The importance of the pericardium for cardiac biomechanics: from physiology to computational modeling

Pfaller, Martin R.; Hörmann, Julia Maria; Weigl, Martina; Nagler, Andreas; Chabiniok, Radomí­r; Bertoglio, C; Wall, Wolfgang A.

doi:10.1007/s10237-018-1098-4

Cited by 102 publications

(105 citation statements)

References 69 publications

(102 reference statements)

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“…Such minimum stress was ignored in the present study. In addition, the effect of pericardial stress was not modelled in this study. To date, information on the spatial and temporal variations of pericardial stress is still lacking, and its influence on LV regional mechanics can only be studied qualitatively .…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the effect of pericardial stress was not modelled in this study. To date, information on the spatial and temporal variations of pericardial stress is still lacking, and its influence on LV regional mechanics can only be studied qualitatively . In one study that compared free, fixed top and sliding pericardial boundary conditions, the longitudinal strain at the right ventricle was dramatically different when a sliding pericardial boundary condition was used.…”

Section: Discussionmentioning

confidence: 99%

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The role of end‐diastolic myocardial fibre stretch on infarct extension

Leong

Dokos

Andriyana

et al. 2019

Numer Methods Biomed Eng

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Myocardial infarct extension, a process involving the enlargement of infarct and border zone, leads to progressive degeneration of left ventricular (LV) function and eventually gives rise to heart failure. Despite carrying a high risk, the causation of infarct extension is still a subject of much speculation. In this study, patient-specific LV models were developed to investigate the correlation between infarct extension and impaired regional mechanics. Subsequently, sensitivity analysis was performed to examine the causal factors responsible for the impaired regional mechanics observed in regions surrounding the infarct and border zone. From our simulations, fibre strain, fibre stress and fibre stress-strain loop (FSSL) were the key biomechanical variables affected in these regions. Among these variables, only FSSL was correlated with infarct extension, as reflected in its work density dissipation (WDD) index value, with high WDD indices recorded at regions with infarct extension. Impaired FSSL is caused by inadequate contraction force generation during the isovolumic contraction and ejection phases. Our further analysis revealed that the inadequacy in contraction force generation is not necessarily due to impaired myocardial intrinsic contractility, but at least in part, due to inadequate muscle fibre stretch at end-diastole, which depresses the ability of myocardium to generate adequate contraction force in the subsequent systole (according to the Frank-Starling law). Moreover, an excessively stiff infarct may cause its neighbouring myocardium to be understretched at end-diastole, subsequently depressing the systolic contractile force of the neighbouring myocardium, which was found to be correlated with infarct extension.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The role of end‐diastolic myocardial fibre stretch on infarct extension

Leong

Dokos

Andriyana

et al. 2019

Numer Methods Biomed Eng

View full text Add to dashboard Cite

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“…It is valid for any arbitrary residuals R S and R 0D . For details of the model used here, again see Reference .…”

Section: D‐0d Coupled Cardiovascular Modelingmentioning

confidence: 99%

Using parametric model order reduction for inverse analysis of large nonlinear cardiac simulations

Pfaller

Varona

Lang

et al. 2020

Numer Methods Biomed Eng

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Predictive high-fidelity finite element simulations of human cardiac mechanics commonly require a large number of structural degrees of freedom. Additionally, these models are often coupled with lumped-parameter models of hemodynamics. High computational demands, however, slow down model calibration and therefore limit the use of cardiac simulations in clinical practice. As cardiac models rely on several patient-specific parameters, just one solution corresponding to one specific parameter set does not at all meet clinical demands. Moreover, while solving the nonlinear problem, 90% of the computation time is spent solving linear systems of equations. We propose to reduce the structural dimension of a monolithically coupled structure-Windkessel system by projection onto a lower-dimensional subspace. We obtain a good approximation of the displacement field as well as of key scalar cardiac outputs even with very few reduced degrees of freedom, while achieving considerable speedups. For subspace generation, we use proper orthogonal decomposition of displacement snapshots. Following a brief comparison of subspace interpolation methods, we demonstrate how projection-based model order reduction can be easily integrated into a gradient-based optimization.We demonstrate the performance of our method in a real-world multivariate inverse analysis scenario. Using the presented projection-based model order reduction approach can significantly speed up model personalization and could be used for many-query tasks in a clinical setting.cardiac mechanics, inverse analysis, parametric model order reduction, proper orthogonal decomposition 1 | INTRODUCTION Cardiac solid mechanics simulations consist of solving large-deformation, materially nonlinear, elastodynamic coupled boundary-value problems. There exist different approaches to incorporate blood flow into the computational model. Three-dimensional fluid-structure interaction is resolved for example in References 1 and 2. As the exact fluid dynamics of blood within the heart are, however, usually not needed, the structural model is commonly coupled to | Model order reductionThe needed huge number of degrees of freedom (DOFs) and other challenges of solving nonlinear problems make the solution of cardiac models computationally expensive and limit the models' use in clinical practice. For example, using a single node with 24 cores, a simulation of one heartbeat, which takes about 1 s in reality, takes about 1 d to compute with our high-fidelity four chamber model. 9 The potential to reduce computation time motivates the use of reduced order models (ROMs). In this work, we solely consider model order reduction (MOR) of time-dependent parametric problems. In the following, different strategies in reduced order modeling are reviewed.An important category of cardiac ROMs is made up by simplified modeling. For these models, the same system of differential equations as for the full order model (FOM) is solved, but on a simplified analytical geometry. The displacements are commonly param...

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“…The full order model (FOM) is given by a monolithically coupled structural‐windkessel model, visualized in Figure . The structural finite element model is formulated as an elastodynamic IBVP with large deformations, nonlinear hyperelastic anisotropic material, and active stress component, denoted by the effective dynamic residual R S using the generalized α ‐method .…”

Section: Projection‐based Model Order Reductionmentioning

confidence: 99%

Challenges of order reduction techniques for problems involving polymorphic uncertainty

et al. 2019

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Modeling of mechanical systems with uncertainties is extremely challenging and requires a careful analysis of a huge amount of data. Both, probabilistic modeling and nonprobabilistic modeling require either an extremely large ensemble of samples or the introduction of additional dimensions to the problem, thus, resulting also in an enormous computational cost growth. No matter whether the Monte‐Carlo sampling or Smolyak's sparse grids are used, which may theoretically overcome the curse of dimensionality, the system evaluation must be performed at least hundreds of times. This becomes possible only by using reduced order modeling and surrogate modeling. Moreover, special approximation techniques are needed to analyze the input data and to produce a parametric model of the system's uncertainties. In this paper, we describe the main challenges of approximation of uncertain data, order reduction, and surrogate modeling specifically for problems involving polymorphic uncertainty. Thereby some examples are presented to illustrate the challenges and solution methods.

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The importance of the pericardium for cardiac biomechanics: from physiology to computational modeling

Cited by 102 publications

References 69 publications

The role of end‐diastolic myocardial fibre stretch on infarct extension

The role of end‐diastolic myocardial fibre stretch on infarct extension

Using parametric model order reduction for inverse analysis of large nonlinear cardiac simulations

Challenges of order reduction techniques for problems involving polymorphic uncertainty

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