A comprehensive and biophysically detailed computational model of the whole human heart electromechanics

Fedele, Marco; Piersanti, Roberto; Regazzoni, Francesco; Salvador, Matteo; Africa, Pasquale Claudio; Bucelli, Michele; Zingaro, Alberto; Dedè, Luca; Quarteroni, Alfio

doi:10.48550/arxiv.2207.12460

Cited by 4 publications

(33 citation statements)

References 127 publications

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“…There are other general-purpose open source software such as LifeV (Bertagna et al, 2017) and FEniCS (Logg et al, 2012), that can be flexibly adapted to simulate different physics in the heart, but significant development effort may be required for this purpose. Recently, Quarteroni et al have also been developing an open source simulator for the cardiac function, and the fiber generation module (Africa et al, 2022) and electromechanics (Fedele et al, 2022) have been announced so far.…”

Section: Statement Of Needmentioning

confidence: 99%

svFSI: A Multiphysics Package for Integrated Cardiac Modeling

Zhu¹,

Vedula²,

Parker³

et al. 2022

JOSS

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Heart disease is the number one cause of death in the US (Xu et al., 2020). Many efforts have been devoted to studying its progression, diagnosis, and treatment. During the past decade, computational modeling has made significant inroads into the research of heart disease. The heart is inherently a multiphysics system that includes electrophysiology, tissue mechanics, and blood dynamics. Its normal function starts with the propagation of electrical signals that trigger the active contraction of the heart muscle to pump blood into the circulatory system. Rooted in fundamental laws of physics such as the balance of mass, momentum, and energy, computational modeling has been instrumental in studying cardiac physiology such as left ventricular function (Mittal et al., 2015), cardiac arrhythmia (Trayanova, 2011), and blood flow in the cardiovascular system (Arzani & Shadden, 2018;Grande Gutiérrez et al., 2021). svFSI is the first open source software that specializes in enabling coupled electro-mechano-hemodynamic simulations of the heart.

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Section: Statement Of Needmentioning

confidence: 99%

svFSI: A Multiphysics Package for Integrated Cardiac Modeling

Zhu¹,

Vedula²,

Parker³

et al. 2022

JOSS

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“…A possible approach is the so called geometric multiscale modeling [50]: the region of interest (in our case, the whole-heart) is described by a 3D model, while the remaining part of the circulation is addressed by means of lumped-parameter models, as 0D [50][51][52][53][54], or 1D [40,50,[55][56][57]. The geometric multiscale modeling allows to account for the mutual interaction between the heart and the circulatory system, especially if the lumped parameter model provides a closed-loop description of the vascular network, as done in [26,51,[58][59][60][61].…”

Section: Introductionmentioning

confidence: 99%

An electromechanics-driven fluid dynamics model for the simulation of the whole human heart

Zingaro¹,

Bucelli²,

Piersanti³

et al. 2023

Preprint

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We introduce a multiphysics and geometric multiscale computational model, suitable to describe the hemodynamics of the whole human heart, driven by a four-chamber electromechanical model. We first present a study on the calibration of the biophysically detailed RDQ20 activation model that is able to reproduce the physiological range of hemodynamic biomarkers. Then, we demonstrate that the ability of the force generation model to reproduce certain microscale mechanisms, such as the dependence of force on fiber shortening velocity, is crucial to capture the overall physiological mechanical and fluid dynamics macroscale behavior. This motivates the need for using multiscale models with high biophysical fidelity, even when the outputs of interest are relative to the macroscale. We show that the use of a high-fidelity electromechanical model, combined with a detailed calibration process, allows us to achieve a remarkable biophysical fidelity in terms of both mechanical and hemodynamic quantities. Indeed, our electromechanical-driven CFD simulationscarried out on an anatomically accurate geometry of the whole heart -provide results that match the cardiac physiology both qualitatively (in terms of flow patterns) and quantitatively (when comparing in silico results with biomarkers acquired in vivo). Moreover, we consider the pathological case of left bundle branch block, and we investigate the consequences that an electrical abnormality has on cardiac hemodynamics thanks to our multiphysics integrated model. The computational model that we propose can faithfully predict a delay and an increasing wall shear stress in the left ventricle in the pathological condition. The interaction of different physical processes in an integrated framework allows us to faithfully describe and model this pathology, by capturing and reproducing the intrinsic multiphysics nature of the human heart.

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“…Personalization of computational heart models is necessary to better addressing patient-specific pathophysiology and for assisting the clinicians in the decision-making process for medical treatment [26,51]. In this field, sophisticated cell-to-organ level mathematical models comprising systems of nonlinear differential equations, along with efficient and accurate numerical methods, have been developed to properly describe the physical phenomena underlying the cardiac function [1,9,11,29,33,39,49,50].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we present a numerical strategy to perform parameter calibration with uncertainty quantification (UQ) by means of a reduced-order model (ROM) of 3D cardiac electromechanics coupled with closed-loop blood circulation [40]. The ROM, which is based on Artificial Neural Networks (ANNs), encodes the dynamics of the pressure-volume relationship obtained from an accurate full-order model (FOM) of the cardiac function [9,33,39]. Moreover, it allows for real-time numerical simulations on a personal computer while embedding electromechanical parameters of the 3D mathematical model [40].…”

Section: Introductionmentioning

confidence: 99%

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Fast and robust parameter estimation with uncertainty quantification for the cardiac function

Salvador¹,

Regazzoni²,

Dedè³

et al. 2022

Preprint

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Parameter estimation and uncertainty quantification are crucial in computational cardiology, as they enable the construction of digital twins that faithfully replicate the behavior of physical patients. Robust and efficient mathematical methods must be designed to fit many model parameters starting from a few, possibly non-invasive, noisy observations. Moreover, the effective clinical translation requires short execution times and a small amount of computational resources. In the framework of Bayesian statistics, we combine Maximum a Posteriori estimation and Hamiltonian Monte Carlo to find an approximation of model parameters and their posterior distributions. To reduce the computational effort, we employ an accurate Artificial Neural Network surrogate of 3D cardiac electromechanics model coupled with a 0D cardiocirculatory model. Fast simulations and minimal memory requirements are achieved by using matrix-free methods, automatic differentiation and automatic vectorization. Furthermore, we account for the surrogate modeling error and measurement error. We perform three different in silico test cases, ranging from the ventricular function to the entire cardiovascular system, involving whole-heart mechanics, arterial and venous circulation. The proposed method is robust when high levels of signal-to-noise ratio are present in the quantities of interest in combination with a random initialization of the model parameters in suitable intervals. As a matter of fact, by employing a single central processing unit on a standard laptop and a few hours of computations, we attain small relative errors for all model parameters and we estimate posterior distributions that contain the true values inside the 90% credibility regions. With these benefits, our approach meets the requirements for clinical exploitation, while being compliant with Green Computing practices.

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A comprehensive and biophysically detailed computational model of the whole human heart electromechanics

Cited by 4 publications

References 127 publications

svFSI: A Multiphysics Package for Integrated Cardiac Modeling

svFSI: A Multiphysics Package for Integrated Cardiac Modeling

An electromechanics-driven fluid dynamics model for the simulation of the whole human heart

Fast and robust parameter estimation with uncertainty quantification for the cardiac function

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