Literature DB >> 29402733

Impact of isotropic constitutive descriptions on the predicted peak wall stress in abdominal aortic aneurysms.

V Man1, S Polzer2, T C Gasser3, T Novotny4, J Bursa2.   

Abstract

Biomechanics-based assessment of Abdominal Aortic Aneurysm (AAA) rupture risk has gained considerable scientific and clinical momentum. However, computation of peak wall stress (PWS) using state-of-the-art finite element models is time demanding. This study investigates which features of the constitutive description of AAA wall are decisive for achieving acceptable stress predictions in it. Influence of five different isotropic constitutive descriptions of AAA wall is tested; models reflect realistic non-linear, artificially stiff non-linear, or artificially stiff pseudo-linear constitutive descriptions of AAA wall. Influence of the AAA wall model is tested on idealized (n=4) and patient-specific (n=16) AAA geometries. Wall stress computations consider a (hypothetical) load-free configuration and include residual stresses homogenizing the stresses across the wall. Wall stress differences amongst the different descriptions were statistically analyzed. When the qualitatively similar non-linear response of the AAA wall with low initial stiffness and subsequent strain stiffening was taken into consideration, wall stress (and PWS) predictions did not change significantly. Keeping this non-linear feature when using an artificially stiff wall can save up to 30% of the computational time, without significant change in PWS. In contrast, a stiff pseudo-linear elastic model may underestimate the PWS and is not reliable for AAA wall stress computations.
Copyright © 2018 IPEM. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Abdominal aortic aneurysm; Non-linear material model; Wall stress

Mesh:

Year:  2018        PMID: 29402733     DOI: 10.1016/j.medengphy.2018.01.002

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  1 in total

1.  Geometric and biomechanical modeling aided by machine learning improves the prediction of growth and rupture of small abdominal aortic aneurysms.

Authors:  Moritz Lindquist Liljeqvist; Marko Bogdanovic; Antti Siika; T Christian Gasser; Rebecka Hultgren; Joy Roy
Journal:  Sci Rep       Date:  2021-09-10       Impact factor: 4.379

  1 in total

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