Literature DB >> 26455809

A finite element updating approach for identification of the anisotropic hyperelastic properties of normal and diseased aortic walls from 4D ultrasound strain imaging.

Andreas Wittek1, Wojciech Derwich2, Konstantinos Karatolios3, Claus Peter Fritzen4, Sebastian Vogt3, Thomas Schmitz-Rixen2, Christopher Blase5.   

Abstract

Computational analysis of the biomechanics of the vascular system aims at a better understanding of its physiology and pathophysiology and eventually at diagnostic clinical use. Because of great inter-individual variations, such computational models have to be patient-specific with regard to geometry, material properties and applied loads and boundary conditions. Full-field measurements of heterogeneous displacement or strain fields can be used to improve the reliability of parameter identification based on a reduced number of observed load cases as is usually given in an in vivo setting. Time resolved 3D ultrasound combined with speckle tracking (4D US) is an imaging technique that provides full field information of heterogeneous aortic wall strain distributions in vivo. In a numerical verification experiment, we have shown the feasibility of identifying nonlinear and orthotropic constitutive behaviour based on the observation of just two load cases, even though the load free geometry is unknown, if heterogeneous strain fields are available. Only clinically available 4D US measurements of wall motion and diastolic and systolic blood pressure are required as input for the inverse FE updating approach. Application of the developed inverse approach to 4D US data sets of three aortic wall segments from volunteers of different age and pathology resulted in the reproducible identification of three distinct and (patho-) physiologically reasonable constitutive behaviours. The use of patient-individual material properties in biomechanical modelling of AAAs is a step towards more personalized rupture risk assessment.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  3D ultrasound; AAA; Constitutive parameter identification; Full field strain imaging; In vivo

Mesh:

Year:  2015        PMID: 26455809     DOI: 10.1016/j.jmbbm.2015.09.022

Source DB:  PubMed          Journal:  J Mech Behav Biomed Mater        ISSN: 1878-0180


  11 in total

1.  A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis.

Authors:  Liang Liang; Minliang Liu; Caitlin Martin; Wei Sun
Journal:  J R Soc Interface       Date:  2018-01       Impact factor: 4.118

2.  A new inverse method for estimation of in vivo mechanical properties of the aortic wall.

Authors:  Minliang Liu; Liang Liang; Wei Sun
Journal:  J Mech Behav Biomed Mater       Date:  2017-05-02

3.  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

4.  A deep learning approach to estimate chemically-treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images.

Authors:  Liang Liang; Minliang Liu; Wei Sun
Journal:  Acta Biomater       Date:  2017-09-20       Impact factor: 8.947

5.  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

6.  Estimation of in vivo mechanical properties of the aortic wall: A multi-resolution direct search approach.

Authors:  Minliang Liu; Liang Liang; Wei Sun
Journal:  J Mech Behav Biomed Mater       Date:  2017-10-20

7.  A machine learning model to estimate myocardial stiffness from EDPVR.

Authors:  Hamed Babaei; Emilio A Mendiola; Sunder Neelakantan; Qian Xiang; Alexander Vang; Richard A F Dixon; Dipan J Shah; Peter Vanderslice; Gaurav Choudhary; Reza Avazmohammadi
Journal:  Sci Rep       Date:  2022-03-31       Impact factor: 4.379

8.  Large-field-of-view optical elastography using digital image correlation for biological soft tissue investigation.

Authors:  Daniel Claus; Marijo Mlikota; Jonathan Geibel; Thomas Reichenbach; Giancarlo Pedrini; Johannes Mischinger; Siegfried Schmauder; Wolfgang Osten
Journal:  J Med Imaging (Bellingham)       Date:  2017-03-16

9.  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

10.  In vivo parameter identification in arteries considering multiple levels of smooth muscle activity.

Authors:  Jan-Lucas Gade; Carl-Johan Thore; Björn Sonesson; Jonas Stålhand
Journal:  Biomech Model Mechanobiol       Date:  2021-05-02
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