Personalized Computational Models of the Heart for Cardiac Resynchronization Therapy



Cardiovascular diseases (CVD) are the major cause of morbidity and mortality in the western world. Within CVD, the increasing prevalence of congestive heart failure (CHF) is mainly caused by the steadily increasing number of heart attack survivors. They suffer an important scar burden on their cardiac function due to the infarction. Moreover, CHF has a terrible prognosis with 50% mortality in the first 3 years after diagnosis. Of all CHF patients, those with an additional dyssynchronous contraction have the worst prognosis. Cardiac resynchronization therapy (CRT) involves placing a pacemaker to improve the synchronicity of cardiac contraction. It has recently been shown to be an effective method of treating patients with dyssynchronous CHF, inducing significant reductions in morbidity and mortality in large clinical trials. However, clinical trials have also demonstrated that up to 30% of patients may be classified as nonresponders. There remains major controversy surrounding patient selection and optimization of this expensive treatment (e.g., lead positioning, pacemaker setting). For instance, recent studies showed that patients with heart failure and narrow QRS intervals do not currently benefit from CRT (RethinQ, [3]) and that no single echocardiographic measure of dyssynchrony may be recommended to improve patient selection (PROSPECT, [10]). Therefore, new approaches are needed in order to provide a better diagnosis and characterization of patients while achieving a better planning and delivery of the therapy.


Right Ventricle Cardiac Resynchronization Therapy Fiber Orientation Deformable Model Congestive Heart Failure Patient 



The authors would like to thank their co-workers in this project: R. Chabiniok, P. Chinchapatnam, T. Mansi, F. Billet, P. Moireau, J.M. Peyrat, K. Rhode, M. Ginks, P. Lambiase, S. Arridge, H. Delingette, M. Sorine, C.A. Rinaldi, D. Chapelle, and N. Ayache.


  1. 1.
    Aliev, R., Panfilov, A.: A simple two-variable model of cardiac excitation. Chaos, Solitons and Fractals 7(3), 293–301 (1996).CrossRefGoogle Scholar
  2. 2.
    Beeler, G.W., Reuter, H.: Reconstruction of the action potential of ventricular myocardial fibers. Journal of Physiology 268, 177–210 (1977).PubMedGoogle Scholar
  3. 3.
    Beshai, J.F., Grimm, R.A., Nagueh, S.F., Baker, J.H., Beau, S.L., Greenberg, S.M., Pires, L.A., Tchou, P.J.: Cardiac-resynchronization therapy in heart failure with narrow QRS complexes. The New England Journal of Medicine 357(24), 2461–2471 (2007).PubMedCrossRefGoogle Scholar
  4. 4.
    Bestel, J., Clément, F., Sorine, M.: A biomechanical model of muscle contraction. In: W. Niessen, M. Viergever (eds.) Medical Image Computing and Computer-Assisted intervention (MICCAI’01), Lecture Notes in Computer Science (LNCS), vol. 2208, pp. 1159–1161. Springer-Verlag, Berlin, Germany (2001).Google Scholar
  5. 5.
    Billet, F., Sermesant, M., Delingette, H., Ayache, N.: Cardiac Motion Recovery and Boundary Conditions Estimation by Coupling an Electromechanical Model and Cine-MRI Data. In: Proceedings of Functional Imaging and Modeling of the Heart 2009 (FIMH’09), Lecture Notes in Computer Science (LNCS), vol. 5528, pp. 376–385, Nice, France, 3–5 June 2009.Google Scholar
  6. 6.
    Caillerie, D., Mourad, A., Raoult, A.: Cell-to-muscle homogenization. Application to a constitutive law for the myocardium. Mathematical Modeling and Numerical Analysis 37(4), 681–698 (2003).CrossRefGoogle Scholar
  7. 7.
