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Karl Diedrich
ARTERIAL TORTUOSITY MEASUREMENT SYSTEM FOR EXAMINING CORRELATIONS WITH VASCULAR DISEASE
Compare vascular disease to negatives
Vascular DiseaseNo vascular disease
AneurysmHigh 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%)
Centerlines with bifurcation guides
Green dots at centerline bifurcations guide selection of end pointsAnterior Cerebral artery (ACA) centerline selected
Cross sectionProjectionFirst make a centerline representing the artery. Simpler to make measurements on. Find end-points to measure from.
Tortuosity measurement
Internal carotid arteryMCA-ACAbifurcation
LdEnd of slab
Distance Factor Metric (DFM) = Length(L)/distance between ends (d)
Repeated measurements, same patient
Slab ends at variable point. Tortuosity measurement can be taken at peak or end of curves.
Phantom tortuosity curves
Higher peaks for more tightly wound coils. Oscillating shapes create oscillating curve.
Imaging modalitiesMRA shows only arteriesCTA shows arteries and veinsUsing simpler MRA images. Arteries are more significant to vascular disease than veins.
MRI scannerRadio frequency coils generate signal. Gradient coils encode spatial position.
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 arteriesSegmentation separates flowing arterial blood from stationary background tissues.
MIP Z-buffer segmentation
Intensity is position in image slice stack of maximum pixel intensity; dark is closer, brighter is fartherContiguous 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.
Cast rays through 3D data and display position of brightest point on each ray. Arterial blood is smooth in the image. MIP-Z smoothness defines a set of seed points; not full 3D artery segmentation.
2-D seed image
Original intensity values for smooth clusters over the thresholdUsed as seeds to grow 3-D image from
Seed histogram threshold
Histogram of 2-D seed20% of histogram from the left is used to find intensity threshold for 3-D region growing
Intensity value
Count
20% below135
3-D Region Growing
Check if pixels neighboring 26 voxels are above seed histogram threshold and add non-maximal 3-D pixels
Region growing threshold
0.20 histogram seed threshold0.07 histogram seed threshold0.20 histogram threshold slice0.07 histogram threshold sliceLowering region growing in 26 directions threshold
3 T
NoiseAneurysm
Slow moving or recirculating blood in aneurysms have low signal; appear as background.
Hole Fill
No filling
Bubble filling
Voxel filling
Bubble + voxel fillingBubble 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 >= 0.20Hole filling especially needed in aneurysms. Aneurysm is a dilation 1.5 X vessel diameter. Holes touching outside arent filled in by connected component bubble filling.
Comparing Performance of Centerline Algorithms for Quantitative Assessment of Brain Vascular AnatomyPaper 1Karl T. Diedrich, John A. Roberts, Richard H. Schmidt and Dennis L. ParkerCompare centerline algorithms used for anatomy assessment.
Least cost path centerline
Least cost paths back to goal node voxel
Goal node
Cost functionsBacktrace from distal ends to goal and remove short paths
Cross section
How we make a centerline. Cost function applied to segmentation has to be cheap in middle and expensive outside. Least cost centerline goes to middle. Working from the goal node assign the least cost back to the goal node from every voxel in the segmentation. Next slide describes removing short paths.
Centerline
Path costsGoal node
Branch meets previous line
Removed short path
This path made first
L. 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.
Distance From Edge (DFE)
Pythagorean theorem d2 = x2 + y2 + z2
xydDiagonal distances are longer than straight
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
DFEMDFECross sectionsHigher intensity in image is higher valueCenter voxel has same DFE in Z
Optional cost function. MDFE higher in middle; lower on outside. Needs reversing.
Inverse cost function
Cost(Vi) = A * (1 - MDFE(Vi)/max_MDFE(Vi) )b +1Inverts to make lower cost internal
MDFECostLower intensity lower cost
Inversion cost function
Modified Distance From Edge (MDFE)MDFE cross sectionCenterline will go to low cost middle.
Center of mass movement
SegmentationMean x, y, z position of each voxel, Vi, and up to 26 neighbors; Repeat.
