Literature DB >> 32090941

Insights into the passive mechanical behavior of left ventricular myocardium using a robust constitutive model based on full 3D kinematics.

David S Li1, Reza Avazmohammadi1, Samer S Merchant2, Tomonori Kawamura3, Edward W Hsu2, Joseph H Gorman3, Robert C Gorman3, Michael S Sacks4.   

Abstract

Myocardium possesses a hierarchical structure that results in complex three-dimensional (3D) mechanical behavior, forming a critical component of ventricular function in health and disease. A wide range of constitutive model forms have been proposed for myocardium since the first planar biaxial studies were performed by Demer and Yin (J. Physiol. 339 (1), 1983). While there have been extensive studies since, none have been based on full 3D kinematic data, nor have they utilized optimal experimental design to estimate constitutive parameters, which may limit their predictive capability. Herein we have applied our novel 3D numerical-experimental methodology (Avazmohammadi et al., Biomechanics Model. Mechanobiol. 2018) to explore the applicability of an orthotropic constitutive model for passive ventricular myocardium (Holzapfel and Ogden, Philos. Trans. R. Soc. Lond.: Math. Phys. Eng. Sci. 367, 2009) by integrating 3D optimal loading paths, spatially varying material structure, and inverse modeling techniques. Our findings indicated that the initial model form was not successful in reproducing all optimal loading paths, due to previously unreported coupling behaviors via shearing of myofibers and extracellular collagen fibers in the myocardium. This observation necessitated extension of the constitutive model by adding two additional terms based on the I8(C) pseudo-invariant in the fiber-normal and sheet-normal directions. The modified model accurately reproduced all optimal loading paths and exhibited improved predictive capabilities. These unique results suggest that more complete constitutive models are required to fully capture the full 3D biomechanical response of left ventricular myocardium. The present approach is thus crucial for improved understanding and performance in cardiac modeling in healthy, diseased, and treatment scenarios.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Constitutive modeling; Inverse modeling; Optimal experimental design

Mesh:

Year:  2019        PMID: 32090941      PMCID: PMC7045908          DOI: 10.1016/j.jmbbm.2019.103508

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


  37 in total

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Journal:  J Biomech       Date:  1993-11       Impact factor: 2.712

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Authors:  Myrianthi Hadjicharalambous; Radomir Chabiniok; Liya Asner; Eva Sammut; James Wong; Gerald Carr-White; Jack Lee; Reza Razavi; Nicolas Smith; David Nordsletten
Journal:  Biomech Model Mechanobiol       Date:  2014-12-16
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  7 in total

1.  Simulation of the 3D Hyperelastic Behavior of Ventricular Myocardium using a Finite-Element Based Neural-Network Approach.

Authors:  Wenbo Zhang; David S Li; Tan Bui-Thanh; Michael S Sacks
Journal:  Comput Methods Appl Mech Eng       Date:  2022-04-01       Impact factor: 6.756

Review 2.  Myocardial mesostructure and mesofunction.

Authors:  Alexander J Wilson; Gregory B Sands; Ian J LeGrice; Alistair A Young; Daniel B Ennis
Journal:  Am J Physiol Heart Circ Physiol       Date:  2022-06-03       Impact factor: 5.125

3.  How hydrogel inclusions modulate the local mechanical response in early and fully formed post-infarcted myocardium.

Authors:  David S Li; Reza Avazmohammadi; Christopher B Rodell; Edward W Hsu; Jason A Burdick; Joseph H Gorman; Robert C Gorman; Michael S Sacks
Journal:  Acta Biomater       Date:  2020-07-30       Impact factor: 8.947

4.  Right ventricular myocardial mechanics: Multi-modal deformation, microstructure, modeling, and comparison to the left ventricle.

Authors:  Sotirios Kakaletsis; William D Meador; Mrudang Mathur; Gabriella P Sugerman; Tomasz Jazwiec; Marcin Malinowski; Emma Lejeune; Tomasz A Timek; Manuel K Rausch
Journal:  Acta Biomater       Date:  2020-12-15       Impact factor: 8.947

5.  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

Review 6.  Computational models of ventricular mechanics and adaptation in response to right-ventricular pressure overload.

Authors:  Oscar O Odeigah; Daniela Valdez-Jasso; Samuel T Wall; Joakim Sundnes
Journal:  Front Physiol       Date:  2022-08-24       Impact factor: 4.755

7.  The impact of myocardial compressibility on organ-level simulations of the normal and infarcted heart.

Authors:  Hao Liu; João S Soares; John Walmsley; David S Li; Samarth Raut; Reza Avazmohammadi; Paul Iaizzo; Mark Palmer; Joseph H Gorman; Robert C Gorman; Michael S Sacks
Journal:  Sci Rep       Date:  2021-06-29       Impact factor: 4.379

  7 in total

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