    Chapelle, D., Clément, F., Génot, F., Tallec, P.L., Sorine, M., Urquiza, J.: A physiologically-based model for the active cardiac muscle contraction. In: T. Katila, I. Magnin, P. Clarysse, J. Montagnat, J. Nenonen (eds.) Functional Imaging and Modeling of the Heart (FIMH’01), no. 2230 in Lecture Notes in Computer Science (LNCS), pp. 128–133. Springer, Berlin (2001).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).PubMedCrossRefGoogle Scholar
  9. 9.
    Chinchapatnam, P.P., Rhode, K.S., King, A., Gao, G., Ma, Y., Schaeffter, T., Hawkes, D., Razavi, R.S., Hill, D.L., Arridge, S., Sermesant, M.: Anisotropic wave propagation and apparent conductivity estimation in a fast electrophysiological model: application to XMR interventional imaging. Medical Image Computing and Computer-Assisted Intervention: International Conference on Medical Image Computing and Computer-Assisted Intervention, 10, 575–583 (2007).Google Scholar
  10. 10.
    Chung, E.S., Leon, A.R., Tavazzi, L., Sun, J.P., Nihoyannopoulos, P., Merlino, J., Abraham, W.T., Ghio, S., Leclercq, C., Bax, J.J., Yu, C.M., Gorcsan, J., St John Sutton, M., De Sutter, J., Murillo, J.: Results of the predictors of response to crt (PROSPECT) trial. Circulation 117(20), 2608–2616 (2008).PubMedCrossRefGoogle Scholar
  11. 11.
    Colli Franzone, P., Guerri, L., Rovida, S.: Wavefront propagation in activation model of the anisotropic cardiac tissue: Asymptotic analysis and numerical simulations. Journal of Mathematical Biology 28(2), 121–176 (1990).PubMedCrossRefGoogle Scholar
  12. 12.
    Crampin, E.J., Halstead, M., Hunter, P., Nielsen, P., Noble, D., Smith, N., Tawhai, M.: Computational physiology and the physiome project. Experimental Physiology 89(1), 1–26 (2004).PubMedCrossRefGoogle Scholar
  13. 13.
    Dou, J., Reese, T.G., Tseng, W.Y.I., Wedeen, V.J.: Cardiac Diffusion MRI without motion effects. Magnetic Resonance in Medicine 48(1), 105–114 (2002).PubMedCrossRefGoogle Scholar
  14. 14.
    Ecabert, O., Peters, J., Schramm, H., Lorenz, C., von Berg, J., Walker, M.J., Vembar, M., Olszewski, M.E., Subramanyan, K., Lavi, G., Weese, J.: Automatic model-based segmentation of the heart in ct images. IEEE Transactions on Medical Imaging 27(9), 1189–1201 (2008).PubMedCrossRefGoogle Scholar
  15. 15.
    Faris, O., Evans, F., Ennis, D., Helm, P., Taylor, J., Chesnick, A., Guttman, M., Ozturk, C., McVeigh, E.: Novel technique for cardiac electromechanical mapping with magnetic resonance imaging tagging and an epicardial electrode sock. Annals of Biomedical Engineering 31(4), 430–440 (2003).PubMedCrossRefGoogle Scholar
  16. 16.
    FitzHugh, R.: Impulses and physiological states in theoretical models of nerve membrane. Biophysical Journal 1, 445–466 (1961).PubMedCrossRefGoogle Scholar
  17. 17.
    Hill, A.: The heat of shortening and the dynamic constants in muscle. Proceedings of the Royal Society of London, Series B 126, 136–195 (1938).CrossRefGoogle Scholar
  18. 18.
    Hodgkin, A., Huxley, A.: A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology 177, 500–544 (1952).Google Scholar
  19. 19.
    Humphrey, J.: Cardiovascular Solid Mechanics. Springer, Berlin (2002).Google Scholar
  20. 20.
    Humphrey, J., Strumpf, R., Yin, F.: Determination of a constitutive relation for passive myocardium: I. A new functional form. ASME Journal of Biomechanical Engineering 112, 333–339 (1990).CrossRefGoogle Scholar
  21. 21.
