Modeling and personalisation of a coupled 3D-1D multiphysics model of the cardiovascular system

Course "Advanced Topics in Biomathematics"
15 May 2023
Start time 
4:30 pm
Polo Ferrari 1 - Via Sommarive 5, Povo (Trento)
Room A204 – Povo1 and via Zoom (please contact for credentials)
Department of Mathematics
Target audience: 
University community
Contact person: 
Dr. Simone Pezzuto
Contact details: 
Staff Dipartimento di Matematica
Federica Caforio (Karl-Franzens-Universität Graz)

Abstract: Image-based computational models of cardiac electromechanics (EM) are a powerful tool to understand the mechanisms underlying physiological and pathological conditions in cardiac function and to improve diagnosis and therapy planning. Challenges in cardiovascular modelling are associated with the intrinsic complexity of the cardiovascular system and the necessity to develop computational schemes that are robust and efficient. 
In particular, the bidirectional coupling of the circulatory system with the heart, which allows changes in the arterial system to adjust the pulsatile load on the heart, is a crucial aspect in the cardiac mechanical function. In this talk, we present a novel and robust strategy for coupling a 3D cardiac EM model with 1D arterial blood flow model. In particular, a personalised coupled 3D-1D model of the left ventricle and artery system is developed for the first time and employed in numerical benchmarks to illustrate the accuracy and robustness of our method. The physiological response of the coupled system to alterations in the arterial system affecting pulse wave propagation, such as aortic stiffening and aortic stenosis, is also studied.
In addition, model personalisation is crucial to enable the clinical translation of such models. To this aim, we also present the results of a variance-based sensitivity analysis for the new coupled 3D-1D model. The method under consideration is based on the employment of Gaussian process emulators to build surrogates for the coupled model and efficiently perform sensitivity analyses to characterise the relative importance of the model input parameters to the model output.