Medical & Biological Engineering & Computing

, Volume 51, Issue 11, pp 1221–1233 | Cite as

Fundamental principles of data assimilation underlying the Verdandi library: applications to biophysical model personalization within euHeart

  • D. Chapelle
  • M. Fragu
  • V. Mallet
  • P. Moireau
Special Issue - Original Article


We present the fundamental principles of data assimilation underlying the Verdandi library, and how they are articulated with the modular architecture of the library. This translates—in particular—into the definition of standardized interfaces through which the data assimilation library interoperates with the model simulation software and the so-called observation manager. We also survey various examples of data assimilation applied to the personalization of biophysical models, in particular, for cardiac modeling applications within the euHeart European project. This illustrates the power of data assimilation concepts in such novel applications, with tremendous potential in clinical diagnosis assistance.


Data assimilation Software library Personalized modeling Cardiac models 



This work has been partially supported by the European Commission (FP7-ICT-2007-224495: euHeart and FP7-ICT-2009-269978: VPH-Share).


  1. 1.
    Auroux D, Blum J (2008) A nudging-based data assimilation method: the Back and Forth Nudging (BFN) algorithm. Nonlinear Process Geophys 15(2):305–319CrossRefGoogle Scholar
  2. 2.
    Bensoussan A (1971) Filtrage Optimal des Systèmes Linéaires. DunodGoogle Scholar
  3. 3.
    Bertoglio C, Moireau P, Gerbeau JF (2012) Sequential parameter estimation for fluid-structure problems. application to hemodynamics. Int J Num Methods Biomed Eng (published online). doi: 10.1002/cnm.1476
  4. 4.
    Chabiniok R, Moireau P, Lesault PF, Rahmouni A, Deux JF, Chapelle D (2011) Estimation of tissue contractility from cardiac cine-mri using a biomechanical heart model. Biomech Model Mechanobiol (published online)Google Scholar
  5. 5.
    Chapelle D, Gariah A, Moireau P, Sainte-Marie J (2012) A Galerkin strategy with proper orthogonal decomposition for parameter-dependent problems—analysis, assessments and applications to parameter estimation. M2AN (submitted)Google Scholar
  6. 6.
    Chavent G (2010) Nonlinear least squares for inverse problems. Springer, BerlinGoogle Scholar
  7. 7.
    D’Elia M, Perego M, Veneziani A (2011) A variational data assimilation procedure for the incompressible Navier–Stokes equations in hemodynamics. J Sci Comput 1–20Google Scholar
  8. 8.
    Delingette H, Billet F, Wong KCL, Sermesant M, Rhode K, Ginks M, Rinaldi CA, Razavi R, Ayache N (2012) Personalization of cardiac motion and contractility from images using variational data assimilation. IEEE Trans Biomed Eng 59(1):20–24PubMedCrossRefGoogle Scholar
  9. 9.
    Evensen G (1994) Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res 99:10143–10162CrossRefGoogle Scholar
  10. 10.
    Hoteit I, Pham DT, Blum J (2002) A simplified reduced order Kalman filtering and application to altimetric data assimilation in Tropical Pacific. J Mar Syst 36(1–2):101–127CrossRefGoogle Scholar
  11. 11.
    Imperiale A, Chabiniok R, Moireau P, Chapelle D (2011) Constitutive parameter estimation methodology using tagged-MRI data. In: Proceedings of FIMH’11. Springer, BerlinGoogle Scholar
  12. 12.
    Julier S, Uhlmann J, Durrant-Whyte H (2000) A new method for the nonlinear transformation of means and covariances in filter and estimators. IEEE Trans Autom Control 45(3):447–482CrossRefGoogle Scholar
  13. 13.