    Hunter, P., McCulloch, A., Keurs, H.: Modeling the mechanical properties of cardiac muscle. Progress in Biophysics and Molecular Biology 69, 289–331 (1998).PubMedCrossRefGoogle Scholar
  22. 22.
    Hunter, P., Nash, M., Sands, G.: Computational Biology of the Heart, chap. 12: Computational Electromechanics of the Heart, pp. 345–407. John Wiley & Sons Ltd, West Sussex, UK (1997).Google Scholar
  23. 23.
    Hunter, P., Nielsen, P.: A strategy for integrative computational physiology. Physiology (Bethesda) 20, 316–325 (2005).CrossRefGoogle Scholar
  24. 24.
    Hunter, P., Pullan, A., Smaill, B.: Modeling total heart function. Annual Review of Biomedical Engineering 5, 147–177 (2003).PubMedCrossRefGoogle Scholar
  25. 25.
    Keener, J., Sneyd, J.: Mathematical Physiology. Springer, Berlin (1998).Google Scholar
  26. 26.
    Kerckhoffs, R.C., Lumens, J., Vernooy, K., Omens, J.H., Mulligan, L.J., Delhaas, T., Arts, T., McCulloch, A.D., Prinzen, F.W.: Cardiac resynchronization: Insight from experimental and computational models. Progress in Biophysics and Molecular Biology 97(2–3), 543–561 (2008).PubMedCrossRefGoogle Scholar
  27. 27.
    Kerckhoffs, R.C., McCulloch, A.D., Omens, J.H., Mulligan, L.J.: Effects of biventricular pacing and scar size in a computational model of the failing heart with left bundle branch block. Medical Image Analysis 13(2), 362–369 (2009).PubMedCrossRefGoogle Scholar
  28. 28.
    Kilner, P., Yang, G., Wilkes, A., Mohiaddin, R., Firmin, D., Yacoub, M.: Asymmetric redirection of flow through the heart. Nature 404, 759–761 (2000).PubMedCrossRefGoogle Scholar
  29. 29.
    Le Tallec, P.: Numerical methods for nonlinear three-dimensional elasticity. In: P. Ciarlet, J.L. Lions (eds.) Handbook of Numerical Analysis, vol. 3. Elsevier, North-Holland (1994).Google Scholar
  30. 30.
    Luo, C., Rudy, Y.: A model of the ventricular cardiac action potential: depolarization, repolarization, and their interaction. Circulation Research 68, 1501–1526 (1991).PubMedCrossRefGoogle Scholar
  31. 31.
    MacLeod, R., Yilmaz, B., Taccardi, B., Punske, B., Serinagaolu, Y., Brooks, D.: Direct and inverse methods for cardiac mapping using multielectrode catheter measurements. Journal of Biomedizinische Technik 46, 207–209 (2001).CrossRefGoogle Scholar
  32. 32.
    Masood, S., Yang, G., Pennell, D., Firmin, D.: Investigating intrinsic myocardial mechanics: the role of MR tagging, velocity phase mapping and diffusion imaging. Journal of Magnetic Resonance Imaging 12(6), 873–883 (2000).PubMedCrossRefGoogle Scholar
  33. 33.
    McCulloch, A., Bassingthwaighte, J., Hunter, P., Noble, D., Blundell, T., Pawson, T.: Computational biology of the heart: From structure to function. Progress in Biophysics and Molecular Biology 69(2/3), 151–559 (1998).Google Scholar
  34. 34.
    McInerney, T., Terzopoulos, D.: Deformable models in medical images analysis: a survey. Medical Image Analysis 1(2), 91–108 (1996).PubMedCrossRefGoogle Scholar
  35. 35.
    Moireau, P., Chapelle, D., Le Tallec, P.: Joint state and parameter estimation for distributed mechanical systems. Computer Methods in Applied Mechanics and Engineering 197, 659–677 (2008).CrossRefGoogle Scholar
  36. 36.
