arterial tortuosity measurement system
DESCRIPTION
Development of a systems for measuring arterial tortuosity from medical images for monitoring vascular disease.TRANSCRIPT
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Karl Diedrich
ARTERIAL TORTUOSITY MEASUREMENT SYSTEM FOR
EXAMINING CORRELATIONS WITH VASCULAR DISEASE
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Compare vascular disease to negativesVascular Disease No vascular disease
Aneurysm
High risk aneurysm relative (10% risk)
J.M. Farnham, N.J. Camp, S.L. Neuhausen, J. Tsuruda, D. Parker, J. MacDonald, and L.A. Cannon-Albright, “Confirmation of chromosome 7q11 locus for predisposition to intracranial aneurysm,” Human Genetics, vol. 114, Feb. 2004, pp. 250-5.
Normal aneurysm risk (5%)
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Centerlines with bifurcation guidesGreen dots at centerline bifurcations guide selection of end points
Anterior Cerebral artery (ACA) centerline selected
Cross section Projection
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Tortuosity measurement
Internal carotid artery
MCA-ACAbifurcation
L
d
End of slab
Distance Factor Metric (DFM) = Length(L)/distance between ends (d)
Repeated measurements, same patient
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Phantom tortuosity curves
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Imaging modalities
MRA shows only arteries CTA shows arteries and veins
Using simpler MRA images. Arteries are more significant to vascular disease than veins.
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MRI scanner
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Medical image segmentation
Time of Flight Magnetic Resonance Angiography images highlight flowing arterial blood
[1] D. L. Parker, B. E. Chapman, J. A. Roberts, A. L. Alexander, and J. S. Tsuruda, “Enhanced image detail using continuity in the MIP Z-buffer: applications to magnetic resonance angiography,” Journal of Magnetic Resonance Imaging: JMRI, vol. 11, no. 4, pp. 378-88, Apr. 2000.
Z-Buffer segmentation [1] of arteries
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MIP Z-buffer segmentation• Intensity is
position in image slice stack of maximum pixel intensity; dark is closer, brighter is farther
• Contiguous blood vessels are smooth
D. L. Parker, B. E. Chapman, J. A. Roberts, A. L. Alexander, and J. S. Tsuruda, “Enhanced image detail using continuity in the MIP Z-buffer: applications to magnetic resonance angiography,” Journal of Magnetic Resonance Imaging: JMRI, vol. 11, no. 4, pp. 378-88, Apr. 2000.
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Region growing threshold
0.20 histogram seed threshold 0.07 histogram seed threshold
0.20 histogram threshold slice
0.07 histogram threshold slice
Lowering region growing in 26 directions threshold
3 T
Noise
Aneurysm
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Hole FillNo filling Bubble filling
Voxel filling Bubble + voxel filling
• Bubble filling uses connected components to fill bubbles completely enclosed bubbles in aneurysm
• Voxel filing fills in individual voxels with artery neighbors in (variable) 24 of 26 directions within 8 voxels
• Bubble fill -> 3 voxel fills -> bubble fill
1.5 T scanner, region growing >= 0.20
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COMPARING PERFORMANCE OF CENTERLINE ALGORITHMS FOR QUANTITATIVE
ASSESSMENT OF BRAIN VASCULAR ANATOMY
Paper 1
Karl T. Diedrich, John A. Roberts, Richard H. Schmidt and Dennis L. Parker
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Least cost path centerline
Least cost paths back to goal node voxel
Goal node
Cost functions
Backtrace from distal ends to goal and remove short paths
Cross section
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Centerline
Path costs
Goal node
Branch meets previous line
Removed short path
This path made firstL. Zhang et al., “Automatic detection of three-dimensional vascular tree centerlines and bifurcations in high-resolution magnetic resonance angiography,” Investigative Radiology, vol. 40, no. 10, pp. 661-71, Oct. 2005.
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Modified Distance From Edge (MDFE)• Increase MDFE of central voxels (V).• MDFE(Vi) = DFE(Vi) + N(Vi)/Nmax
• N(Vi) = neighbor voxels with same DFE• Nmax = possible neighbours
DFE MDFE
Cross sections
Higher intensity in image is higher value
Center voxel has same DFE in Z
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Inverse cost function
Cost(Vi) = A * (1 - MDFE(Vi)/max_MDFE(Vi) )b +1
Inverts to make lower cost internal
MDFE Cost
Lower intensity lower cost
Inversion cost function
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Modified Distance From Edge (MDFE)
MDFE cross section
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Center of mass movement
Segmentation
Mean x, y, z position of each voxel, Vi, and up to 26 neighbors; Repeat.
