Advertisement

Improving Efficiency of Data Assimilation Procedure for a Biomechanical Heart Model by Representing Surfaces as Currents

  • Alexandre Imperiale
  • Alexandre Routier
  • Stanley Durrleman
  • Philippe Moireau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7945)

Abstract

We adapt the formalism of currents to compare data surfaces and surfaces of a mechanical model and we use this discrepancy measure to feed a data assimilation procedure. We apply our methodology to perform parameter estimation in a biomechanical model of the heart using synthetic observations of the endo- and epicardium surfaces of an infarcted left ventricle. We compare this formalism with a more classical signed distance operator between surfaces and we numerically show that we have improved the efficiency of our estimation justifying the use of state-of-the-art computational geometry formalism in the data assimilation measurements processing.

Keywords

Data Assimilation Biomechanical Model Unscented Kalman Filter Observation Operator Medical Image Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chabiniok, R., Moireau, P., Lesault, A., Rahmouni, P.-F., Deux, J.-F., Chapelle, D.: Estimation of tissue contractility from cardiac cine-mri using a biomechanical heart model. In: Biomechanics and Modeling in Mechanobiology (2011)Google Scholar
  2. 2.
    Delfour, M.C., Zolésio, J.-P.: Shapes and geometries: metrics, analysis, differential calculs, and optimization. In: Advances in Design and Control. Society for Industrial and Applied Mathematics (2011)Google Scholar
  3. 3.
    Delingette, H., Billet, F., Wong, K.C.L., Sermesant, M., Rhode, K., Ginks, M., Rinaldi, C.A., Razavi, R., Ayache, N.: Personalization of Cardiac Motion and Contractility From Images Using Variational Data Assimilation. IEEE Trans. Biomed. Eng. 59(1), 20–24 (2012)CrossRefGoogle Scholar
  4. 4.
    Durrleman, S., Pennec, X., Trouvé, A., Ayache, A.: Statistical models on sets of curves and surfaces based on currents. Med. Image Anal. 13(5), 793–808 (2009)CrossRefGoogle Scholar
  5. 5.
    Glaunès, J., Qui, J., Miller, M.I., Younes, L.: Large deformation diffeomorphic metric curve mapping. Int. J. Comput. Vision 80(3), 317–336 (2008)CrossRefGoogle Scholar
  6. 6.
    Imperiale, A., Chabiniok, R., Moireau, P., Chapelle, D.: Constitutive parameter estimation methodology using tagged-MRI data. In: Metaxas, D.N., Axel, L. (eds.) FIMH 2011. LNCS, vol. 6666, pp. 409–417. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Liu, H., Shi, P.: Maximum a Posteriori Strategy for the Simultaneous Motion and Material Property Estimation of the Heart. IEEE Trans. Biomed. Eng. 56(2), 378–389 (2009)CrossRefGoogle Scholar
  8. 8.
    Marchesseau, S., Delingette, H., Sermesant, M., Ayache, N.: Fast parameter calibration of a cardiac electromechanical model from medical images based on the unscented transform. Biomecch. Model. Mechan. (2012)Google Scholar
  9. 9.
    Moireau, P., Chapelle, D.: Reduced-order unscented kalman filtering with application to parameter identification in large-dimensional systems. Control, Optimisation and Calculus of Variations 17, 380–405 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Moireau, P., Chapelle, D., Le Tallec, P.: Filtering for distributed mechanical dystems using position measurements: perspectives in medical imaging. Inverse Problems, 25 (2009)Google Scholar
  11. 11.
    Peters, J., Ecabert, O., Meyer, C., Schramm, H., Kneser, R., Groth, A., Weese, J.: Automatic whole segmentation in static magnetic resonance image volumes. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 402–410. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Sainte-Marie, J., Chapelle, D., Cimrman, R., Sorine, M.: Modeling and estimation of the cardiac electromechanical activity. Comput. Struct. 84, 1743–1759 (2006)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Sermesant, M., Forest, C., Pennec, X., Delingette, H., Ayache, N.: Deformable biomechanical models: application to 4D cardiac image analysis. Medical Image Analysis 7(4), 475–488 (2003)CrossRefGoogle Scholar
  14. 14.
    Vaillant, M., Glaunès, J.: Surface matching via currents. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 381–392. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Xi, J., Lamata, P., Lee, J., Moireau, P., Chapelle, D., Smith, N.P.: Myocardial transversely isotropic material parameter estimation from in-silico measurements based on a reduced-order unscented Kalman filter. Journal of the Mechanical Behavior of Biomedical Materials 4(7), 1090–1102 (2011)CrossRefGoogle Scholar
  16. 16.
    Xi, J., Lamata, P., Niederer, S., Land, S., Shi, W., Zhuang, X., Ourselin, S., Duckett, S.G., Shetty, A.K., Rinaldi, C.A., Rueckert, D., Razavi, R., Smith, N.P.: The estimation of patient-specific cardiac diastolic functions from clinical measurements. In: Medical Image Analysis, pp. 1–14 (2012)Google Scholar
  17. 17.
    Younes, L.: Shapes and diffeomorphisms. Springer (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alexandre Imperiale
    • 1
  • Alexandre Routier
    • 1
    • 2
  • Stanley Durrleman
    • 2
  • Philippe Moireau
    • 1
  1. 1.M∃DISIM TeamInria Ile-de-France SaclayPalaiseauFrance
  2. 2.ICM, Hôpital La Pitié-SalpêtrièreParisFrance

Personalised recommendations