Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models - Archive ouverte HAL Access content directly
Journal Articles Biomechanics and Modeling in Mechanobiology Year : 2017

Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models

(1) , (1) , (1) , (2) , (1) , (1)
1
2

Abstract

Personalised computational models of the heart are of increasing interest for clinical applica- tions due to their discriminative and predictive abili- ties. However, the simulation of a single heartbeat with a 3D cardiac electromechanical model can be long and computationally expensive, which makes some practical applications, such as the estimation of model parame- ters from clinical data (the personalisation), very slow. Here we introduce an original multi delity approach between a 3D cardiac model and a simpli ed "0D" ver- sion of this model, which enables to get reliable (and extremely fast) approximations of the global behavior of the 3D model using 0D simulations. We then use this multi delity approximation to speed-up an ecient parameter estimation algorithm, leading to a fast and computationally ecient personalisation method of the 3D model. In particular, we show results on a cohort of 121 di erent heart geometries and measurements. Fi- nally, an exploitable code of the 0D model with scripts to perform parameter estimation will be released to the community.
Fichier principal
Vignette du fichier
BMMB_Paper___Multifidelity_CMA___Camera_Ready.pdf (2.95 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01656008 , version 1 (05-12-2017)

Identifiers

Cite

Roch Molléro, Xavier Pennec, Hervé Delingette, Alan Garny, Nicholas Ayache, et al.. Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models. Biomechanics and Modeling in Mechanobiology, 2017, pp.1-16. ⟨10.1007/s10237-017-0960-0⟩. ⟨hal-01656008⟩
554 View
314 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More