    Montagnat, J., Delingette, H.: 4D deformable models with temporal constraints: application to 4D cardiac image segmentation. Medical Image Analysis 9(1), 87–100 (2005).PubMedCrossRefGoogle Scholar
  37. 37.
    Moreau-Villéger, V., Delingette, H., Sermesant, M., Ashikaga, H., McVeigh, E., Ayache, N.: Building maps of local apparent conductivity of the epicardium with a 2D electrophysiological model of the heart. IEEE Transactions on Biomedical Engineering 53(8), 1457–1466 (2006).PubMedCrossRefGoogle Scholar
  38. 38.
    Nash, M.: Mechanics and material properties of the heart using an anatomically accurate mathematical model. Ph.D. thesis, University of Auckland (1998).Google Scholar
  39. 39.
    Nickerson, D., Nash, M., Nielsen, P., Smith, N., Hunter, P.: Computational multiscale modeling in the IUPS physiome project: modeling cardiac electromechanics. Systems Biology 50(6), 617–630 (2006).Google Scholar
  40. 40.
    Nielsen, P., Grice, I.L., Smail, B., Hunter, P.: Mathematical Model of Geometry and Fibrous Structure of the Heart. American Journal of Physiology -Heart and Circulatory Physiology 260(29), H1365–H1378 (1991).Google Scholar
  41. 41.
    Noble, D.: A modification of the Hodgkin–Huxley equations applicable to purkinje fiber action and pace-maker potentials. Journal of Physiology 160, 317–352 (1962).PubMedGoogle Scholar
  42. 42.
    Noble, D.: Modeling the heart. Physiology 19, 191–197 (2004).PubMedCrossRefGoogle Scholar
  43. 43.
    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. Canadian Journal of Cardiology 14, 123–134 (1998).PubMedGoogle Scholar
  44. 44.
    Park, J., Metaxas, D., Axel, L.: Analysis of left ventricular wall motion based on volumetric deformable models and MRI-SPAMM. Medical Image Analysis 1, 53–71 (1996).PubMedCrossRefGoogle Scholar
  45. 45.
    Peyrat, J.M., Sermesant, M., Pennec, X., Delingette, H., Xu, C., McVeigh, E.R., Ayache, N.: A computational framework for the statistical analysis of cardiac diffusion tensors: Application to a small database of canine hearts. IEEE Transactions on Medical Imaging 26(11), 1500–1514 (2007). doi: 10.1109/TMI.2007.907286.PubMedCrossRefGoogle Scholar
  46. 46.
    Peyrat, J.M., Sermesant, M., Pennec, X., Delingette, H., Xu, C., McVeigh, E.R., Ayache, N.: Statistical Comparison of Cardiac Fiber Architectures. In: Proceedings of the 4th International Conference on Functional Imaging and Modeling of the Heart (FIMH’07), vol. 4466 of LNCS, pp. 413–423 (2007).Google Scholar
  47. 47.
    Pollard, A., Hooke, N., Henriquez, C.: Cardiac propagation simulation. Critical Reviews in Biomedical Engineering 20(3,4), 171–210 (1992).PubMedGoogle Scholar
  48. 48.
    Rhode, K., Sermesant, M., Brogan, D., Hegde, S., Hipwell, J., Lambiase, P., Rosenthal, E., Bucknall, C., Qureshi, S., Gill, J., Razavi, R., Hill, D.: A system for real-time XMR guided cardiovascular intervention. IEEE Transactions on Medical Imaging 24(11), 1428–1440 (2005).PubMedCrossRefGoogle Scholar
  49. 49.
    Sachse, F.B.: Computational Cardiology, Modeling of Anatomy, Electrophysiology, and Mechanics, Lecture Notes in Computer Science, vol. 2966. Springer, Berlin (2004).Google Scholar
  50. 50.
    Sainte-Marie, J., Chapelle, D., Cimrman, R., Sorine, M.: Modeling and estimation of the cardiac electromechanical activity. Computers and Structures 84, 1743–1759 (2006).CrossRefGoogle Scholar
  51. 51.