    Konukoglu E, Relan J, Cilingir U, Menze BH, Chinchapatnam P, Jadidi A, Cochet H, Hocini M, Delingette H, Jais P, Haïssaguerre M, Ayache N, Sermesant M (2011) Efficient probabilistic model personalization integrating uncertainty on data and parameters: application to Eikonal-Diffusion models in cardiac electrophysiology. Prog Biophys Mol Bio 107(1):134–146PubMedCrossRefGoogle Scholar
  14. 14.
    Luenberger DG (1963) Determining the state of a linear with observers of low dynamic order. PhD Thesis, Stanford UniversityGoogle Scholar
  15. 15.
    Moireau P (2008) Filtering-based data assimilation for second-order hyperbolic PDEs. Applications in cardiac mechanics. PhD Thesis, Ecole PolytechniqueGoogle Scholar
  16. 16.
    Moireau P, Chapelle D (2010) Reduced-order unscented Kalman filtering with application to parameter identification in large-dimensional systems. COCV (published online). doi: 10.1051/cocv/2010006
  17. 17.
    Moireau P, Chapelle D (2011) Erratum of article “reduced-order unscented Kalman filtering with application to parameter identification in large-dimensional systems”. COCV. 17:406–409. doi: 10.1051/cocv/2011001 Google Scholar
  18. 18.
    Moireau P, Chapelle D, Le Tallec P (2008) Joint state and parameter estimation for distributed mechanical systems. Comput Methods Appl Mech Eng 197:659–677CrossRefGoogle Scholar
  19. 19.
    Moireau P, Chapelle D, Le Tallec P (2009) Filtering for distributed mechanical systems using position measurements: perspectives in medical imaging. Inverse Probl 25(3):035010. doi: 10.1088/0266-5611/25/3/035010
  20. 20.
    Moreau-Villeger V, Delingette H, Sermesant M, Ashikaga H, McVeigh ER, Ayache N (2006) Building maps of local apparent conductivity of the epicardium with a 2-D electrophysiological model of the heart. IEEE Trans Biomed Eng 53(8):1457–1466PubMedCrossRefGoogle Scholar
  21. 21.
    Pham DT (2001) Stochastic methods for sequential data assimilation in strongly nonlinear systems. J Mar Syst 129:1194–1207Google Scholar
  22. 22.
    Pham DT, Verron J, Roubaud MC (1998) A singular evolutive extended Kalman filter for data assimilation in oceanography. J Mar Syst 16(3–4):323–340CrossRefGoogle Scholar
  23. 23.
    Relan J, Chinchapatnam P, Sermesant M, Rhode K, Ginks M, Delingette H, Rinaldi CA, Razavi R, Ayache N (2011) Coupled personalization of cardiac electrophysiology models for prediction of ischaemic ventricular tachycardia. J R Soc Interf Focus 1(3):396–407CrossRefGoogle Scholar
  24. 24.
    Smith N, de Vecchi A, McCormick M, Nordsletten D, Camara O, Frangi AF, Delingette H, Sermesant M, Relan J, Ayache N, Krueger MW, Schulze WHW, Hose R, Valverde I, Beerbaum P, Staicu C, Siebes M, Spaan J, Hunter P, Weese J, Lehmann H, Chapelle D, Razavi R (2011) euHeart: personalized and integrated cardiac care using patient-specific cardiovascular modelling. Interf Focus 1(3):349–364CrossRefGoogle Scholar
  25. 25.
    Wang L, Zhang H, Wong KCL, Shi P (2009) A reduced-rank square root filtering framework for noninvasive functional imaging of volumetric cardiac electrical activity. In: IEEE International conference on acoustics, speech and signal processing. ICASSP 2009, pp 533–536Google Scholar
  26. 26.
    Xi J, Lamata L, Lee J, Moireau P, Chapelle D, Smith N. (2011) Myocardial transversely isotropic material parameter estimation from in-silico measurements based on a reduced-order unscented Kalman filter. J Mech Behav Biomed Mater 4(7):1090–1102PubMedCrossRefGoogle Scholar
  27. 27.
    Xi J, Lamata P, Shi W, Niederer S, Land S, Rueckert D, Duckett D, Shetty A, Rinaldi CA, Razavi R (2011) An automatic data assimilation framework for patient-specific myocardial mechanical parameter estimation. Funct Imaging Model Heart 392–400Google Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2012

Authors and Affiliations

  1. 1.InriaLe ChesnayFrance

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