Literature DB >> 24596311

A generalized prestressing algorithm for finite element simulations of preloaded geometries with application to the aorta.

Hannah Weisbecker1, David M Pierce, Gerhard A Holzapfel.   

Abstract

Finite element models reconstructed from medical imaging data, for example, computed tomography or MRI scans, generally represent geometries under in vivo load. Classical finite element approaches start from an unloaded reference configuration. We present a generalized prestressing algorithm based on a concept introduced by Gee et al. (Int. J. Num. Meth. Biomed. Eng. 26:52-72, 2012) in which an incremental update of the displacement field in the classical approach is replaced by an incremental update of the deformation gradient field. Our generalized algorithm can be implemented in existing finite element codes with relatively low implementation effort on the element level and is suitable for material models formulated in the current or initial configurations. Applicable to any finite element simulations started from preloaded geometries, we demonstrate the algorithm and its convergence properties on an academic example and on a segment of a thoracic aorta meshed from MRI data. Furthermore, we present an example to discuss the influence of neglecting prestresses in geometries obtained from medical images, a topic on which conflicting statements are found in the literature.
Copyright © 2014 John Wiley & Sons, Ltd.

Entities:  

Keywords:  aorta; finite element analysis; inverse design analysis; patient-specific modeling; prestress; prestretch

Mesh:

Year:  2014        PMID: 24596311     DOI: 10.1002/cnm.2632

Source DB:  PubMed          Journal:  Int J Numer Method Biomed Eng        ISSN: 2040-7939            Impact factor:   2.747


  8 in total

1.  On the computation of in vivo transmural mean stress of patient-specific aortic wall.

Authors:  Minliang Liu; Liang Liang; Haofei Liu; Ming Zhang; Caitlin Martin; Wei Sun
Journal:  Biomech Model Mechanobiol       Date:  2018-11-09

2.  Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach.

Authors:  Minliang Liu; Liang Liang; Wei Sun
Journal:  Comput Methods Appl Mech Eng       Date:  2018-12-28       Impact factor: 6.756

3.  A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm.

Authors:  Liang Liang; Minliang Liu; Caitlin Martin; John A Elefteriades; Wei Sun
Journal:  Biomech Model Mechanobiol       Date:  2017-04-06

Review 4.  Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases.

Authors:  Yong He; Hannah Northrup; Ha Le; Alfred K Cheung; Scott A Berceli; Yan Tin Shiu
Journal:  Front Bioeng Biotechnol       Date:  2022-04-27

5.  A nonlinear rotation-free shell formulation with prestressing for vascular biomechanics.

Authors:  Nitesh Nama; Miquel Aguirre; Jay D Humphrey; C Alberto Figueroa
Journal:  Sci Rep       Date:  2020-10-16       Impact factor: 4.379

Review 6.  Inverse problems in blood flow modeling: A review.

Authors:  David Nolte; Cristóbal Bertoglio
Journal:  Int J Numer Method Biomed Eng       Date:  2022-05-24       Impact factor: 2.648

7.  Identification of in vivo nonlinear anisotropic mechanical properties of ascending thoracic aortic aneurysm from patient-specific CT scans.

Authors:  Minliang Liu; Liang Liang; Fatiesa Sulejmani; Xiaoying Lou; Glen Iannucci; Edward Chen; Bradley Leshnower; Wei Sun
Journal:  Sci Rep       Date:  2019-09-10       Impact factor: 4.996

8.  Modeling intracranial aneurysm stability and growth: an integrative mechanobiological framework for clinical cases.

Authors:  Frederico S Teixeira; Esra Neufeld; Niels Kuster; Paul N Watton
Journal:  Biomech Model Mechanobiol       Date:  2020-06-12
  8 in total

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