Biomechanics and Modeling in Mechanobiology

, Volume 17, Issue 1, pp 285–300 | Cite as

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

  • Roch MolléroEmail author
  • Xavier Pennec
  • Hervé Delingette
  • Alan Garny
  • Nicholas Ayache
  • Maxime Sermesant
Original Paper


Personalised computational models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. 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 parameters from clinical data (the personalisation), very slow. Here we introduce an original multifidelity approach between a 3D cardiac model and a simplified “0D” version of this model, which enables to get reliable (and extremely fast) approximations of the global behaviour of the 3D model using 0D simulations. We then use this multifidelity approximation to speed-up an efficient parameter estimation algorithm, leading to a fast and computationally efficient personalisation method of the 3D model. In particular, we show results on a cohort of 121 different heart geometries and measurements. Finally, an exploitable code of the 0D model with scripts to perform parameter estimation will be released to the community.


Cardiac electromechanical modelling Reduced model Multifidelity modelling Parameter estimation Finite element mechanical modelling 



This work has been partially funded by the European Union FP7-funded project MD-Paedigree (Grant Agreement 600932) and contributes to the objectives of the European Research Council advanced Grant MedYMA (2011-291080).

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

Informed consent

Informed consent was obtained from the subjects, and the protocol was approved by the local research ethics committee.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Inria, Asclepios Research ProjectSophia AntipolisFrance
  2. 2.Auckland Bioengineering InstituteUniversity of AucklandAucklandNew Zealand

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