A cardiac electromechanics model coupled with a lumped parameters model for closed-loop blood circulation. Part II: numerical approximation

Regazzoni, Francesco; Salvador, Matteo; Africa, Pasquale Claudio; Fedele, Marco; Dedè, Luca; Quarteroni, Alfio

doi:10.48550/arxiv.2011.15051

Cited by 8 publications

(47 citation statements)

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“…To numerically approximate this multiphysics and multiscale model, we adopt the segregated approach proposed in [51], by which the subproblems are solved sequentially. For space discretization, we rely on bilinear Finite Elements defined on hexahedral meshes, adopting a different spatial resolution for the electrophysiological and the mechanical variables.…”

Section: The Cardiac Electromechanical Modelmentioning

confidence: 99%

A machine learning method for real-time numerical simulations of cardiac electromechanics

Regazzoni,

Salvador,

Dedè

et al. 2021

Preprint

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We propose a machine learning-based method to build a system of differential equations that approximates the dynamics of 3D electromechanical models for the human heart, accounting for the dependence on a set of parameters. Specifically, our method permits to create a reducedorder model (ROM), written as a system of Ordinary Differential Equations (ODEs) wherein the forcing term, given by the right-hand side, consists of an Artificial Neural Network (ANN), that possibly depends on a set of parameters associated with the electromechanical model to be surrogated. This method is non-intrusive, as it only requires a collection of pressure and volume transients obtained from the full-order model (FOM) of cardiac electromechanics. Once trained, the ANN-based ROM can be coupled with hemodynamic models for the blood circulation external to the heart, in the same manner as the original electromechanical model, but at a dramatically lower computational cost. Indeed, our method allows for real-time numerical simulations of the cardiac function. Our results show that the ANN-based ROM is accurate with respect to the FOM (relative error between 10 −3 and 10 −2 for biomarkers of clinical interest), while requiring very small training datasets (30-40 samples). We demonstrate the effectiveness of the proposed method on two relevant contexts in cardiac modeling. First, we employ the ANN-based ROM to perform a global sensitivity analysis on both the electromechanical and hemodynamic models. Second, we perform a Bayesian estimation of two parameters starting from noisy measurements of two scalar outputs. In both these cases, replacing the FOM of cardiac electromechanics with the ANN-based ROM makes it possible to perform in a few hours of computational time all the numerical simulations that would be otherwise unaffordable, because of their overwhelming computational cost, if carried out with the FOM. As a matter of fact, our ANN-based ROM is able to speedup the numerical simulations by more than three orders of magnitude.

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Section: The Cardiac Electromechanical Modelmentioning

confidence: 99%

A machine learning method for real-time numerical simulations of cardiac electromechanics

Regazzoni,

Salvador,

Dedè

et al. 2021

Preprint

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“…In order to account for the transitional flow regime, the Variational Multiscale -Large Eddy Simulation (VMS-LES) turbulence model is considered [8,38,39], which also acts as a stabilization method for the CFD numerical scheme. The motion of the wall is derived from an EM simulation on the left ventricle (LV) [40,41] and then extended to the whole boundary of the domain of interest by means of an original preprocessing procedure, suitably considering a volume-based definition of the displacement of the left atrium (LA). By prescribing the EM-based velocity at the endocardial wall, we enforce a one-way (kinematic) coupling condition between EM and CFD in the LV.…”

Section: Introductionmentioning

confidence: 99%

“…By prescribing the EM-based velocity at the endocardial wall, we enforce a one-way (kinematic) coupling condition between EM and CFD in the LV. Furthermore, to address the interdependence between the fluid dynamics of the LH and the remaining cardiovascular system, we couple the 3D CFD model to the lumped-parameter (0D) model proposed in [40,41] representing the whole cardiovascular system.…”

Section: Introductionmentioning

confidence: 99%

“…This work is organized as follows: in Section 2 we recall the lumped-parameter circulation model of the cardiovascular system developed in [40,41]; in Section 3 we describe the mathematical model for the movingdomain hemodynamics in the LH with the immersed valves. In particular, in Section 3.4 we introduce the preprocessing procedure to reconstruct the displacement on the whole domain boundary based on an EM simulation of the LV, while in Section 3.5 we present the reduced valve dynamics model employed.…”

