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Personalisation of a 3D Ventricular Electrophysiological Model, Using Endocardial and Epicardial Contact Mapping and MRI

  • Jatin Relan
  • Maxime Sermesant
  • Hervé Delingette
  • Nicholas Ayache
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7085)

Abstract

Personalisation, i.e. parameter estimation of a cardiac ElectroPhysiology (EP) model is needed to build patient-specific models, which could then be used to understand and predict the complex dynamics involved in patient’s pathology. In this paper, we present an EP model personalisation approach applied to an infarcted porcine heart, using contact mapping data and Diffusion Tensor MRI. The contact mapping data was gathered during normal sinus rhythm, on the ventricles in-vivo, endocardially as well as epicardially, using a CARTO mapping system. The Diffusion Tensor MRI was then obtained ex-vivo, in order to have the true cardiac fibre orientations, for the infarcted heart. Both datasets were then used to build and personalise the 3D ventricular electrophysiological model, with the proposed personalisation approach. Secondly, the effect of using only endocardial mapping or epicardial mapping measurements, on the personalised EP model was also tested.

Keywords

Normal Sinus Rhythm Contact Mapping Cardiac Electrophysiology Epicardial Surface Extracellular Potential 
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.

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References

  1. 1.
    Fenton, F.H., Cherry, E.M.: Models of cardiac cell. Scholarpedia 3(8), 1868 (2008)CrossRefGoogle Scholar
  2. 2.
    Noble, D., Varghese, A., Kohl, P., Noble, P.: Improved guinea-pig ventricular cell model incorporating a diadic space, IKr and IKs, and length-and tension-dependent processes. The Canadian Journal of Cardiology 14(1), 123 (1998)Google Scholar
  3. 3.
    Ten Tusscher, K., Noble, D., Noble, P., Panfilov, A.: A model for human ventricular tissue. American Journal of Physiology-Heart and Circulatory Physiology 286(4), H1573 (2004)CrossRefGoogle Scholar
  4. 4.
    Bueno-Orovio, A., Cherry, E., Fenton, F.: Minimal model for human ventricular action potentials in tissue. Journal of Theoretical Biology 253(3), 544–560 (2008)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Fitzhugh, R.: Impulses and physiological states in theoretical models of nerve membrane. Biophysical Journal 1(6), 445–466 (1961)CrossRefGoogle Scholar
  6. 6.
    Aliev, R.R., Panfilov, A.V.: A simple two-variable model of cardiac excitation. Chaos, Solitons & Fractals 7(3), 293–301 (1996)CrossRefGoogle Scholar
  7. 7.
    Sermesant, M., Konukog̃lu, E., Delingette, H., Coudière, Y., Chinchapatnam, P., Rhode, K., Razavi, R., Ayache, N.: An Anisotropic Multi-front Fast Marching Method for Real-Time Simulation of Cardiac Electrophysiology. In: Sachse, F.B., Seemann, G. (eds.) FIHM 2007. LNCS, vol. 4466, pp. 160–169. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Chinchapatnam, P., Rhode, K., Ginks, M., Rinaldi, C., Lambiase, P., Razavi, R., Arridge, S., Sermesant, M.: Model-based imaging of cardiac apparent conductivity and local conduction velocity for diagnosis and planning of therapy. IEEE Transactions on Medical Imaging 27(11), 1631–1642 (2008)CrossRefGoogle Scholar
  9. 9.
    Lepiller, D., Sermesant, M., Pop, M., Delingette, H., Wright, G., Ayache, N.: Cardiac Electrophysiology Model Adjustment Using the Fusion of MR and Optical Imaging. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 678–685. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Relan, J., Pop, M., Delingette, H., Wright, G., Ayache, N., Sermesant, M.: Personalisation of a cardiac electrophysiology model using optical mapping and mri for prediction of changes with pacing. IEEE Transactions on Biomedical Engineering (2011)Google Scholar
  11. 11.
    Relan, J., Chinchapatnam, P., Sermesant, M., Rhode, K., Ginks, M., Delingette, H., Rinaldi, C., Razavi, R., Ayache, N.: Coupled personalization of cardiac electrophysiology models for prediction of ischaemic ventricular tachycardia. Interface Focus 1(3), 396 (2011)CrossRefGoogle Scholar
  12. 12.
    Mitchell, C., Schaeffer, D.: A two-current model for the dynamics of cardiac membrane. Bulletin of Mathematical Biology 65(5), 767–793 (2003)CrossRefzbMATHGoogle Scholar
  13. 13.
    Fenton, F., Karma, A.: Vortex dynamics in three-dimensional continuous myocardium with fiber rotation: filament instability and fibrillation. Chaos 8(1), 20–47 (1998)CrossRefzbMATHGoogle Scholar
  14. 14.
    Relan, J., Sermesant, M., Delingette, H., Pop, M., Wright, G., Ayache, N.: Quantitative comparison of two cardiac electrophysiology models using personalisation to optical and mr data. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009, pp. 1027–1030. IEEE (2009)Google Scholar
  15. 15.
    Konukoglu, E., Relan, J., Cilingir, U., Menze, B., Chinchapatnam, P., Jadidi, A., Cochet, H., Hocini, M., Delingette, H., Jaïs, P., Haïssaguerre, M., Ayache, N., Sermesant, M.: Efficient probabilistic model personalization integrating uncertainty on data and parameters: Application to eikonal-diffusion models in cardiac electrophysiology. Progress in Biophysics and Molecular Biology (accepted, 2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jatin Relan
    • 1
  • Maxime Sermesant
    • 1
  • Hervé Delingette
    • 1
  • Nicholas Ayache
    • 1
  1. 1.Inria, Asclepios ProjectSophia AntipolisFrance

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