A Multiscale Cardiac Model for Fast Personalisation and Exploitation

  • Roch MolleroEmail author
  • Xavier Pennec
  • Hervé Delingette
  • Nicholas Ayache
  • Maxime Sermesant
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)


Computer models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. However a single 3D simulation can be computationally expensive and long, which can make some practical applications such as the personalisation phase, or a sensitivity analysis of mechanical parameters over the simulated behaviour quite slow. In this manuscript we present a multiscale 0D/3D model which allows us to have a reliable (and extremely fast) approximation of the behaviour of the 3D model under a few simplifying assumptions. We first detail the two different models, then explain the coupling of the two models to get fast 0D approximation of 3D simulations. Finally we demonstrated how the multiscale model can speed-up an efficient optimization algorithm, which enables a fast personalisation of the 3D simulations by leveraging on the advantages of each scale.


Fast Personalisation Cardiac Model Phasic Personality Reasonable Computational Burden Local Gradient Descent 
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This work has been partially funded by the EU FP7-funded project MD-Paedigree (Grant Agreement 600932) and contributes to the objectives of the ERC advanced grant MedYMA (2011-291080).


  1. 1.
    Neumann, D., et al.: Robust image-based estimation of cardiac tissue parameters and their uncertainty from noisy data. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part II. LNCS, vol. 8674, pp. 9–16. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  2. 2.
    Konukoglu, E., Relan, J., Cilingir, U., Menze, B.H., Chinchapatnam, P., Jadidi, A., Cochet, H., Hocini, M., Delingette, H., Jaïs, P., et al.: Efficient probabilistic model personalization integrating uncertainty on data and parameters: application to eikonal-diffusion models in cardiac electrophysiology. Prog. Biophys. Mol. Biol. 107(1), 134–146 (2011)CrossRefGoogle Scholar
  3. 3.
    Moghadam, M.E., Vignon-Clementel, I.E., Figliola, R., Marsden, A.L.: A modular numerical method for implicit 0d/3d coupling in cardiovascular finite element simulations. J. Comput. Phys. 244, 63–79 (2013)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Chapelle, D., Le Tallec, P., Moireau, P., Sorine, M.: Energy-preserving muscle tissue model: formulation and compatible discretizations. Int. J. Multiscale Comput. Eng. 10(2), 189–211 (2012)CrossRefGoogle Scholar
  5. 5.
    Marchesseau, S., Delingette, H., Sermesant, M., Ayache, N.: Fast parameter calibration of a cardiac electromechanical model from medical images based on the unscented transform. Biomech. Model. Mechanobiol. 12(4), 815–831 (2013)CrossRefGoogle Scholar
  6. 6.
    Caruel, M., Chabiniok, R., Moireau, P., Lecarpentier, Y., Chapelle, D.: Dimensional reductions of a cardiac model for effective validation and calibration. Biomech. Model. Mechanobiol. 13(4), 897–914 (2014)CrossRefGoogle Scholar
  7. 7.
    Garny, A., Hunter, P.J.: Opencor: a modular and interoperable approach to computational biology. Front. Physiol. 6, 26 (2015)CrossRefGoogle Scholar
  8. 8.
    Hansen, N.: The CMA evolution strategy: a comparing review. In: Lozano, J.A., Larrañaga, P., Inza, I., Bengoetxea, E. (eds.) Towards a New Evolutionary Computation. Advances in the Estimation of Distribution Algorithms. Studies in Fuzziness and Soft Computing, vol. 192, pp. 75–102. Springer, Berlin, Heidelberg (2006)CrossRefGoogle Scholar

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Authors and Affiliations

  • Roch Mollero
    • 1
    Email author
  • Xavier Pennec
    • 1
  • Hervé Delingette
    • 1
  • Nicholas Ayache
    • 1
  • Maxime Sermesant
    • 1
  1. 1.Inria - Asclepios Research ProjectSophia AntipolisFrance

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