Alfio Quarteroni | EPFL Graph Search

Of italian nationality, Alfio Quarteroni was born on May 30th 1952. He pursued his studies in mathematics at University of Pavia and at University of Paris VI. In 1986 he was nominated full professor at Catholic University of Brescia, later professor in mathematics at University of Minnesota at Minneapolis and professor in numerical analysis at Politecnico di Milano. He is designated full professor in 1997 and enters into service with EPFL in 1998. At EPFL, he teaches numerical analysis to engineers and mathematicians and holds specialized courses about mathematical modelling and scientific computing for master and PhD students. He had been scientific director of CRS4, plenary speaker of more than two hundred international conferences; he is member of the European Academy of Sciences, the Italian Academy of Sciences, the Lombard Academy of Science and Letters. He is Editor in Chief of two book series (MS&A and Unitext) by Springer, associate editor of 25 international journals. He has been plenary speaker at the International Congress of Mathematicians ICM2006. He had been responsible of several European research networks. His team has carried out the aerodynamic and hydrodynamic simulations for the optimization of Alinghi, the Swiss sailing yacht that has won two editions of the America's Cup in 2003 and 2007.

A mathematical model that integrates cardiac electrophysiology, mechanics, and fluid dynamics: Application to the human left heart

Alfio Quarteroni

We propose a mathematical and numerical model for the simulation of the heart function that couples cardiac electrophysiology, active and passive mechanics and hemodynamics, and includes reduced models for cardiac valves and the circulatory system. Our model accounts for the major feedback effects among the different processes that characterize the heart function, including electro-mechanical and mechano-electrical feedback as well as force-strain and force-velocity relationships. Moreover, it provides a three-dimensional representation of both the cardiac muscle and the hemodynamics, coupled in a fluid-structure interaction (FSI) model. By leveraging the multiphysics nature of the problem, we discretize it in time with a segregated electrophysiology-force generation-FSI approach, allowing for efficiency and flexibility in the numerical solution. We employ a monolithic approach for the numerical discretization of the FSI problem. We use finite elements for the spatial discretization of partial differential equations. We carry out a numerical simulation on a realistic human left heart model, obtaining results that are qualitatively and quantitatively in agreement with physiological ranges and medical images.

WILEY2023

Impact of atrial fibrillation on left atrium haemodynamics: A computational fluid dynamics study

Alfio Quarteroni

We analyse the haemodynamics of the left atrium, highlighting differences between healthy individuals and patients affected by atrial fibrillation. The computational study is based on patient-specific geometries of the left atria to simulate blood flow dynamics. We design a novel procedure to compute the boundary data for the 3D haemodynamic simulations, which are particularly useful in absence of data from clinical measurements. With this aim, we introduce a parametric definition of atrial displacement, and we use a closed-loop lumped parameter model of the whole cardiovascular circulation conveniently tuned on the basis of the patient's characteristics. We evaluate several fluid dynamics indicators for atrial haemodynamics, validating our numerical results in terms of clinical measurements; we investigate the impact of geometric and clinical characteristics on the risk of thrombosis. To highlight the correlation of thrombus formation with atrial fibrillation, according to medical evidence, we propose a novel indicator: age stasis. It arises from the combination of Eulerian and Lagrangian quantities. This indicator identifies regions where slow flow cannot properly rinse the chamber, accumulating stale blood particles, and creating optimal conditions for clots formation.

PERGAMON-ELSEVIER SCIENCE LTD2022

PDE-Aware Deep Learning for Inverse Problems in Cardiac Electrophysiology

Simone Deparis, Stefano Pagani, Alfio Quarteroni, Riccardo Tenderini

In this work, we present a PDE-aware deep learning model for the numerical solution to the inverse problem of electrocardiography. The model both leverages data availability and exploits the knowledge of a physically based mathematical model, expressed by means of partial differential equations (PDEs), to carry out the task at hand. The goal is to estimate the epicardial potential field from measurements of the electric potential at a discrete set of points on the body surface. The employment of deep learning techniques in this context is made difficult by the low amount of clinical data at disposal, as measuring cardiac potentials requires invasive procedures. Suitably exploiting the underlying physically based mathematical model allowed circumventing the data availability issue and led to the development of fast-training and low-complexity models. Physical awareness has been pursued by means of two elements: the projection of the epicardial potential onto a space-time reduced subspace, spanned by the numerical solutions of the governing PDEs, and the inclusion of a tensorial reduced basis solver of the forward problem in the network architecture. Numerical tests have been conducted only on synthetic data, obtained via a full order model approximation of the problem at hand, and two variants of the model have been addressed. Both proved to be accurate, up to an average

\ell^1

-norm relative error on epicardial activation maps of 3.5%, and both could be trained in

\approx$$15

min. Nevertheless, some improvements, mostly concerning data generation, are necessary in order to bridge the gap with clinical applications.

2022

PDE-Aware Deep Learning for Inverse Problems in Cardiac Electrophysiology

Simone Deparis, Stefano Pagani, Alfio Quarteroni, Riccardo Tenderini

\ell^1

-norm relative error on epicardial activation maps of 3.5%, and both could be trained in

\approx$$15

min. Nevertheless, some improvements, mostly concerning data generation, are necessary in order to bridge the gap with clinical applications.

2022