joshua new
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
JN 04/21/23
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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
JN 04/21/23
www.cs.utk.edu/~seelab
• 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
JN 04/21/23
www.cs.utk.edu/~seelab
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
JN 04/21/23
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Background
...
...
0.002400
0.037610
000
MRI
fMRI Volume VolumeNormalization
DTFiber Tracts
Normalized Tracts
Visualization
JN 04/21/23
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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 [email protected]
– 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
JN 04/21/23
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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?
JN 04/21/23
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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)
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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