Section: Introductionmentioning

confidence: 99%

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A multiscale CFD model of blood flow in the human left heart coupled with a lumped-parameter model of the cardiovascular system

Zingaro,

Fumagalli,

Dede'

et al. 2021

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We present a novel computational model for the numerical simulation of the blood flow in the human heart by focusing on 3D fluid dynamics of the left heart. With this aim, we employ the Navier-Stokes equations in an Arbitrary Lagrangian Eulerian formulation to account for the endocardium motion, and we model both the mitral and the aortic valves by means of the Resistive Immersed Implicit Surface method. To impose a physiological displacement of the domain boundary, we use a 3D cardiac electromechanical model of the left ventricle coupled to a lumped-parameter (0D) closed-loop model of the circulation and the remaining cardiac chambers, including the left atrium. To extend the left ventricle motion to the endocardium of the left atrium and the ascending aorta, we introduce a preprocessing procedure that combines an harmonic extension of the left ventricle displacement with the motion of the left atrium based on the 0D model. We thus obtain a one-way coupled electromechanics-fluid dynamics model in the left ventricle. To better match the 3D CFD with blood circulation, we also couple the 3D Navier-Stokes equations -with domain motion driven by electromechanics -to the 0D circulation model. We obtain a multiscale coupled 3D-0D fluid dynamics model that we solve via a segregated numerical scheme. We carry out numerical simulations for a healthy left heart and we validate our model by showing that significant hemodynamic indicators are correctly reproduced. We finally show that our model is able to simulate the blood flow in the left heart in the scenario of mitral valve regurgitation.

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“…The initial development of life x has been oriented towards a heart module incorporating several state-of-the-art core models for the simulation of cardiac electrophysiology, mechanics, electromechanics, blood fluid dynamics and myocardial perfusion. Such models have been recently exploited for a variety of standalone or coupled simulations both under physiological and pathological conditions (see, e.g., [5,6,7,8,9,10,11,12,13,14]).…”

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confidence: 99%

life$^x$ - heart module: a high-performance simulator for the cardiac function. Package 1: Fiber generation

Africa¹,

Piersanti²,

Fedele³

et al. 2022

Preprint

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Modeling the whole cardiac function involves several complex multiphysics and multi-scale phenomena that are highly computationally demanding, which makes calling for simpler yet accurate, high-performance computational tools still a paramount challenge to be addressed. Despite all the efforts made by several research groups worldwide, no software has progressed as a standard reference tool for whole-heart fully-coupled cardiac simulations in the scientific community yet.In this work we present the first publicly released package of the heart module of life x , a high-performance solver for multi-physics and multi-scale problems, aimed at cardiac applications.The goal of life x is twofold. On the one side, it aims at making in silico experiments easily reproducible and accessible to the wider public, targeting also users with a background in medicine or bioengineering, thanks to an extensive documentation and user guide. On the other hand, being conceived as an academic research library, life x can be exploited by scientific computing experts to explore new modeling and numerical methodologies within a robust development framework.life x has been developed with a modular structure and will be released bundled in different modules/packages. This initial release includes a generator for myocardial fibers based on Laplace-Dirichlet-Rule-Based-Methods (LDRBMs). This report comes with an extensive Official website: https://lifex.gitlab.io/ Download link:

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A cardiac electromechanics model coupled with a lumped parameters model for closed-loop blood circulation. Part II: numerical approximation

Cited by 8 publications

References 0 publications

A machine learning method for real-time numerical simulations of cardiac electromechanics

A machine learning method for real-time numerical simulations of cardiac electromechanics

A multiscale CFD model of blood flow in the human left heart coupled with a lumped-parameter model of the cardiovascular system

life$^x$ - heart module: a high-performance simulator for the cardiac function. Package 1: Fiber generation

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