A Microstructurally Based Multi-Scale Constitutive Model of Active Myocardial Mechanics

Krishnamurthy, Adarsh; Coppola, Benjamin A.; Tangney, Jared R.; Kerckhoffs, Roy; Omens, Jeffrey H.; McCulloch, Andrew D.

doi:10.1007/978-1-4899-7630-7_22

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…The scaling factor n can take values between 0 and one and scales the additional active stress acting in the sheet direction. This additional cross-fiber stress is motivated by the need to model the fiber dispersion ( Krishnamurthy et al, 2016 ). The fiber vector

, defined by the local predominant aggregated myocyte orientation, does not take the variation of myocyte orientations within the represented local region into account.…”

Section: Methodsmentioning

confidence: 99%

Personalization of biomechanical simulations of the left ventricle by in-vivo cardiac DTI data: Impact of fiber interpolation methods

et al. 2022

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Simulations of cardiac electrophysiology and mechanics have been reported to be sensitive to the microstructural anisotropy of the myocardium. Consequently, a personalized representation of cardiac microstructure is a crucial component of accurate, personalized cardiac biomechanical models. In-vivo cardiac Diffusion Tensor Imaging (cDTI) is a non-invasive magnetic resonance imaging technique capable of probing the heart’s microstructure. Being a rather novel technique, issues such as low resolution, signal-to noise ratio, and spatial coverage are currently limiting factors. We outline four interpolation techniques with varying degrees of data fidelity, different amounts of smoothing strength, and varying representation error to bridge the gap between the sparse in-vivo data and the model, requiring a 3D representation of microstructure across the myocardium. We provide a workflow to incorporate in-vivo myofiber orientation into a left ventricular model and demonstrate that personalized modelling based on fiber orientations from in-vivo cDTI data is feasible. The interpolation error is correlated with a trend in personalized parameters and simulated physiological parameters, strains, and ventricular twist. This trend in simulation results is consistent across material parameter settings and therefore corresponds to a bias introduced by the interpolation method. This study suggests that using a tensor interpolation approach to personalize microstructure with in-vivo cDTI data, reduces the fiber uncertainty and thereby the bias in the simulation results.

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, defined by the local predominant aggregated myocyte orientation, does not take the variation of myocyte orientations within the represented local region into account.…”

Section: Methodsmentioning

confidence: 99%

Personalization of biomechanical simulations of the left ventricle by in-vivo cardiac DTI data: Impact of fiber interpolation methods

et al. 2022

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“…The active stress, given in Equation (3c), defines the amount of contraction as well as the length dependent mechanisms (Kerckhoffs et al, 2003 ). Here, stresses were also applied both along fibres as well as across fibres based on known myofibre dispersion (Tangney et al, 2013 ; Krishnamurthy et al, 2016 ). Note that the active scalings, α lv , α rv , are dotted with the region identifier, ϕ, in order to selectively activate LV and RV chambers.…”

Section: Methodsmentioning

confidence: 99%

An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline

et al. 2021

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Parameterised patient-specific models of the heart enable quantitative analysis of cardiac function as well as estimation of regional stress and intrinsic tissue stiffness. However, the development of personalised models and subsequent simulations have often required lengthy manual setup, from image labelling through to generating the finite element model and assigning boundary conditions. Recently, rapid patient-specific finite element modelling has been made possible through the use of machine learning techniques. In this paper, utilising multiple neural networks for image labelling and detection of valve landmarks, together with streamlined data integration, a pipeline for generating patient-specific biventricular models is applied to clinically-acquired data from a diverse cohort of individuals, including hypertrophic and dilated cardiomyopathy patients and healthy volunteers. Valve motion from tracked landmarks as well as cavity volumes measured from labelled images are used to drive realistic motion and estimate passive tissue stiffness values. The neural networks are shown to accurately label cardiac regions and features for these diverse morphologies. Furthermore, differences in global intrinsic parameters, such as tissue anisotropy and normalised active tension, between groups illustrate respective underlying changes in tissue composition and/or structure as a result of pathology. This study shows the successful application of a generic pipeline for biventricular modelling, incorporating artificial intelligence solutions, within a diverse cohort.

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“…For instance, Lee et al (91) revisited the structural models by Horowitz et al (87) and Eriksson et al (92). Krishnamurthy et al (93) studied the effect of fiber dispersion on myocardium active behavior. Avazmohammadi et al (88) developed a structural model for the passive behavior of RVFW myocardium using histologically measured, 3D transmural orientation distributions for both myo- and collagen fibers.…”

Section: Biomechanics Of Passive Myocardiummentioning

confidence: 99%

“…Surprisingly, very few similar studies were conducted thereafter. Recently, however, Krishnamurthy et al (93) took an important step by developing a biophysical model that integrates cross-bridge mechanics with the passive behavior of myofibers to explain the interaction between the fiber and transverse directions in the active state. In summary, there remains a pressing need for 3D characterization of the mechanical behavior of contracting myocardium, as well as for the development of integrated structural models that can identify the role of myo- and collagen fibers and their interaction in the contractile function of myocardium.…”

Section: Electromechanical Modeling Of Myocardiummentioning

confidence: 99%

A Contemporary Look at Biomechanical Models of Myocardium

Avazmohammadi

Soares

et al. 2019

Annu. Rev. Biomed. Eng.

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Understanding and predicting the mechanical behavior of myocardium under healthy and pathophysiological conditions are vital to developing novel cardiac therapies and promoting personalized interventions. Within the past 30 years, various constitutive models have been proposed for the passive mechanical behavior of myocardium. These models cover a broad range of mathematical forms, microstructural observations, and specific test conditions to which they are fitted. We present a critical review of these models, covering both phenomenological and structural approaches, and their relations to the underlying structure and function of myocardium. We further explore the experimental and numerical techniques used to identify the model parameters. Next, we provide a brief overview of continuum-level electromechanical models of myocardium, with a focus on the methods used to integrate the active and passive components of myocardial behavior. We conclude by pointing to future directions in the areas of optimal form as well as new approaches for constitutive modeling of myocardium.

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A Microstructurally Based Multi-Scale Constitutive Model of Active Myocardial Mechanics

Cited by 6 publications

References 22 publications

Personalization of biomechanical simulations of the left ventricle by in-vivo cardiac DTI data: Impact of fiber interpolation methods

Personalization of biomechanical simulations of the left ventricle by in-vivo cardiac DTI data: Impact of fiber interpolation methods

An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline

A Contemporary Look at Biomechanical Models of Myocardium

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