract Major Eigenvectors

Barycentric Space

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Background

...

...

0.002400

0.037610

000

MRI

fMRI Volume VolumeNormalization

DTFiber Tracts

Normalized Tracts

Visualization

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Background

0.002400

0.037610

000

Tensor at eachvoxel location

MRI Density

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Background

• Mat2img – data normalization (SPM2)

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Fiber Tractography

DT-MRI

Seed Point

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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 EindhovenEindhoven, The Netherlands

JN 04/21/23

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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

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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 Top ViewBottomView

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Vis Paper I

• Clustering Methods– Agglomerative hierarchical clustering (each fiber in own

cluster then join most similar)

1. Single-link (min distance between a pair)

2. Complete-link (max dist between a pair)

3. *Weighted* average of max & min

4. Shared Nearest Neighbors (new to fibers)

o k-nearest neighbor graph at each vertexo Edge weight based on number and ordering

of shared neighbors (normalized distance?)

o Cluster by removing edges below weight τ

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Vis Paper I

• Clustering ValidationRand Index (normalized goodness)

Adjust for agreement by chance

assuming hypergeometric distribution yields

use supported by Milligan & Cooper

Incomplete

Incorrect

Good

Good

2

n

#Bndls

#Clstrs

b c)()(

)(

SExpValSMax

SExpValSS

0.1,0.0 M

daRand

Mmmmm

MmmaAR

)(2)(

)(

2121

21

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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 incompleteness

Weighted NAR (WNAR); optimal 75% correctness

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Vis Paper I

• One equation to rule them all

• Results(again)

/Min Dist /Avg Dist /Max Dist

R

i

S

ji

ijS

j

R

i i

ij

u

ng

u

nfwhere

1 12

22

1 1,

22 RRRff

RgfWNAR

Clusters

Fig 1b Fig 1d

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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

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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?

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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

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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 at

vert median in given direction– Convex polyhedron coverage– Vertices linked to fibers

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Vis Paper II

• Method Details– Polyhedron as intersection of half-spaces– Node of kd-tree fully

inside, fully outside, orpartially insidei. Inside (all Bbox corners

contained by P)

ii. Outside (a halfspace ofP contains no pts)

iii. Partial (neither, recurse)

n

iiHP

1

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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)

JN 04/21/23

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Vis Paper II

• Validation “Fast and Reproducible”– Real-time selection and rendering

i. Pm 1.6Ghz @ [1.0,2.0]M fib/secP4 3Ghz @ [1.5,3.5]M fib/sec

ii. Previous work with general collision detection libs 1.6Ghz @ [80,220]K fib/sec

– Stable average FA over selected regionsi. 2 users, 10 datasets, l/r cingulum @ 2m each

ii. Nonparametric Spearman correlation left .903, right .976, two-tailed significance 0.001

JN 04/21/23

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Vis Paper II

The Coolest Part

JN 04/21/23

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Vis Paper III (why not?)

A System for Comparative Visualization ofBrain 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

JN 04/21/23

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Vis Paper III

PreviousVis04(1 BBox)

ThemVis05(3 BBox)

UsVis05 Reject (10 features = 3.3 BBox)

Fiber QueryIntel Pentium Laptop 1.6Ghz [0.08-0.22]M fibs/sec

Fiber QueryIntel Pentium M 1.6Ghz

533Mhz FSB, 32K L1 inst, 32K L1 data, 2MB L2Intel Pentium 4 3.0 Ghz

800Mhz FSB, 28K L1, 1MB L2[1.5-3.5]M fibs/sec

[1.0-2.0]M fibs/sec

Over 10 Features Fiber QueryIntel Xeon 2.0 Ghz

400Mhz FSB, 8K L1, 512K L2AMD Athlon 64 2.2Ghz

1Ghz FSB, 64K L1 inst, 64K L1 data, 512K-1MB L2AMD AthlonXP 1800+ 1.53 Ghz

266Mhz FSB, 64K L1 inst, 64K L1 data (2way SA)256K L2 (16-way set associative, 64byte line size)

4.7M fibs/sec

4.9M fibs/sec

9.0M fibs/sec

?

JN 04/21/23

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Vis Paper III

FiberRenderer – 4.8K fibers; 350.3K verts10 Features Fiber Query

Intel Xeon 2.0 Ghz400Mhz FSB, 8K L1, 512K L2

AMD Athlon 64 2.2Ghz1Ghz FSB, 64K L1 inst, 64K L1 data, 512K-1MB L2

AMD AthlonXP 1800+ 1.53 Ghz266Mhz FSB, 64K L1 inst, 64K L1 data (2way SA)256K L2 (16-way set associative, 64byte line size)

4.7M fibs/sec

4.9M fibs/sec

9.0M fibs/sec

1280x600 viewport Frame RateNVIDIA Quadro FX 1000

AGP 4x, 128MB/400Mhz, 300Mhz core, 8 pixel pipesNVIDIA GeForce 7800GT

PCIx, 512MB/1Ghz, 400Mhz core, 20 pixel pipesNVIDIA GeForce FX 5500

AGP 8x, 256MB/400Mhz, 270Mhz core, 4 pixel pipes

25 fps 119.1K fib/sec 8.8M vert/sec

4 fps 38.1K fib/sec 1.4M vert/sec

2.8M vert/sec38.1K fib/sec8 fps

Vertex Query