Development of a computer system for measuring the twisting or tortuosity in arteries from medical MRI images for potential monitoring of vascular disease progression and treatment.

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)
Normal aneurysm risk (5%)
Aneurysm
suruda, D. Parker, J. MacDonald, and L.A. Cannon-Albright, “Confirmation of chromosome 7q11 locus for predisposition to intracranial aneurysm,” Hu
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3. Centerlines with bifurcation guides
enterline bifurcations guide Anterior Cerebral artery (ACA) centerline selec
selection of end points
Cross section Projection
3

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

5. Phantom tortuosity curves
5

6. Imaging modalities
MRA shows only arteries CTA shows arteries and veins
er MRA images. Arteries are more significant to vascular disease
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7. MRI scanner
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8. Medical image segmentation
Z-Buffer segmentation [1] of arteries
tic Resonance Angiography images highlight flowing arterial blood
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 Ima
8

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 J. S. Tsuruda, “Enhanced image detail using continuity in the MIP Z-buffer: applications to magnetic resonance angiography,” Journal of Magnetic Re
9

10. 2-D seed image
Original intensity
values for smooth
clusters over the
threshold
Used as seeds to
grow 3-D image
from
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11. Seed histogram threshold
Histogram of 2-D seed
20% of histogram from the left is used to find
intensity threshold for 3-D region growing
Count
20% below
135
Intensity value
11

12. 3-D Region Growing
Check if pixels
neighboring 26 voxels
are above seed
histogram threshold
and add non-maximal
3-D pixels
12

13. 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 threshold slice 0.07 histogram threshold sli
3T
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14. Hole Fill
No filling Bubble 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 Voxel filling Bubble + voxel filling
voxels
Bubble fill -> 3 voxel fills ->
bubble fill
1.5 T scanner, region growing >= 0.20 14

15. Paper 1
Centerline Algorithms for Quantitative Assessmen
Karl T. Diedrich, John A. Roberts, Richard H. Schmidt and Dennis L. Parker
15

16. 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 s
16

17. Centerline
Path costs
L. Zhang et al., “Automatic detection of three-dimensional vascular tree centerlines and bifurcations in high-resolution magnetic resonance
Goal node
Removed short path
This path made first
Branch meets previous line
17

18. Distance From Edge (DFE)
Pythagorean theorem d2 = x2 + y2 + z2
d
y
x
Diagonal distances are
longer than straight
18

19. M
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
Center voxel has same DFE in Z sections
DF MD
Higher intensity in image is higher value FE 19

20. 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
MD Cost 20

21. Modified Distance From Edge (MDFE)
MDFE cross section 21

22. Center of mass movement
Segmentation
Mean x, y, z position of each voxel, Vi, and up to 26 neighbors; R
Segmentation collapsing to center of mass
Accumulate the distance moved
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23. Center of mass cost
cost is the total distance move. Exterior voxels move farther to COM; highe
23

24. Binary thinned artery
des segmentation to single lines. Pass to centerline algorithm to
ht Journal - Implementation of a 3D thinning algorithm,” 12-Oct-2007. [Online]. Available: http://www.insight-journal.org/browse/publication/181. [A
24

25. Multiple centerlines stability test
Second round goal nod
COM
First goal node
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26. Phantom stability & accuracy
A-B) MDFE C-D) COM
ability, brighter centerline
Green known centerline. E-F) BT-MDFE G-H) BT-COM
Red calculated centerline.
Yellow is overlap. Stability Accuracy
26

27. Helix and line phantom
Root Mean Square Error (RMSE) of accuracy. Lower is better.
A lg orith m S ta b ility R MS E of
Ac c u ra c y
MD F E 0 .8 8 0 0 .2 4 0
C OM 0 .9 8 0 0 .6 1 0
B T-MD F E 1 .0 0 0 1 .8 3 3
B T-C O M 1 .0 0 0 1 .8 3 0
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28. Artery centerline stability
A) MDFE B) MDFE C) COM
D) COM E) BT-COM F) BT-COM
Arrows show errors in ICA siphon loop 28

29. Artery centerline stability
stability compares well with inherently stable BT algorithms (8 su
29

30. Kissing vessels (ICA)
DFE cost cross section Kiss COM cost cross section
Kiss
Segmentation Kiss
MDFE cost
M cost, completes loop Binary thinned
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31. Stability of arterial centerlines
A lg orit IC A Portion B oth Me a n S ta n d a rd Me a n S ta n d a rd
hm s ip h on s IC A IC A num ber d e v ia tion s ta b ili d e v ia tion
a c c u ra te s ip h on s c orre c t of tre e s of tre e s ty s ta b ility
c orre c t in
im a g e
MD F E 6 /1 6 0 .3 7 5 1 /8 3 8 .8 7 5 1 4 .6 7 2 0 .6 7 7 0 .0 7 6
C O M 1 6 /1 6 1 .0 0 0 8 /8 3 5 .1 2 5 1 3 .3 1 4 0 .8 7 7 0 .0 4 2
B T- 1 0 /1 6 0 .6 2 5 4 /8 3 7 .5 0 0 1 3 .6 1 7 0 .8 8 3 0 .0 6 8
C OM
31

