Annals of Biomedical Engineering

, Volume 44, Issue 11, pp 3346–3358 | Cite as

Image-Based Simulations Show Important Flow Fluctuations in a Normal Left Ventricle: What Could be the Implications?

  • C. ChnafaEmail author
  • S. Mendez
  • F. Nicoud


Intra-cardiac flow has been explored for decades but there is still no consensus on whether or not healthy left ventricles (LV) may harbour turbulent-like flow despite its potential physiological and clinical relevance. The purpose of this study is to elucidate if a healthy LV could harbour flow instabilities, using image-based computational fluid dynamics (CFD). 35 cardiac cycles were simulated in a patient-specific left heart model obtained from cardiovascular magnetic resonance (CMR). The model includes the valves, atrium, ventricle, papillary muscles and ascending aorta. We computed phase-averaged flow patterns, fluctuating kinetic energy (FKE) and associated frequency components. The LV harbours disturbed flow during diastole with cycle-to-cycle variations. However, phase-averaged velocity fields much resemble those of CMR measurements and usually reported CFD results. The peak FKE value occurs during the E wave deceleration and reaches 25% of the maximum phase-averaged flow kinetic energy. Highest FKE values are predominantly located in the basal region and their frequency content reach more than 200 Hz. This study suggests that high-frequency flow fluctuations in normal LV may be common, implying deficiencies in the hypothesis usually made when computing cardiac flows and highlighting biases when deriving quantities from velocity fields measured with CMR.


Left heart Turbulence LES Turbulent kinetic energy Atrium Third sound 



The authors would like to express their gratitude to MD Dr. D. Coisne for many fruitful discussions. Dr. R. Moreno from the Rangueil University Hospital, Toulouse (France) is acknowledged for the CMR exams. Dr. V. Moureau and Dr. G. Lartigue from the CORIA lab, and the SUCCESS scientific group are acknowledged for providing the YALES2 code, which served as a basis for the development of YALES2BIO. This work was performed using HPC resources from GENCI-CINES (Grants 2014- and 2015-c2014037194).

