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Research Papers

The Relationship Between Surface Curvature and Abdominal Aortic Aneurysm Wall Stress

[+] Author and Article Information
Sergio Ruiz de Galarreta

Department of Mechanical Engineering,
Tecnun, University of Navarra,
Paseo Manuel de Lardizabal, 13,
San Sebastián 20018, Spain
e-mail: sruiz@tecnun.es

Aitor Cazón

Department of Mechanical Engineering,
Tecnun, University of Navarra,
Paseo Manuel de Lardizabal, 13,
San Sebastián 20018, Spain
e-mail: acazon@tecnun.es

Raúl Antón

Department of Mechanical Engineering,
Tecnun, University of Navarra,
Paseo Manuel de Lardizabal, 13,
San Sebastián 20018, Spain
e-mail: ranton@tecnun.es

Ender A. Finol

Department of Mechanical Engineering,
The University of Texas at San Antonio,
One UTSA Circle, EB 3.04.23,
San Antonio, TX 78249-0669
e-mail: ender.finol@utsa.edu

1Corresponding author.

Manuscript received July 26, 2016; final manuscript received April 27, 2017; published online June 16, 2017. Assoc. Editor: C. Alberto Figueroa.

J Biomech Eng 139(8), 081006 (Jun 16, 2017) (7 pages) Paper No: BIO-16-1316; doi: 10.1115/1.4036826 History: Received July 26, 2016; Revised April 27, 2017

The maximum diameter (MD) criterion is the most important factor when predicting risk of rupture of abdominal aortic aneurysms (AAAs). An elevated wall stress has also been linked to a high risk of aneurysm rupture, yet is an uncommon clinical practice to compute AAA wall stress. The purpose of this study is to assess whether other characteristics of the AAA geometry are statistically correlated with wall stress. Using in-house segmentation and meshing algorithms, 30 patient-specific AAA models were generated for finite element analysis (FEA). These models were subsequently used to estimate wall stress and maximum diameter and to evaluate the spatial distributions of wall thickness, cross-sectional diameter, mean curvature, and Gaussian curvature. Data analysis consisted of statistical correlations of the aforementioned geometry metrics with wall stress for the 30 AAA inner and outer wall surfaces. In addition, a linear regression analysis was performed with all the AAA wall surfaces to quantify the relationship of the geometric indices with wall stress. These analyses indicated that while all the geometry metrics have statistically significant correlations with wall stress, the local mean curvature (LMC) exhibits the highest average Pearson's correlation coefficient for both inner and outer wall surfaces. The linear regression analysis revealed coefficients of determination for the outer and inner wall surfaces of 0.712 and 0.516, respectively, with LMC having the largest effect on the linear regression equation with wall stress. This work underscores the importance of evaluating AAA mean wall curvature as a potential surrogate for wall stress.

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Figures

Grahic Jump Location
Fig. 1

Spatial distributions of first principal stress (in kPa) and local mean curvature (LMC, in mm−1) computed on the outer wall surface of three exemplary AAA geometries

Grahic Jump Location
Fig. 2

Graphical representation of the correlation analyses of wall stress with (a) LMC, (b) LGC, (c) LWT, and (d) LD for the outer wall surface nodes of AAA #9. The Pearson's r correlation coefficients were −0.817, −0.437, −0.236, and 0.191, respectively.

Grahic Jump Location
Fig. 3

Graphical representation of the correlation analyses of wall stress with (a) LMC, (b) LGC, (c) LWT, and (d) LD for the inner wall surface nodes of AAA #3. The Pearson's r correlation coefficients were 0.483, 0.182, −0.239, and 0.188, respectively.

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