www.cs.utk.edu/~seelab jn 10/13/2015 cs690 vis papers dti tractography background “evaluation of...
TRANSCRIPT
JN 04/21/23
www.cs.utk.edu/~seelab
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
JN 04/21/23
www.cs.utk.edu/~seelab
• 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
JN 04/21/23
www.cs.utk.edu/~seelab
• 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
JN 04/21/23
www.cs.utk.edu/~seelab
• 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
www.cs.utk.edu/~seelab
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
www.cs.utk.edu/~seelab
Background
...
...
0.002400
0.037610
000
MRI
fMRI Volume VolumeNormalization
DTFiber Tracts
Normalized Tracts
Visualization
JN 04/21/23
www.cs.utk.edu/~seelab
Background
0.002400
0.037610
000
Tensor at eachvoxel location
MRI Density
JN 04/21/23
www.cs.utk.edu/~seelab
Background
• Mat2img – data normalization (SPM2)
JN 04/21/23
www.cs.utk.edu/~seelab
Fiber Tractography
DT-MRI
Seed Point
JN 04/21/23
www.cs.utk.edu/~seelab
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
www.cs.utk.edu/~seelab
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
JN 04/21/23
www.cs.utk.edu/~seelab
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
JN 04/21/23
www.cs.utk.edu/~seelab
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
JN 04/21/23
www.cs.utk.edu/~seelab
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
JN 04/21/23
www.cs.utk.edu/~seelab
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
JN 04/21/23
www.cs.utk.edu/~seelab
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
JN 04/21/23
www.cs.utk.edu/~seelab
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
www.cs.utk.edu/~seelab
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
www.cs.utk.edu/~seelab
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
JN 04/21/23
www.cs.utk.edu/~seelab
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
JN 04/21/23
www.cs.utk.edu/~seelab
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
JN 04/21/23
www.cs.utk.edu/~seelab
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
www.cs.utk.edu/~seelab
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
www.cs.utk.edu/~seelab
Vis Paper II
The Coolest Part
JN 04/21/23
www.cs.utk.edu/~seelab
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
www.cs.utk.edu/~seelab
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
www.cs.utk.edu/~seelab
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