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  1. 1.
    Barré, D., M. Kraushaar, G. Staffelbach, V. Moureau, and L. Y. M. Gicquel. Compressible and low Mach number LES of a swirl experimental burner. Comptes Rendus Mécanique 341:277–287, 2013.CrossRefGoogle Scholar
  2. 2.
    Baya Toda, H., O. Cabrit, K. Truffin, G. Bruneaux, and F. Nicoud. Assessment of subgrid-scale models with a large-eddy simulation-dedicated experimental database: the pulsatile impinging jet in turbulent cross-flow. Phys. Fluids 26:075108, 2014.CrossRefGoogle Scholar
  3. 3.
    Carlsson, M., E. Heiberg, J. Toger, and H. Arheden. Quantification of left and right ventricular kinetic energy using four-dimensional intracardiac magnetic resonance imaging flow measurements. AJP Hear. Circ. Physiol. 302:H893–H900, 2012.CrossRefGoogle Scholar
  4. 4.
    Celik, I. B., Z. N. Cehreli, and I. Yavuz. Index of resolution quality for large eddy simulations. J. Fluids Eng. 127:949, 2005.CrossRefGoogle Scholar
  5. 5.
    Charonko, J. J., R. Kumar, K. Stewart, W. C. Little, and P. P. Vlachos. Vortices formed on the mitral valve tips aid normal left ventricular filling. Ann. Biomed. Eng. 41:1049–1061, 2013.CrossRefPubMedGoogle Scholar
  6. 6.
    Cheng, C. P., D. Parker, and C. A. Taylor. Quantification of Wall shear stress in large blood vessels using Lagrangian interpolation functions with cine phase-contrast magnetic resonance imaging. Ann. Biomed. Eng. 30:1020–1032, 2002.CrossRefPubMedGoogle Scholar
  7. 7.
    Chien, S. Shear dependence of effective cell volume as a determinant of blood viscosity. Science (80-) 168:977–979, 1970.CrossRefGoogle Scholar
  8. 8.
    Chnafa, C. Using image-based large-eddy simulations to investigate the intracardiac flow and its turbulent nature. Montpellier: University of Montpellier, 2014.Google Scholar
  9. 9.
    Chnafa, C., S. Mendez, R. Moreno, and F. Nicoud. Using image-based CFD to investigate the intracardiac turbulence. In: Modeling the Heart and the Circulatory System, edited by A. Quarteroni. New-York: Springer, 2015, pp. 97–117.Google Scholar
  10. 10.
    Chnafa, C., S. Mendez, and F. Nicoud. Image-based large-eddy simulation in a realistic left heart. Comput. Fluids 94:173–187, 2014.CrossRefGoogle Scholar
  11. 11.
    Collins, S. P., P. Arand, C. J. Lindsell, W. F. Peacock, and A. B. Storrow. Prevalence of the third and fourth heart sound in asymptomatic adults. Congest. Hear. Fail. 11:242–247, 2005.CrossRefGoogle Scholar
  12. 12.
    Davies, P. F., A. Remuzzi, E. J. Gordon, C. F. Dewey, and M. A. Gimbrone. Turbulent fluid shear stress induces vascular endothelial cell turnover in vitro. Proc. Natl. Acad. Sci. USA 83:2114–2117, 1986.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Domenichini, F., G. Pedrizzetti, and B. Baccani. Three-dimensional filling flow into a model left ventricle. J. Fluid Mech. 539:179, 2005.CrossRefGoogle Scholar
  14. 14.
    Domenichini, F., G. Querzoli, A. Cenedese, and G. Pedrizzetti. Combined experimental and numerical analysis of the flow structure into the left ventricle. J. Biomech. 40:1988–1994, 2007.CrossRefPubMedGoogle Scholar
  15. 15.
    Dyverfeldt, P., M. Bissell, A. J. Barker, A. F. Bolger, C.-J. Carlhäll, T. Ebbers, C. J. Francios, A. Frydrychowicz, J. Geiger, D. Giese, M. D. Hope, P. J. Kilner, S. Kozerke, S. Myerson, S. Neubauer, O. Wieben, and M. Markl. 4D flow cardiovascular magnetic resonance consensus statement. J. Cardiovasc. Magn. Reson. 17:72, 2015.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Dyverfeldt, P., M. D. Hope, E. E. Tseng, and D. Saloner. Magnetic resonance measurement of turbulent kinetic energy for the estimation of irreversible pressure loss in aortic stenosis. JACC Cardiovasc. Imaging 6:64–71, 2013.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Dyverfeldt, P., J.-P. E. Kvitting, C. J. Carlhäll, G. Boano, A. Sigfridsson, U. Hermansson, A. F. Bolger, J. Engvall, and T. Ebbers. Hemodynamic aspects of mitral regurgitation assessed by generalized phase-contrast MRI. J. Magn. Reson. Imaging 33:582–588, 2011.CrossRefPubMedGoogle Scholar
  18. 18.
