Biomechanics and Modeling in Mechanobiology

, Volume 10, Issue 3, pp 295–306 | Cite as

Models of cardiac electromechanics based on individual hearts imaging data

Image-based electromechanical models of the heart
  • Viatcheslav Gurev
  • Ted Lee
  • Jason Constantino
  • Hermenegild Arevalo
  • Natalia A. Trayanova
Original Paper


Current multi-scale computational models of ventricular electromechanics describe the full process of cardiac contraction on both the micro- and macro- scales including: the depolarization of cardiac cells, the release of calcium from intracellular stores, tension generation by cardiac myofilaments, and mechanical contraction of the whole heart. Such models are used to reveal basic mechanisms of cardiac contraction as well as the mechanisms of cardiac dysfunction in disease conditions. In this paper, we present a methodology to construct finite element electromechanical models of ventricular contraction with anatomically accurate ventricular geometry based on magnetic resonance and diffusion tensor magnetic resonance imaging of the heart. The electromechanical model couples detailed representations of the cardiac cell membrane, cardiac myofilament dynamics, electrical impulse propagation, ventricular contraction, and circulation to simulate the electrical and mechanical activity of the ventricles. The utility of the model is demonstrated in an example simulation of contraction during sinus rhythm using a model of the normal canine ventricles.


