Maximum Principal AAA Wall Stress Is Proportional to Wall Thickness

  • K. MillerEmail author
  • G. R. Joldes
  • J. Qian
  • A. P. Patel
  • M. S. Jung
  • A. C. R. Tavner
  • A. Wittek
Conference paper


Abdominal aortic aneurysm (AAA) is a permanent and irreversible dilation of the lower region of the aorta. It is an asymptomatic condition which if left untreated can expand to the point of rupture. Rupture of an artery will occur when the local wall stress exceeds the local wall strength. Therefore, estimation of a patient’s AAA wall stress non-invasively, quickly, and reliably is desirable. One solution to this problem is to use recently-published methods to compute AAA wall stress, using geometry from CT scans, and median arterial pressure as the load. Our method is embedded in the software platform BioPARR—Biomechanics based Prediction of Aneurysm Rupture Risk, freely available from Experience with over 50 patient-specific stress analyses, as well as common sense, suggests that the AAA wall stress is critically dependent on the local AAA wall thickness. This thickness is currently very difficult to measure in the clinical environment. Therefore, we conducted a simulation study to elucidate the relationship between the wall thickness and the maximum principal stress. The results of the analysis of three cases presented here unequivocally demonstrate that this relationship is approximately linear, bringing us closer to being able to compute predictive stress envelopes for every patient.


Abdominal aortic aneurysm Patient-specific modelling Finite element method Stress Wall Thickness 



The financial support of the National Health and Medical Research Council (Grant No. APP1063986) is gratefully acknowledged. We wish to acknowledge the Raine Medical Research Foundation for funding G. R. Joldes through a Raine Priming Grant, and the Department of Health, Western Australia, for funding G. R. Joldes through a Merit Award. The AAA data has been obtained from the MA3RS study [22].


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • K. Miller
    • 1
    Email author
  • G. R. Joldes
    • 2
    • 3
  • J. Qian
    • 1
  • A. P. Patel
    • 1
  • M. S. Jung
    • 1
  • A. C. R. Tavner
    • 1
  • A. Wittek
    • 1
  1. 1.Intelligent Systems for Medicine LaboratoryThe University of Western Australia, CrawleyPerthAustralia
  2. 2.School of Engineering and Information TechnologyMurdoch UniversityMurdochAustralia
  3. 3.Intelligent Systems for Medicine Laboratory, School of Mechanical and Chemical EngineeringThe University of Western Australia, CrawleyPerthAustralia

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