Accumulate the distance moved
Segmentation collapsing to center of mass (COM)
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Center of mass cost
COM cost is the total distance move. Exterior voxels move farther to COM; higher cost
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Binary thinned artery
Binary thinning (BT) erodes segmentation to single lines. Pass to centerline algorithm to prune short branches.
H. Homman, “Insight Journal - Implementation of a 3D thinning algorithm,” 12-Oct-2007. [Online]. Available: http://www.insight-journal.org/browse/publication/181. [Accessed: 26-Mar-2010].
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COM
Multiple centerlines stability test
First goal node
Second round goal nodes
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Green known centerline. Red calculated centerline. Yellow is overlap.
Phantom stability & accuracy
E-F) BT-MDFE G-H) BT-COM
A-B) MDFE C-D) COM
Stability Accuracy
Instability, brighter centerline
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Algorithm Stability RMSE of
Accuracy
MDFE 0.880 0.240
COM 0.980 0.610
BT-MDFE 1.000 1.833
BT-COM 1.000 1.830
Helix and line phantomRoot Mean Square Error (RMSE) of accuracy. Lower is better.
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Artery centerline stabilityA) MDFE B) MDFE C) COM
D) COM E) BT-COM F) BT-COMArrows show errors in ICA siphon loop
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Artery centerline stability
COM stability compares well with inherently stable BT algorithms (8 subjects).
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Kissing vessels (ICA)
COM cost cross sectionMDFE cost
cross section
Segmentation MDFE cost
COM cost, completes loop
Binary thinned
Kiss Kiss
Kiss
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Stability of arterial centerlines
Algorithm
ICA siphons accurate
Portion ICA siphons correct
Both ICA correct in image
Mean number of trees
Standard deviation of trees
Mean stability
Standard deviation stability
MDFE6/16 0.375 1/8 38.875 14.672 0.677 0.076
COM16/16 1.000 8/8 35.125 13.314 0.877 0.042
BT-COM 10/16 0.625 4/8 37.500 13.617 0.883 0.068
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Paper 2
VALIDATION OF AN ARTERIAL TORTUOSITY MEASURE WITH APPLICATION TO
HYPERTENSION COLLECTION OF CLINICAL HYPERTENSIVE PATIENTS
Karl T. Diedrich, John A. Roberts, Richard H. Schmidt, Chang-Ki Kang, Zang-Hee Cho, and Dennis L. ParkerAccepted BMC Bioinformatics 2011 supplement 8
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COM MDFE DFE-COM
Lopsided phantom accuracy
Algorithm Number of trees Stability RMSE of Accuracy
COM 6 0.918 0.879
MDFE 6 0.819 0.417
DFE-COM 6 0.905 0.413
Lopsided phantom challenges COM
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Algorithm
ICA siphons accurate
Portion ICA siphons correct
Both ICA correct in image
Portion correct images
Mean number of trees
Standard deviation of trees
Mean stability
Standard deviation stability
COM15/16 0.938 7/8 0.875 37.000 12.352 0.872 0.0459
MDFE 7/16 0.438 1/8 0.125 39.875 13.228 0.673 0.0732DFE-COM 15/16 0.938 7/8 0.875 38.625 11.439 0.825 0.0434
DFE-COM ICA siphon
DFE-COM ICA siphon centerline
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Visual versus quantitative ranking
• DFM to mean human 0.72 Spearmen rank correlation coefficient
• Between humans 0.88±0.048
• 25 arteries• 5 observers
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Hypertension in microvessels
Lenticulostriate arteries (LSA) in hypertensives (HTN) increased tortuosity, less number than normotensives (NOR) (7 T Siemens imager) Data from C. Kang et al., “Hypertension correlates with lenticulostriate arteries visualized by 7T magnetic resonance angiography,” Hypertension, vol. 54, no. 5, pp. 1050-1056, Nov. 2009.
HTN NOR
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Resolution effect on tortuosity
Same subjects at different resolutions by acquisition and interpolation
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Hypertension and tortuosityArtery P-value
Left ACA 0.00377
Right ACA 0.0593
L to R ACA 0.0165
Left ICA 0.0215
Right ICA 0.142
Left LSAs 0.00161
Right LSAs 0.000520
Left LSAs 0.00977
Right LSAs 0.000800
Left LSA 1 0.0238
Right LSA 1 0.00905
Left LSA 1 0.0880
Right LSA 1 0.0786• HTN N = 18±3.0• NEG N = 18±3.8• 1-sided Wilcoxon signed rank test
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Negative controls
North Carolina data from: E. Bullitt et al., “The effects of healthy aging on intracerebral blood vessels visualized by magnetic resonance angiography,” Neurobiology of Aging, vol. 31, no. 2, pp. 290-300, Feb. 2010.