Ventricular contraction Computational modeling Image-based models Cardiac pump 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Arsigny V, Fillard P, Pennec X, Ayache N (2006) Log-Euclidean metrics for fast and simple calculus on diffusion tensors. Magn Reson Med 56: 411–421CrossRefGoogle Scholar
  2. Arts T, Reneman R, Veenstra P (1979) A model of the mechanics of the left ventricle. Ann Biomed Eng 7: 299–318CrossRefGoogle Scholar
  3. Ashikaga H, Coppola B, Hopenfeld B, Leifer E, McVeigh E, Omens J (2007) Transmural dispersion of myofiber mechanics: implications for electrical heterogeneity in vivo. J Am Coll Cardiol 49: 909–916CrossRefGoogle Scholar
  4. Ashikaga H, Omens JH, Ingels NB, Covell JW (2004) Transmural mechanics at left ventricular epicardial pacing site. Am J Physiol Heart Circ Physiol 286: H2401–H2407CrossRefGoogle Scholar
  5. Balay S, Buschelman K, Eijkhout V, Cropp W, Kaushik D, Knepley M, McInnes LC, Smith B, Zhang H (2007) PETSc User manual, vol ANL-95/11-Revision 2.3.3. Argonne National Laboratory, ArgonneGoogle Scholar
  6. Bovendeerd PHM, Arts T, Huyghe JM, van Campen DH, Reneman RS (1992) Dependence of local left ventricular wall mechanics on myocardial fiber orientation: a model study. J Biomech 25: 1129–1140CrossRefGoogle Scholar
  7. Campbell S, Howard E, Aguado-Sierra J, Coppola B, Omens J, Mulligan L, McCulloch A, Kerckhoffs R (2009) Effect of transmurally heterogeneous myocyte excitation-contraction coupling on canine left ventricular electromechanics. Exp Phys 94: 541–552CrossRefGoogle Scholar
  8. Chen J, Song S-K, Liu W, McLean M, Allen S, Tan J, Wickline S, Yu X (2003) Remodeling of cardiac fiber structure after infarction in rats quantified with diffusion tensor MRI. Am J Physiol Heart Circ Physiol 285: H946–H954Google Scholar
  9. Cohen S, Hindmarsh C (1996) Cvode, a stiff/nonstiff Ode solver in C. Comput Phys 10: 138–143Google Scholar
  10. Durrer D, van Dam RT, Freud GE, Janse MJ, Meijler FL, Arzbaecher RC (1970) Total excitation of the isolated human heart. Circulation 41: 899–912Google Scholar
  11. Feit TS (1979) Diastolic pressure-volume relations and distribution of pressure and fiber extension across the wall of a model left ventricle. Biophys J 28: 143–166CrossRefGoogle Scholar
  12. Fernandez JW, Mithraratne P, Thrupp SF, Tawhai MH, Hunter PJ (2004) Anatomically based geometric modelling of the musculo-skeletal system and other organs. Biomech Model Mechanobiol 2: 139–155CrossRefGoogle Scholar
  13. Greenstein J, Hinch R, Winslow R (2006) Mechanisms of excitation-contraction coupling in an integrative model of the cardiac ventricular myocyte. Biophys J 90: 77–91CrossRefGoogle Scholar
  14. Guccione J, Costa K, McCulloch A (1995) Finite element stress analysis of left ventricular mechanics in the beating dog heart. J Biomech 28: 1167–1177CrossRefGoogle Scholar
  15. Helm P, Beg MF, Miller M, Winslow R (2005) Measuring and mapping cardiac fiber and laminar architecture using diffusion tensor MR imaging. Ann NY Acad Sci 1047: 296–307CrossRefGoogle Scholar
  16. Helm P, Tseng H-J, Younes L, McVeigh E, Winslow R (2005) Ex vivo 3D diffusion tensor imaging and quantification of cardiac laminar structure. Magn Reson Med 54: 850–859CrossRefGoogle Scholar
  17. Helm P, Winslow R, McVeigh E (2004) DTMRI data sets.
  18. Helm PA, Younes L, Beg MF, Ennis DB, Leclercq C, Faris OP, McVeigh E, Kass D, Miller MI, Winslow RL (2006) Evidence of structural remodeling in the dyssynchronous failing heart. Circ Res 98: 125–132CrossRefGoogle Scholar
  19. Hsu EW, Muzikant AL, Matulevicius SA, Penland RC, Henriquez CS (1998) Magnetic resonance myocardial fiber-orientation mapping with direct histological correlation. Am J Physiol 274: H1627–H1634Google Scholar
  20. Janz R, Grimm A (1972) Finite-element model for the mechanical behavior of the left ventricle: predictioni of deformation in the potassium-arrested rat heart. Circ Res 30: 244–252Google Scholar
  21. Kerckhoffs Roy, Neal Maxwell, Gu Quan, Bassingthwaighte James, Omens Jeff, McCulloch Andrew (2007) Coupling of a 3D finite element model of cardiac ventricular mechanics to lumped systems models of the systemic and pulmonic circulation. Ann Biomed Eng 35: 1–18CrossRefGoogle Scholar
  22. Kerckhoffs RC, Bovendeerd PH, Kotte JC, Prinzen FW, Smits K, Arts T (2003) Homogeneity of cardiac contraction despite physiological asynchrony of depolarization: a model study. Ann Biomed Eng 31: 536–547CrossRefGoogle Scholar
  23. Kerckhoffs RCP, Healy SN, Usyk TP, McCulloch AD (2006) Computational methods for cardiac electromechanics. Proc IEEE 94: 769–783CrossRefGoogle Scholar
  24. Nielsen PM, Le Grice IJ, Smaill BH, Hunter PJ (1991) Mathematical model of geometry and fibrous structure of the heart. Am J Physiol Heart Circ Physiol 260: H1365–H1378Google Scholar
  25. Onate E, Rojek J, Taylor R, Zienkiewicz O (2004) Finite calculus formulation for incompressible solids using linear triangles and tetrahedra. Int J Numer Meth Eng 59: 1473–1500MathSciNetzbMATHCrossRefGoogle Scholar
  26. Plank G, Zhou L, Greenstein JL, Cortassa S, Winslow RL, O’Rourke B, Trayanova NA (2008) From mitochondrial ion channels to arrhythmias in the heart: computational techniques to bridge the spatio-temporal scales. Phil Trans R Soc A 366: 3381–3409MathSciNetCrossRefGoogle Scholar
  27. Prassl AJ, Kickinger F, Ahammer H, Grau V, Schneider JE, Hofer E, Vigmond EJ, Trayanova NA, Plank G (2009) Automatically generated, anatomically accurate meshes for cardiac electrophysiology problems. IEEE Trans Biomed Eng 56: 1318–1330CrossRefGoogle Scholar
  28. Provost J, Lee WN, Fujikura K, Konofagou EE (2010) Electromechanical wave imaging of normal and ischemic hearts in vivo. IEEE Trans Med Imaging 29: 625–635CrossRefGoogle Scholar
  29. Provost J, Gurev V, Konofagou EE, Trayanova NA (In Review) Electromechanical wave imaging for mapping of cardiac electrical activation: a simulation-experimental assessment of the methodology. Heart RhythmGoogle Scholar
  30. Rice JJ, Wang F, Bers DM, de Tombe PP (2008) Approximate model of cooperative activation and crossbridge cycling in cardiac muscle using ordinary differential equations. Biophys J 95: 2368–2390CrossRefGoogle Scholar
  31. Roberts DE, Scher AM (1982) Effect of tissue anisotropy on extracellular potential fields in canine myocardium in situ. Circ Res 50: 342–351Google Scholar
  32. Scollan DF, Holmes A, Winslow R, Forder J (1998) Histological validation of myocardial microstructure obtained from diffusion tensor magnetic resonance imaging. Am J Physiol 275: H2308–H2318Google Scholar
  33. Spach MS, Barr RC (1975) Ventricular intramural and epicardial potential distributions during ventricular activation and repolarization in the intact dog. Circ Res 37: 243–257Google Scholar
  34. Stevens C, Remme E, LeGrice I, Hunter P (2003) Ventricular mechanics in diastole: material parameter sensitivity. J Biomech 36: 737–748CrossRefGoogle Scholar
  35. Usyk T, Legrice I, McCulloch A (2002) Computational model of three-dimensional cardiac electromechanics. Comput Vis Sci 4: 249–257zbMATHCrossRefGoogle Scholar
  36. Vadakkumpadan F, Arevalo H, Prassl A, Chen J, Kickinger F, Plank G, Trayanov N (2009a) Image-based models of cardiac structure in health and disease WIREs. Syst Biol and Med. (in press)Google Scholar
  37. Vadakkumpadan F, Rantner L, Tice B, Boyle P, Prassl A, Vigmond E, Plank G, Trayanova N (2009) Image-based models of cardiac structure with applications in arrhythmia and defibrillation studies. J Electriocardiol 42: 157.e110–157.e151Google Scholar
  38. Vetter F, McCulloch A (2000) Three-dimensional stress and strain in passive rabbit left ventricle: a model study. Ann Biomed Eng 28: 781–792CrossRefGoogle Scholar
  39. Vetter FJ, McCulloch AD (1998) Three-dimensional analysis of regional cardiac function: a model of rabbit ventricular anatomy. Prog Biophys Mol Biol 69: 157–183CrossRefGoogle Scholar
  40. Wenk J, Jhun C-S, Zhang Z, Sun K, Burger M, Einstein D, Ratcliffe M, Guccione J, Guccione J, Kassab G, Ratcliffe M (2010). In vivo left ventricular geometry and boundary conditions. In: Computational cardiovascular mechanics. Springer, US, pp 3–21Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Viatcheslav Gurev
    • 1
  • Ted Lee
    • 1
  • Jason Constantino
    • 1
  • Hermenegild Arevalo
    • 1
  • Natalia A. Trayanova
    • 1
  1. 1.Institute for Computational Medicine, Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreUSA

Personalised recommendations