32. Paper 2
of an arterial tortuosity measure with application to hypertensio
32

33. Lopsided phantom accuracy
d phantom challenges COM
COM MDFE DFE-COM
A lg orith m N u m b e r of tre e s S ta b ility R MS E of Ac c u ra c y
C OM 6 0 .9 1 8 0 .8 7 9
MD F E 6 0 .8 1 9 0 .4 1 7
D F E -C O M 6 0 .9 0 5 0 .4 1 3
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34. DFE-COM ICA siphon
A lg IC A Portio B oth Porti Me a n S ta n d a r Me a n S ta n d a
orit s ip h n IC A IC A on num d s ta b ili rd
hm on s s ip h o c orre c orre ber d e v ia ti ty d e v ia ti
accu ns c t in ct of on of on
ra te c orre im a g im a g tre e s tre e s s ta b ilit
ct e es y
C O M 1 5 /1 6 0 .9 3 8 7 /8 0 .8 7 5 3 7 .0 0 1 2 .3 5 2 0 .8 7 2 0 .0 4 5 9
0
MD F 7 /1 6 0 .4 3 8 1 /8 0 .1 2 5 3 9 .8 7 1 3 .2 2 8 0 .6 7 3 0 .0 7 3 2
E
5
D F E - 1 5 /1 6 0 .9 3 8 7 /8 0 .8 7 5 3 8 .6 2 1 1 .4 3 9 0 .8 2 5 0 .0 4 3 4
C OM
5
34

35. Visual versus quantitative ranking
DFM to mean human 0.72 Spearmen rank c
Between humans 0.88±0.048
25 arteries
5 observers
35

36. Hypertension in microvessels
HTN NOR
eries (LSA) in hypertensives (HTN) increased tortuosity, less number than normotensives (NOR) (7 T
g et al., “Hypertension correlates with lenticulostriate arteries visualized by 7T magnetic resonance angiography,” Hypertension, vol. 54, no. 5, pp. 10
36

37. Resolution effect on tortuosity
Same subjects at different resolutions by acquisition and interpolation
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38. Hypertension and tortuosity
A rte ry P-v a lu e
L e ft AC A 0 .0 0 3 7 7
R ig h t AC A 0 .0 5 9 3
L to R AC A 0 .0 1 6 5
L e ft IC A 0 .0 2 1 5
R ig h t IC A 0 .1 4 2
L e ft L S A s 0 .0 0 1 6 1
R ig h t L S A s 0 .0 0 0 5 2 0
L e ft L S A s 0 .0 0 9 7 7
R ig h t L S A s 0 .0 0 0 8 0 0
L e ft L S A 1 0 .0 2 3 8
R ig h t L S A 1 0 .0 0 9 0 5
L e ft L S A 1 0 .0 8 8 0
R ig h t L S A 1 0 .0 7 8 6
HTN N = 18±3.0
NEG N = 18±3.8
1-sided Wilcoxon signed rank test
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39. Negative controls
Korean negative control consistently lower
Utah hospital same as North Carolina negative control
ffects of healthy aging on intracerebral blood vessels visualized by magnetic resonance angiography,” Neurobiology
39

40. Utah hypertension
None significant at α = 0.05
Utah hypertensives on anti-hypertensive medication
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41. Paper 3
d D. L. Parker, “Medical record and imaging evaluation to
41

42. Tortuosity curves
Aneurysm, Marfan/Loeys-Dietz syndrome
Aneurysm
Aneurysm
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43. Aneurysms and tortuosity
A rte ry P-v a lu e
L e ft AC A 0 .0 0 0 5 4
R ig h t 0 .0 7 9
AC A
L to R 0 .3 2 0
AC A
B a s ila r 0 .1 5 7
L e ft IC A 0 .0 9 7
R ig h t 0 .0 7 8
IC A
L e ft VA 0 .0 4 3
R ig h t VA 0 .4 3 1
urysm N = 53±10
ative N = 36±5.9
ded Wilcoxon signed rank test
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44. Loeys-Dietz tortuosity
A rte ry P-v a lu e
AC A le ft 0 .4 7 4
AC A 0 .1 3 1
rig h t
B a s ila r 0 .0 0 4 5
0
L -R AC A 0 .0 6 3 1
IC A le ft 0 .3 2 2
IC A 0 .2 1 6
rig h t
V A le f t 0 .0 0 0 4
3
VA rig h t 0 .0 5 0 9
Dietz N = 4.5±1.2
ve N = 36±5.9
d Wilcoxon signed rank test
tially distinguish LDS from Marfan with tortuosity
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45. Tortuosity distribution
Arnold-Chiari malformation: occurs 1 in 1280,
13.3% of LDS patients [1]
Marfan diagnosis: LDS can be misdiagnosed as Marfan
Loeys-Dietz
(LDS)
mean = 1.9
Collection of negative controls and vascular
omes caused by mutations in the TGF-beta receptor,” The New England Journal of Medicine, vol. 355
45

46. Components of medical informatics
Signal processing
Applied image processing to anatomical measurement
Database design 5/5
Applied database design to medical image analysis
Decision making
Aided diagnosing Loeys-Dietz syndrome
Modeling and simulation
Simulated artery shapes to challenge centerline algorithms
Optimizing interfaces between human and machine
Artery and centerline measurement and display
Centerline visualizations
Medical informatics: a real discipline?,” Journal of the American Medical Informatics Association: JAMIA, vol. 2, no. 4, pp. 207-21
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47. 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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48. 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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49. Acknowledgments
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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50. Acknowledgments
nstitute (NRI), Gachon University of Medicine and Science
, Division Of Cardiology, Primary Children's Medical Cent
, University of Utah
n and Leo
50