Research Papers

Three-Dimensional Geometrical Characterization of Abdominal Aortic Aneurysms: Image-Based Wall Thickness Distribution

[+] Author and Article Information
Giampaolo Martufi

 Università degli Studi di Roma Tor Vergata, Via del Politecnico, 1 00133 Roma Italiamartufi@kth.se

Elena S. Di Martino

Department of Civil Engineering, and Centre for Bioengineering Research and Education, University of Calgary, 2500 University Drive Northwest, Calgary, AL, T2N 1N4, Canadaedimarti@ucalgary.ca

Cristina H. Amon

Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ON, M5S 3G8, Canadacristina.amon@utoronto.ca

Satish C. Muluk

Division of Vascular Surgery, Allegheny General Hospital, 320 East North Avenue, South Tower, 14th Floor, Pittsburgh, PA 15212-4772smuluk@wpahs.org

Ender A. Finol1

Department of Biomedical Engineering, and Department of Mechanical Engineering, Institute for Complex Engineered Systems, Carnegie Mellon University, Pittsburgh, PA 15213-3890finole@cmu.edu


Corresponding author.

J Biomech Eng 131(6), 061015 (May 12, 2009) (11 pages) doi:10.1115/1.3127256 History: Received September 10, 2008; Revised December 25, 2008; Published May 12, 2009

The clinical assessment of abdominal aortic aneurysm (AAA) rupture risk is based on the quantification of AAA size by measuring its maximum diameter from computed tomography (CT) images and estimating the expansion rate of the aneurysm sac over time. Recent findings have shown that geometrical shape and size, as well as local wall thickness may be related to this risk; thus, reliable noninvasive image-based methods to evaluate AAA geometry have a potential to become valuable clinical tools. Utilizing existing CT data, the three-dimensional geometry of nine unruptured human AAAs was reconstructed and characterized quantitatively. We propose and evaluate a series of 1D size, 2D shape, 3D size, 3D shape, and second-order curvature-based indices to quantify AAA geometry, as well as the geometry of a size-matched idealized fusiform aneurysm and a patient-specific normal abdominal aorta used as controls. The wall thickness estimation algorithm, validated in our previous work, is tested against discrete point measurements taken from a cadaver tissue model, yielding an average relative difference in AAA wall thickness of 7.8%. It is unlikely that any one of the proposed geometrical indices alone would be a reliable index of rupture risk or a threshold for elective repair. Rather, the complete geometry and a positive correlation of a set of indices should be considered to assess the potential for rupture. With this quantitative parameter assessment, future research can be directed toward statistical analyses correlating the numerical values of these parameters with the risk of aneurysm rupture or intervention (surgical or endovascular). While this work does not provide direct insight into the possible clinical use of the geometric parameters, we believe it provides the foundation necessary for future efforts in that direction.

Copyright © 2009 by American Society of Mechanical Engineers
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Figure 4

Gaussian and mean curvatures for a sphere and cylinder predicted numerically (by VESSEG )

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Figure 5

Patient-specific AAA models (U1,…, U9), idealized fusiform-shaped AAA model (F) and patient-specific normal abdominal aorta model used as control (C).

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Figure 6

Comparison of discrete wall thickness sites between VESSEG predictions and actual measurements from cadaver specimen AAA No. 1 reported by Raghavan (22)

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Figure 7

Patient-specific wall thickness distributions for (a) U1 and (b) U5

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Figure 8

AAA sac wall thickness (average with Y error bars) for model U9

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Figure 9

Minimum and maximum AAA sac wall thickness for model U9

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Figure 10

Distribution of wall thickness at Dmax cross section for model U9. The nonuniform circumferential spacing between points is approximately 5 deg

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Figure 3

Curvature refinement (Gaussian and mean) for model U1

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Figure 2

Schematic of the 1D geometrical indices: (a) diameters, lengths and heights in a segmented AAA model; and (b) location of dc and parameters required to calculate a cross-sectional diameter Ai

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Figure 1

3D image segmentation and model reconstruction: (a) active contour detection on image with false coloring, (b) mask generated from segmented image, and (c) resulting 3D aortic geometry (lumen shown in yellow and thrombus in cyan)



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