arterial tortuosity measurement system

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Karl Diedrich ARTERIAL TORTUOSITY MEASUREMENT SYSTEM FOR EXAMINING CORRELATIONS WITH VASCULAR DISEASE

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Development of a systems for measuring arterial tortuosity from medical images for monitoring vascular disease.

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Page 1: Arterial tortuosity measurement system

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