• Korean negative control consistently lower • Utah hospital same as North Carolina negative control
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Utah hypertension
None significant at α = 0.05Utah hypertensives on anti-hypertensive medication
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Paper 3
MEDICAL RECORD AND IMAGING EVALUATION TO IDENTIFY ARTERIAL
TORTUOSITY PHENOTYPE IN POPULATIONS AT RISK FOR
INTRACRANIAL ANEURYSMS
Karl T. Diedrich, MS, John A. Roberts, PhD, Richard H. Schmidt, MD, PhD, Lisa A. Cannon Albright, PhD, Anji T. Yetman, MD
and Dennis L. Parker, PhDAccepted AMIA 2011 Proceedings
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Tortuosity curvesAneurysm, Marfan/Loeys-Dietz syndrome
Aneurysm
Aneurysm
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Aneurysms and tortuosityArtery P-valueLeft ACA 0.00054
Right ACA 0.079
L to R ACA 0.320
Basilar 0.157
Left ICA 0.097
Right ICA 0.078
Left VA 0.043
Right VA 0.431
• Aneurysm N = 53±10• Negative N = 36±5.9• 1-sided Wilcoxon
signed rank test
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Loeys-Dietz tortuosityArtery P-value
ACA left 0.474
ACA right
0.131
Basilar 0.00450
L-R ACA 0.0631
ICA left 0.322
ICA right 0.216
VA left 0.00043
VA right 0.0509• Loeys-Dietz N = 4.5±1.2• Negative N = 36±5.9• 1-sided Wilcoxon signed
rank test• Potentially distinguish
LDS from Marfan with tortuosity
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Tortuosity distribution
Marfan diagnosis: LDS can be misdiagnosed as Marfan
Arnold-Chiari malformation: occurs 1 in 1280, 13.3% of LDS patients [1]
Collection of negative controls and vascular diseases
Loeys-Dietz (LDS) mean = 1.9
[1] B. L. Loeys et al., “Aneurysm syndromes caused by mutations in the TGF-beta receptor,” The New England Journal of Medicine, vol. 355, no. 8, pp. 788-798, Aug. 2006.
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1. Signal processing • Applied image processing to anatomical
measurement2. Database design
• Applied database design to medical image analysis3. Decision making
• Aided diagnosing Loeys-Dietz syndrome4. Modeling and simulation
• Simulated artery shapes to challenge centerline algorithms
5. Optimizing interfaces between human and machine• Artery and centerline measurement and display• Centerline visualizations
Components of medical informatics
H. R. Warner, “Medical informatics: a real discipline?,” Journal of the American Medical Informatics Association: JAMIA, vol. 2, no. 4, pp. 207-214, Aug. 1995.
5/5
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Experiment conclusions
• Methods detected increased arterial tortuosity– Hypertensive sample– Loeys-Dietz syndrome sample
• Increased tortuosity could distinguish Loeys-Dietz from related Marfan
• Correlated Loeys-Dietz syndrome TGFBR2 genotype with tortuosity phenotype
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System conclusions
• Flexible analysis system – Change groups in comparisons– Change and modify tortuosity algorithms– Reanalyze with new data
• Secondary use of existing images– Enabled by interpolation of images– Enables quick less expensive testing of hypotheses– Use to decide on best prospective studies
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Acknowledgements
• Committee: John Roberts, Richard Schmidt, Lisa Canon-Albright, Paul Clayton, Dennis Parker
• Co-authors: John Roberts, Richard Schmidt, Lisa Canon-Albright, Dennis Parker, Chang-Ki Kang, Zang-Hee Cho, Anji T. Yetman
• This work was support by NLM Grants: T15LM007124, and 1R01 HL48223, and the Ben B. and Iris M. Margolis Foundation.
• Many thanks to the students and staff at Utah Center for Advanced Imaging Research (UCAIR)
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Acknowledgements
• Neuroscience Research Institute (NRI), Gachon University of Medicine and Science in Incheon, South Korea
• Department of Pediatrics, Division Of Cardiology, Primary Children's Medical Center
• Department of Radiology, University of Utah• My Family: Mi-Young, Han and Leo