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

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

2. Compare vascular disease to negatives
No vascular disease
Vascular Disease
High risk aneurysm
relative (10% risk)
Aneurysm Normal aneurysm risk (5%)
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. ‹#›

3. Centerlines with bifurcation guides
Green dots at centerline Anterior Cerebral artery
bifurcations guide selection (ACA) centerline selected
of end points
Cross section Projection
‹#›

4. Tortuosity measurement
Distance Factor Metric (DFM) =
Length(L)/distance between ends (d)
MCA-ACA
bifurcation
L
d
Internal carotid artery
End of slab
Repeated measurements, same patient ‹#›

5. Phantom tortuosity curves
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6. 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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7. MRI scanner
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8. Medical image segmentation
Z-Buffer segmentation [1] of
Time of Flight Magnetic arteries
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. ‹#›

9. 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.
‹#›

10. Region growing threshold
Lowering region growing in 26 directions threshold
0.20 histogram seed threshold 0.07 histogram seed threshold
Noise
Aneurysm
0.20 histogram 0.07 histogram
threshold slice threshold slice
3T
‹#›

11. Hole Fill
• Bubble filling uses No filling Bubble filling
connected
components to fill
bubbles completely
enclosed bubbles in
aneurysm
• Voxel filing fills in
individual voxels
with artery
neighbors in Voxel filling Bubble + voxel filling
(variable) 24 of 26
directions within 8
voxels
• Bubble fill -> 3 voxel
fills -> bubble fill
1.5 T scanner, region growing >= 0.20 ‹#›

12. Paper 1
COMPARING PERFORMANCE OF CENTERLINE
ALGORITHMS FOR QUANTITATIVE
ASSESSMENT OF BRAIN VASCULAR ANATOMY
Karl T. Diedrich, John A. Roberts, Richard H. Schmidt
and Dennis L. Parker
‹#›

13. Least cost path centerline
Cost functions
Goal node Cross section
Least cost paths back to
goal node voxel
Backtrace from distal
ends to goal and remove
short paths ‹#›

14. Centerline
Path costs
Goal node
Removed short path
This path made first
L. Zhang et al., “Automatic detection of three-dimensional
vascular tree centerlines and bifurcations in high-resolution
Branch meets previous line magnetic resonance angiography,” Investigative Radiology,
vol. 40, no. 10, pp. 661-71, Oct. 2005.
‹#›

15. 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
Cross sections
Center voxel has same DFE in Z
DFE MDFE
Higher intensity in image is higher value ‹#›

16. Inverse cost function
Cost(Vi) = A * (1 - MDFE(Vi)/max_MDFE(Vi) )b +1
Inverts to make lower cost internal
Lower intensity
lower cost
Inversion
cost
function
MDFE Cost ‹#›

17. Modified Distance From Edge (MDFE)
MDFE cross section ‹#›

18. Center of mass movement
Segmentation
Mean x, y, z position of each voxel, Vi,
and up to 26 neighbors; Repeat.
Segmentation
collapsing to center of
mass (COM)
Accumulate the distance moved
‹#›

19. Center of mass cost
COM cost is the total distance move. Exterior voxels move farther
to COM; higher cost ‹#›

20. 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]. ‹#›

21. Multiple centerlines stability test
Second
round goal
nodes
COM
First goal node
‹#›

22. Phantom stability & accuracy
A-B) MDFE C-D) COM
Instability,
brighter
centerline
Green known centerline. E-F) BT-MDFE G-H) BT-COM
Red calculated centerline.
Yellow is overlap.
Stability Accuracy
‹#›

23. Helix and line phantom
Root Mean Square Error (RMSE) of accuracy. Lower is better.
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
‹#›

24. Artery centerline stability
A) MDFE B) MDFE C) COM
D) COM E) BT-COM F) BT-COM
Arrows show errors in ICA siphon loop ‹#›

25. Artery centerline stability
COM stability compares well with inherently stable BT
algorithms (8 subjects).
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26. Kissing vessels (ICA)
COM cost
MDFE cost Kiss Kiss
cross section
cross section
Kiss
Segmentation MDFE cost
COM cost, Binary thinned
completes
loop
‹#›

27. ‹#›
Standard deviation
stability
0.076
0.042
0.068
Mean stability
Stability of arterial centerlines
0.677
0.877
0.883
Standard deviation
of trees
38.875 14.672
35.125 13.314
37.500 13.617
Mean number of
trees
Both ICA correct in
image
1/8
8/8
4/8
Portion ICA
siphons correct
0.375
1.000
0.625
ICA siphons
accurate
16/16
10/16
6/16
Algorithm
MDFE
COM
COM
BT-

28. 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. Parker
Accepted BMC Bioinformatics 2011
supplement 8
‹#›

29. Lopsided phantom accuracy
Lopsided
phantom
challenges COM
COM MDFE DFE-COM
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
‹#›

30. DFE-COM ICA siphon
Both ICA correct
siphons correct
Portion correct
Mean stability
Mean number
deviation of
ICA siphons
Portion ICA
Algorithm
deviation
Standard
Standard
accurate
in image
stability
of trees
images
trees
COM
15/16 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 0.0732
DFE-
COM 15/16 0.938 7/8 0.875 38.625 11.439 0.825 0.0434
DFE-COM ICA siphon centerline
‹#›

31. Visual versus quantitative ranking
• DFM to mean
human 0.72
Spearmen rank
correlation
coefficient
• Between
humans
0.88±0.048
• 25 arteries
• 5 observers
‹#›

32. Hypertension in microvessels
HTN NOR
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. ‹#›

33. Resolution effect on tortuosity
Same subjects at different resolutions by acquisition and interpolation
‹#›

34. Hypertension and tortuosity
Artery 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 ‹#›

35. Negative controls
• Korean negative control consistently lower
• Utah hospital same as North Carolina negative control
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.
‹#›

36. Utah hypertension
None significant at α = 0.05
Utah hypertensives on anti-hypertensive medication‹#›

37. 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, PhD
Accepted AMIA 2011 Proceedings
‹#›

38. Tortuosity curves
Aneurysm, Marfan/Loeys-Dietz syndrome
Aneurysm
Aneurysm
‹#›

39. Aneurysms and tortuosity
Artery P-value
Left 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
‹#›

40. Loeys-Dietz tortuosity
Artery 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 ‹#›

41. Tortuosity distribution
Arnold-Chiari malformation: occurs 1 in 1280,
13.3% of LDS patients [1]
Loeys-Dietz Marfan diagnosis: LDS
(LDS) can be misdiagnosed
mean = 1.9 as Marfan
Collection of negative
controls and vascular
diseases
[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. ‹#›

42. Components of medical informatics
1. Signal processing
• Applied image processing to anatomical
measurement
5/5
2. Database design
• Applied database design to medical image analysis
3. Decision making
• Aided diagnosing Loeys-Dietz syndrome
4. 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
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.
‹#›

43. 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
‹#›

44. 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
‹#›

45. 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)
‹#›

46. 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
‹#›