Literature DB >> 23668998

In vivo determination of elastic properties of the human aorta based on 4D ultrasound data.

Andreas Wittek1, Konstantinos Karatolios, Peter Bihari, Thomas Schmitz-Rixen, Rainer Moosdorf, Sebastian Vogt, Christopher Blase.   

Abstract

Computational analysis of the biomechanics of the vascular system aims at a better understanding of its physiology and pathophysiology. To be of clinical use, however, these models and thus their predictions, have to be patient specific regarding geometry, boundary conditions and material. In this paper we present an approach to determine individual material properties of human aortae based on a new type of in vivo full field displacement data acquired by dimensional time resolved three dimensional ultrasound (4D-US) imaging. We developed a nested iterative Finite Element Updating method to solve two coupled inverse problems: The prestrains that are present in the imaged diastolic configuration of the aortic wall are determined. The solution of this problem is integrated in an iterative method to identify the nonlinear hyperelastic anisotropic material response of the aorta to physiologic deformation states. The method was applied to 4D-US data sets of the abdominal aorta of five healthy volunteers and verified by a numerical experiment. This non-invasive in vivo technique can be regarded as a first step to determine patient individual material properties of the human aorta.
© 2013 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  3-Dimensional ultrasound imaging; 4D-US; Abdominal aorta; Anisotropic hyperelastic; BC; BM; EM; FEU; Finite Element Updating method; Finite element updating method; In vivo; SEF; Vascular mechanics; benchmark model; boundary condition; estimation model; strain energy function; three dimensional time resolved ultrasound

Mesh:

Year:  2013        PMID: 23668998     DOI: 10.1016/j.jmbbm.2013.03.014

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


  21 in total

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2.  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

3.  A Meshfree Representation for Cardiac Medical Image Computing.

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Journal:  IEEE J Transl Eng Health Med       Date:  2018-01-18       Impact factor: 3.316

4.  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

5.  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

6.  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

7.  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

8.  Cyclic Strain and Hypertension Increase Osteopontin Expression in the Aorta.

Authors:  Christa Caesar; Alicia N Lyle; Giji Joseph; Daiana Weiss; Fadi M F Alameddine; Bernard Lassègue; Kathy K Griendling; W Robert Taylor
Journal:  Cell Mol Bioeng       Date:  2016-12-27       Impact factor: 2.321

9.  Immersed boundary-finite element model of fluid-structure interaction in the aortic root.

Authors:  Vittoria Flamini; Abe DeAnda; Boyce E Griffith
Journal:  Theor Comput Fluid Dyn       Date:  2015-12-19       Impact factor: 1.606

10.  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
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