Literature DB >> 27041786

Dynamic finite-strain modelling of the human left ventricle in health and disease using an immersed boundary-finite element method.

Hao Gao1, David Carrick2, Colin Berry2, Boyce E Griffith3, Xiaoyu Luo1.   

Abstract

Detailed models of the biomechanics of the heart are important both for developing improved interventions for patients with heart disease and also for patient risk stratification and treatment planning. For instance, stress distributions in the heart affect cardiac remodelling, but such distributions are not presently accessible in patients. Biomechanical models of the heart offer detailed three-dimensional deformation, stress and strain fields that can supplement conventional clinical data. In this work, we introduce dynamic computational models of the human left ventricle (LV) that are derived from clinical imaging data obtained from a healthy subject and from a patient with a myocardial infarction (MI). Both models incorporate a detailed invariant-based orthotropic description of the passive elasticity of the ventricular myocardium along with a detailed biophysical model of active tension generation in the ventricular muscle. These constitutive models are employed within a dynamic simulation framework that accounts for the inertia of the ventricular muscle and the blood that is based on an immersed boundary (IB) method with a finite element description of the structural mechanics. The geometry of the models is based on data obtained non-invasively by cardiac magnetic resonance (CMR). CMR imaging data are also used to estimate the parameters of the passive and active constitutive models, which are determined so that the simulated end-diastolic and end-systolic volumes agree with the corresponding volumes determined from the CMR imaging studies. Using these models, we simulate LV dynamics from enddiastole to end-systole. The results of our simulations are shown to be in good agreement with subject-specific CMR-derived strain measurements and also with earlier clinical studies on human LV strain distributions.

Entities:  

Keywords:  excitation–contraction coupling; finite element method; hyperelasticity; immersed boundary method; invariant-based constitutive model; left ventricle; magnetic resonance imaging; myocardial infarction

Year:  2014        PMID: 27041786      PMCID: PMC4816497          DOI: 10.1093/imamat/hxu029

Source DB:  PubMed          Journal:  IMA J Appl Math        ISSN: 0272-4960            Impact factor:   0.845


  50 in total

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Journal:  Congest Heart Fail       Date:  2007 Jul-Aug

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9.  Quasi-static image-based immersed boundary-finite element model of left ventricle under diastolic loading.

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10.  Patient-Specific Models of Cardiac Biomechanics.

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  18 in total

1.  Gaussian process emulation to accelerate parameter estimation in a mechanical model of the left ventricle: a critical step towards clinical end-user relevance.

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2.  An Immersed Interface Method for Discrete Surfaces.

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3.  On the Lagrangian-Eulerian Coupling in the Immersed Finite Element/Difference Method.

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Review 5.  Multiphysics and multiscale modelling, data-model fusion and integration of organ physiology in the clinic: ventricular cardiac mechanics.

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Journal:  Interface Focus       Date:  2016-04-06       Impact factor: 3.906

6.  Changes and classification in myocardial contractile function in the left ventricle following acute myocardial infarction.

Authors:  Hao Gao; Andrej Aderhold; Kenneth Mangion; Xiaoyu Luo; Dirk Husmeier; Colin Berry
Journal:  J R Soc Interface       Date:  2017-07       Impact factor: 4.118

7.  A mathematical model for active contraction in healthy and failing myocytes and left ventricles.

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8.  Study of cardiovascular function using a coupled left ventricle and systemic circulation model.

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9.  Parameter estimation in a Holzapfel-Ogden law for healthy myocardium.

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10.  Estimating prognosis in patients with acute myocardial infarction using personalized computational heart models.

Authors:  Hao Gao; Kenneth Mangion; David Carrick; Dirk Husmeier; Xiaoyu Luo; Colin Berry
Journal:  Sci Rep       Date:  2017-10-19       Impact factor: 4.379

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