Accumulate the distance movedSegmentation collapsing to center of mass (COM)
Center of mass cost
COM cost is the total distance move. Exterior voxels move farther to COM; higher costBlack area in middle actually has a gradient of values.
Binary thinned arteryBinary 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].Dim short branches were pruned by shortest paths centerline algorithm.
COM
Multiple centerlines stability testFirst goal node
Second round goal nodes
Compare algorithm stability starting from different goal nodes. Phantom generated starting with lines of dots and fill in around dots. Original dots used as true centerline.
Green known centerline. Red calculated centerline. Yellow is overlap.
Phantom stability & accuracyE-F) BT-MDFEG-H) BT-COMA-B) MDFEC-D) COMStability Accuracy
Instability, brighter centerline
Green known centerline. Red is calculated centerline missing green. Yellow is overlap between known and calculated. Brighter stability plot; all centerlines not taking the same path. Display scales stability intensity.
AlgorithmStabilityRMSE of Accuracy
MDFE0.8800.240
COM0.9800.610
BT-MDFE1.0001.833
BT-COM1.0001.830
Helix and line phantomRoot Mean Square Error (RMSE) of accuracy. Lower is better.BT-DFE and BT-COM are BT eroded data input into other algorithm. The stability measure for an image was the percentage of centerline voxels in the accumulated image called centerline for all of the centerline roots. Stability is fraction of all points that are the same from all starting points.
Artery centerline stabilityA) MDFEB) MDFE C) COMD) COM E) BT-COM F) BT-COMArrows show errors in ICA siphon loopOnly COM doesnt have errors in ICA siphon loop.
Artery centerline stabilityCOM stability compares well with inherently stable BT algorithms (8 subjects).
Kissing vessels (ICA)
COM cost cross sectionMDFE cost cross section
SegmentationMDFE costCOM cost, completes loopBinary thinnedKissKissKiss
Sometime the MDFE is correct but not from all goal nodes.
Stability of arterial centerlinesAlgorithmICA siphons accuratePortion ICA siphons correctBoth ICA correct in imageMean number of treesStandard deviation of treesMean stabilityStandard deviation stability
MDFE6/160.3751/838.87514.6720.6770.076
COM16/161.0008/835.12513.3140.8770.042
BT-COM10/160.6254/837.50013.6170.8830.068
BT eroded data so few alternatives exist. BT is inherently stable.
Paper 2K. T. Diedrich, J. A. Roberts, R. H. Schmidt, C.-K. Kang, Z.-H. Cho, and D. L. Parker, Validation of an arterial tortuosity measure with application to hypertension collection of clinical hypertensive patients, BMC Bioinformatics, vol. 12 Suppl 10, p. S15, 2011.
Apply centerline hypertensive population
COM MDFE DFE-COMLopsided phantom accuracyAlgorithmNumber of treesStabilityRMSE of Accuracy
COM60.9180.879
MDFE60.8190.417
DFE-COM60.9050.413
Lopsided phantom challenges COMMade phantom to challenge COM algorithm. Weighted COM with DFE to make voxels toward middle have more weight in centerline calculation. COM centerline pulled to one side.
AlgorithmICA siphons accuratePortion ICA siphons correctBoth ICA correct in imagePortion correct imagesMean number of treesStandard deviation of treesMean stabilityStandard deviation stability
COM15/160.9387/80.87537.00012.3520.8720.0459
MDFE7/160.4381/80.12539.87513.2280.6730.0732
DFE-COM15/160.9387/80.87538.62511.4390.8250.0434
DFE-COM ICA siphon
Visual versus quantitative rankingDFM to mean human 0.72 Spearmen rank correlation coefficient
Between humans 0.880.048
25 arteries
5 observers
Humans are more similar to each other than to computer. Repeated experiment and got lower correlations between neurosurgeons.
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.
HTNNOR
Hypertensives have less microvessels.