    Sermesant, M., Delingette, H., Ayache, N.: An electromechanical model of the heart for image analysis and simulation. IEEE Transactions in Medical Imaging 25(5), 612–625 (2006).CrossRefGoogle Scholar
  52. 52.
    Sermesant, M., Konukoglu, E., Delingette, H., Coudiere, Y., Chinchaptanam, P., Rhode, K., Razavi, R., Ayache, N.: An anisotropic multi-front fast marching method for real-time simulation of cardiac electrophysiology. In: Proceedings of Functional Imaging and Modeling of the Heart 2007 (FIMH’07), LNCS, vol. 4466, pp. 160–169 (2007).Google Scholar
  53. 53.
    Sermesant, M., Moireau, P., Camara, O., Sainte-Marie, J., Andriantsimiavona, R., Cimrman, R., Hill, D.L., Chapelle, D., Razavi, R.: Cardiac function estimation from MRI using a heart model and data assimilation: advances and difficulties. Medical Image Analysis 10(4), 642–656 (2006).PubMedCrossRefGoogle Scholar
  54. 54.
    Sermesant M, Billet F, Chabiniok R, Mansi T, Chinchapatnam P, Moireau P, Peyrat JM, Rhode K, Ginks M, Lambiase P, Arridge S, Delingette H, Sorine M, Rinaldi A, Chapelle D, Razavi R, Ayache N. Personalised Electromechanical Model of the Heart for the Prediction of the Acute Effects of Cardiac Resynchronisation Therapy. In: Proceedings of Functional Imaging and Modeling of the Heart 2009 (FIMH’09), LCNS, vol. 5528, pp. 239–248 (2009).Google Scholar
  55. 55.
    Smith, N., Nickerson, D., Crampin, E., Hunter, P.: Computational mechanics of the heart from tissue structure to ventricular function. Journal of Elasticity 61(1), 113–141 (2000).CrossRefGoogle Scholar
  56. 56.
    Stergiopulos, N., Westerhof, B., Westerhof, N.: Total arterial inertance as the fourth element of the windkessel model. American Journal of Physiology 276, H81–H88 (1999).PubMedGoogle Scholar
  57. 57.
    Streeter, D.: Gross Morphology and Fiber Geometry of the Heart. In: R. Berne (ed.) Handbook of Physiology, chap. The Cardiovascular System, Williams & Wilkins, Baltimore (1979).Google Scholar
  58. 58.
    Sutton, M.S., Keane, M.G.: Reverse remodeling in heart failure with cardiac resynchronization therapy. Heart 93(2), 167–171 (2007). doi: 10.1136/hrt.2005.067967.PubMedCrossRefGoogle Scholar
  59. 59.
    Ten Tusscher, K., Noble, D., Noble, P., Panfilov, A.: A model of the human ventricular myocyte. American Journal of Physiology -Heart and Circulatory Physiology 286(4), 1573–1589 (2004).CrossRefGoogle Scholar
  60. 60.
    Tomlinson, K.: Finite element solution of an eikonal equation for excitation wavefront propagation in ventricular myocardium. Ph.D. thesis, University of Auckland (2000).Google Scholar
  61. 61.
    Toussaint, N., Mansi, T., Delingette, H., Ayache, N., Sermesant, M.: An Integrated Platform for Dynamic Cardiac Simulation and Image Processing: Application to Personalized Tetralogy of Fallot Simulation. In: Proceedings of the Eurographics Workshop on Visual Computing for Biomedicine (VCBM). Delft, The Netherlands (2008).Google Scholar
  62. 62.
    Turk, G., O’Brien, J.: Variational implicit surfaces. Tech. rep., Georgia Institute of Technology (1999).Google Scholar
  63. 63.
    Xia, L., Huo, M.: Analysis of ventricular wall motion based on an electromechanical biventricular model. In: A. Murray (ed.) Computers in Cardiology, pp. 315–318. IEEE, New York NY, USA (2003).Google Scholar

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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Asclepios Team INRIASophia AntipolisFrance

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