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Identification of rupture locations in patient-specific abdominal aortic aneurysms using experimental and computational techniques

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journal contribution
posted on 2022-10-06, 08:23 authored by Barry J. Doyle, Aidan J. Cloonan, Michael WalshMichael Walsh, David A. Vorp, Timothy M. McGloughlin
In the event of abdominal aortic aneurysm (AAA) rupture, the outcome is often death. This paper aims to experimentally identify the rupture locations of in vitro AAA models and validate these rupture sites using finite element analysis (FEA). Silicone rubber AAA models were manufactured using two different materials (Sylgard 160 and Sylgard 170, Dow Corning) and imaged using computed tomography (CT). Experimental models were inflated until rupture with high speed photography used to capture the site of rupture. 3D reconstructions from CT scans and subsequent FEA of these models enabled the wall stress and wall thickness to be determined for each of the geometries. Experimental models ruptured at regions of inflection, not at regions of maximum diameter. Rupture pressures (mean ± SD) for the Sylgard 160 and Sylgard 170 models were 650.6 ± 195.1 mmHg and 410.7 ± 159.9 mmHg, respectively. Computational models accurately predicted the locations of rupture. Peak wall stress for the Sylgard 160 and Sylgard 170 models was 2.15 ± 0.26 MPa at an internal pressure of 650 mmHg and 1.69 ± 0.38 MPa at an internal pressure of 410 mmHg, respectively. Mean wall thickness of all models was 2.19 ± 0.40 mm, with a mean wall thickness at the location of rupture of 1.85 ± 0.33 mm and 1.71 ± 0.29 mm for the Sylgard 160 and Sylgard 170 materials, respectively. Rupture occurred at the location of peak stress in 80% (16/20) of cases and at a high stress regions but not peak stress in 10% (2/20) of cases. 10% (2/20) of models had defects in the AAA which moved the rupture location away from regions of elevated stress. The results presented may further contribute to the understanding of AAA biomechanics and ultimately AAA rupture prediction.

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A new method for transforming data to normality with application to density estimation

National Research Foundation

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peer-reviewed

Other Funding information

IRCSET, US National Heart Lung and Blood Institute

Language

English

Also affiliated with

  • Bernal Institute
  • CABER - Centre for Applied Biomedical Engineering Research Design Factors

Department or School

  • School of Engineering

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