Robust and efficient fixed-point algorithm for the inverse elastostatic problem to identify myocardial passive material parameters and the unloaded reference configuration

Marx, Laura; Niestrawska, Justyna A.; Gsell, Matthias A. F.; Caforio, Federica; Plank, Gernot; Augustin, Christoph M.

doi:10.1016/j.jcp.2022.111266

Cited by 22 publications

(14 citation statements)

References 93 publications

(133 reference statements)

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“…Because of its bottom-up, physical nature, the model can be used to predict the EDPVR of a ventricle with given information, such as mechanical properties and thickness of the myocardium. In Figure 4A , Dokos et al (2002) represents the parameters for pig hearts, Demiray (1972) and Marx et al (2022) are for the human hearts. We further performed finite element simulations using COMSOL Multiphysics (version 6.0.0.405) with the same parameter sets.…”

Section: Validation and Discussionmentioning

confidence: 99%

Physical model of end-diastolic and end-systolic pressure-volume relationships of a heart

Zhang,

Kalhöfer-Köchling,

Bodenschatz

et al. 2023

Front. Physiol.

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Left ventricular stiffness and contractility, characterized by the end-diastolic pressure-volume relationship (EDPVR) and the end-systolic pressure-volume relationship (ESPVR), are two important indicators of the performance of the human heart. Although much research has been conducted on EDPVR and ESPVR, no model with physically interpretable parameters combining both relationships has been presented, thereby impairing the understanding of cardiac physiology and pathology. Here, we present a model that evaluates both EDPVR and ESPVR with physical interpretations of the parameters in a unified framework. Our physics-based model fits the available experimental data and in silico results very well and outperforms existing models. With prescribed parameters, the new model is used to predict the pressure-volume relationships of the left ventricle. Our model provides a deeper understanding of cardiac mechanics and thus will have applications in cardiac research and clinical medicine.

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Section: Validation and Discussionmentioning

confidence: 99%

Physical model of end-diastolic and end-systolic pressure-volume relationships of a heart

Zhang,

Kalhöfer-Köchling,

Bodenschatz

et al. 2023

Front. Physiol.

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“…We used incompressible hyperelasticity for modeling the passive response of cardiac ventricles after ischemia (Borowska et al 2022). Marx et al (2022) showed that the calculated material parameters may depend on the choice of the bulk modulus for a nearly incompressible myocardium. Moreover, prior studies have modeled cardiac ventricles as nearly incompressible during diastole but compressible during systole (Liu et al 2021) or exhibiting slight compressibility during passive behavior (McEvoy et al 2018).…”

Section: Estimated Passive Propertiesmentioning

confidence: 99%

Personalized Evaluation of the Passive Myocardium in Ischemic Cardiomyopathy via Computational Modeling Using Bayesian Optimization

Torbati,

Daneshmehr,

Pouraliakbar

et al. 2024

Preprint

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Biomechanics-based patient-specific modeling is a promising approach that has proved invaluable for its clinical potential to assess the adversities caused by ischemic heart disease (IDH). In the present study, we propose a framework to find the passive material properties of the myocardium and the unloaded shape of cardiac ventricles simultaneously in patients diagnosed with ischemic cardiomyopathy (ICM). This was achieved by minimizing the difference between the simulated and target end-diastolic pressure-volume relationships (EDPVRs) using black-box Bayesian optimization, based on the finite element analysis (FEA). End-diastolic (ED) biventricular geometry and the location of the ischemia were determined from cardiac magnetic resonance (CMR) imaging. We employed our pipeline to model the cardiac ventricles of three patients aged between 57 and 66 years, with and without the inclusion of valves. An excellent agreement between the simulated and target EDPVRs has been reached. Our results revealed that the incorporation of valvular springs typically leads to lower hyperelastic parameters for both healthy and ischemic myocardium, as well as a higher fiber Green strain in the viable regions compared to models without valvular stiffness. Furthermore, the addition of valve-related effects did not result in significant changes in myofiber stress after optimization. We concluded that more accurate results could be obtained when cardiac valves were considered in modeling ventricles. The present novel and practical methodology paves the way for developing digital twins of ischemic cardiac ventricles, providing a non-invasive assessment for designing optimal personalized therapies in precision medicine.

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“…In order to alleviate the problems that arise in such cases, one typically resorts to solving the Laplace problems in suitably selected subdomains of the target geometry and calculates the material orientation per subdomain. 48,49 Specifically for aortic dissections, prior knowledge of the expected configurations allows for a straightforward approach, avoiding the selection of subdomains and tuning of associated boundary conditions. The key idea in this alternative approach, which was initially proposed in Schussnig et al, 50 is to combine a single auxiliary Laplace problem solved for the axial direction with a rule-based extrapolation of the normal vector on the intimal surface into the tissue layer by layer with threshold-based averaging.…”

Section: Local Fiber Orientationsmentioning

confidence: 99%

On the role of tissue mechanics in fluid–structure interaction simulations of patient‐specific aortic dissection

Schussnig,

Rolf‐Pissarczyk,

Bäumler

et al. 2024

Numerical Meth Engineering

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Modeling an aortic dissection represents a particular challenge from a numerical perspective, especially when it comes to the interaction between solid (aortic wall) and liquid (blood flow). The complexity of patient‐specific simulations requires a variety of parameters, modeling assumptions and simplifications that currently hinder their routine use in clinical settings. We present a numerical framework that captures, among other things, the layer‐specific anisotropic properties of the aortic wall, the non‐Newtonian behavior of blood, patient‐specific geometry, and patient‐specific flow conditions. We compare hemodynamic indicators and stress measurements in simulations with increasingly complex material models for the vessel tissue ranging from rigid walls to anisotropic hyperelastic materials. We find that for the present geometry and boundary conditions, rigid wall simulations produce different results than fluid–structure interaction simulations. Considering anisotropic fiber contributions in the tissue model, stress measurements in the aortic wall differ, but shear stress‐based biomarkers are less affected. In summary, the increasing complexity of the tissue model enables capturing more details. However, an extensive parameter set is also required. Since the simulation results depend on these modeling choices, variations can lead to different recommendations in clinical applications.

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Robust and efficient fixed-point algorithm for the inverse elastostatic problem to identify myocardial passive material parameters and the unloaded reference configuration

Cited by 22 publications

References 93 publications

Physical model of end-diastolic and end-systolic pressure-volume relationships of a heart

Physical model of end-diastolic and end-systolic pressure-volume relationships of a heart

Personalized Evaluation of the Passive Myocardium in Ischemic Cardiomyopathy via Computational Modeling Using Bayesian Optimization

On the role of tissue mechanics in fluid–structure interaction simulations of patient‐specific aortic dissection

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