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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