Resolution effect on tortuosity
Same subjects at different resolutions by acquisition and interpolationImages not all at same resolution. Double resolution increases tortuosity about 5%. Closer resolutions more similar tortuosity scores. 0.23x0.23x0.36
Hypertension and tortuosity
ArteryP-value
Left ACA0.00377
Right ACA0.0593
L to R ACA0.0165
Left ICA0.0215
Right ICA0.142
Left LSAs0.00161
Right LSAs0.000520
Left LSAs0.00977
Right LSAs0.000800
Left LSA 10.0238
Right LSA 10.00905
Left LSA 10.0880
Right LSA 10.0786
HTN N = 183.0
NEG N = 183.8
1-sided Wilcoxon signed rank test
DFM curve was good enough to show statistical significant difference, but not clinically useful due to overlap. Hypertension can be used as a training set testing tortuosity measurements to increase separation between groups to find clinically significant measure. Phase frequency artifact. Pulsatile flow. X and Y position are recorded at different times.
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
Repeat experiment with Utah population. Utah and North Carolina negatives similar. Shows that Utah hospital control of patients with headaches or head injuries are a valid negative control. Difference not explained by sex or age. Ethnicity is different. Utah and NC are both mostly white European populations. Use specific negative controls for each test population.
Utah hypertensionNone significant at = 0.05Utah hypertensives on anti-hypertensive medicationOnly compared within Utah population. Utah hypertensive population on hypertensive medication.
Paper 3K. T. Diedrich, J. A. Roberts, R. H. Schmidt, L. A. C. Albright, A. T. Yetman, and D. L. Parker, Medical record and imaging evaluation to identify arterial tortuosity phenotype in populations at risk for intracranial aneurysms, AMIA Annu Symp Proc, vol. 2011, pp. 295304, 2011.
Tortuosity curves
Aneurysm, Marfan/Loeys-Dietz syndromeAneurysm
AneurysmHighest, median and low tortuosity subjects all have intracranial aneurysms. Marfan syndome can be misdiagnosis of Loeys-Dietz syndrome.
Aneurysms and tortuosity
ArteryP-value
Left ACA0.00054
Right ACA0.079
L to R ACA0.320
Basilar0.157
Left ICA0.097
Right ICA0.078
Left VA0.043
Right VA0.431
Aneurysm N = 5310
Negative N = 365.9
1-sided Wilcoxon signed rank test
Compared Aneurysms, high-risk aneurysms, high-risk no aneurysms versus Utah negative control.
Loeys-Dietz tortuosity
ArteryP-value
ACA left0.474
ACA right0.131
Basilar0.00450
L-R ACA0.0631
ICA left0.322
ICA right0.216
VA left0.00043
VA right0.0509
Loeys-Dietz N = 4.51.2
Negative N = 365.9
1-sided Wilcoxon signed rank test
Potentially distinguish LDS from Marfan with tortuosity
Tortuosity distribution
Marfan diagnosis: LDS can be misdiagnosed as MarfanArnold-Chiari malformation: occurs 1 in 1280, 13.3% of LDS patients [1]
Collection of negative controls and vascular diseasesLoeys-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.Database and plotting interface allow distribution viewing.
Arnold-Chiari malformation: structural defects in the cerebellum, the part of the brain that controls balance
Combination of tortuosity and medical record screening for Marfan, Arnold-Chiari malformation can identify LDS
plotDFM(pwd=kpwd, conType='RODBC', arteryIds=c(5), cmdline=TRUE, legendx=.5, legendy=.95, hist=TRUE)
Signal processing
Applied image processing to anatomical measurement
Database designApplied database design to medical image analysis
Decision makingAided diagnosing Loeys-Dietz syndrome
Modeling and simulationSimulated artery shapes to challenge centerline algorithms
Optimizing interfaces between human and machineArtery and centerline measurement and display
Centerline visualizations
Components of medical informaticsH. R. Warner, Medical informatics: a real discipline?, Journal of the American Medical Informatics Association: JAMIA, vol. 2, no. 4, pp. 207-214, Aug. 1995.5/5Biomedical informaticians always have to talk about what biomedical informatics is.
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
System conclusionsFlexible analysis system
Change groups in comparisons
Change and modify tortuosity algorithms
Reanalyze with new data
Secondary use of existing imagesEnabled by interpolation of images
Enables quick less expensive testing of hypotheses
Use to decide on best prospective studies
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)
Acknowledgments
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
Data suppliers.
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