    Dyverfeldt, P., J. P. E. Kvitting, A. Sigfridsson, J. Engvall, A. F. Bolger, and T. Ebbers. Assessment of fluctuating velocities in disturbed cardiovascular blood flow: in vivo feasibility of generalized phase-contrast MRI. J. Magn. Reson. Imaging 28:655–663, 2008.CrossRefPubMedGoogle Scholar
  19. 19.
    Falahatpisheh, A., and A. Kheradvar. High-speed particle image velocimetry to assess cardiac fluid dynamics in vitro: from performance to validation. Eur. J. Mech. B/Fluids 35:2–8, 2012.CrossRefGoogle Scholar
  20. 20.
    Glower, D. D., R. L. Murrah, C. O. Olsen, J. W. Davis, and J. S. Rankin. Mechanical correlates of the third heart sound. J. Am. Coll. Cardiol. 19:450–457, 1992.CrossRefPubMedGoogle Scholar
  21. 21.
    Hendabadi, S., J. Bermejo, Y. Benito, R. Yotti, F. Fernández-Avilés, J. C. Del Álamo, and S. C. Shadden. Topology of blood transport in the human left ventricle by novel processing of doppler echocardiography. Ann. Biomed. Eng. 41:2603–2616, 2013.CrossRefPubMedGoogle Scholar
  22. 22.
    Hult, P., T. Fjällbrant, B. Wranne, and P. Ask. Detection of the third heart sound using a tailored wavelet approach. Med. Biol. Eng. Comput. 42:253–258, 2004.CrossRefPubMedGoogle Scholar
  23. 23.
    Kanski, M., P. M. Arvidsson, J. Töger, R. Borgquist, E. Heiberg, M. Carlsson, and H. Arheden. Left ventricular fluid kinetic energy time curves in heart failure from cardiovascular magnetic resonance 4D flow data. J. Cardiovasc. Magn. Reson. 17:111, 2015.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Khalafvand, S. S., E. Y. K. Ng, L. Zhong, and T. K. Hung. Fluid-dynamics modelling of the human left ventricle with dynamic mesh for normal and myocardial infarction: preliminary study. Comput. Biol. Med. 42:863–870, 2012.CrossRefPubMedGoogle Scholar
  25. 25.
    Kheradvar, A., and M. Gharib. On mitral valve dynamics and its connection to early diastolic flow. Ann. Biomed. Eng. 37:1–13, 2009.CrossRefPubMedGoogle Scholar
  26. 26.
    Kilner, P. J., G. Z. Yang, A. J. Wilkes, R. H. Mohiaddin, D. N. Firmin, and M. H. Yacoub. Asymmetric redirection of flow through the heart. Nature 404:759–761, 2000.CrossRefPubMedGoogle Scholar
  27. 27.
    Kono, T., H. Rosman, M. Alam, P. D. Stein, H. N. Sabbah, D. Stein, and N. Wbbah. Hemodynamic correlates of the third heart sound during the evolution of chronic heart failure. Am. J. Med. 21:419–423, 1992.Google Scholar
  28. 28.
    Le, T. B., and F. Sotiropoulos. On the three-dimensional vortical structure of early diastolic flow in a patient-specific left ventricle. Eur. J. Mech. B/Fluids 35:20–24, 2012.CrossRefPubMedGoogle Scholar
  29. 29.
    Long, Q., R. Merrifield, X. Y. Xu, P. Kilner, D. N. Firmin, and G.-Z. Yang. Subject-specific computational simulation of left ventricular flow based on magnetic resonance imaging. Proc. Inst. Mech. Eng. H 222:475–485, 2008.CrossRefPubMedGoogle Scholar
  30. 30.
    Lu, P. C., H. C. Lai, and J. S. Liu. A reevaluation and discussion on the threshold limit for hemolysis in a turbulent shear flow. J. Biomech. 34:1361–1364, 2001.CrossRefPubMedGoogle Scholar
  31. 31.
    Mann, D. L., D. P. Zipes, P. Libby, and R. O. Bonow. Braunwald’s Heart Disease: A Textbook of Cardiovascular Medicine. Philadelphia: Elsevier, p. 2136, 2014.Google Scholar
  32. 32.
    Markl, M., P. J. Kilner, and T. Ebbers. Comprehensive 4D velocity mapping of the heart and great vessels by cardiovascular magnetic resonance. J. Cardiovasc. Magn. Reson. 13:7, 2011.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Mendez, S., E. Gibaud, and F. Nicoud. An unstructured solver for simulations of deformable particles in flows at arbitrary Reynolds numbers. J. Comput. Phys. 256:465–483, 2014.CrossRefGoogle Scholar
  34. 34.
    Mihalef, V., R. I. Ionasec, P. Sharma, B. Georgescu, I. Voigt, M. Suehling, and D. Comaniciu. Patient-specific modelling of whole heart anatomy, dynamics and haemodynamics from four-dimensional cardiac CT images. Interface Focus 1:286–296, 2011.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Nicoud, F., H. B. Toda, O. Cabrit, S. Bose, and J. Lee. Using singular values to build a subgrid-scale model for large eddy simulations. Phys. Fluids 23:1–35, 2011.CrossRefGoogle Scholar
  36. 36.
    Olesen, S. P., D. E. Clapham, and P. F. Davies. Haemodynamic shear stress activates a K+ current in vascular endothelial cells. Nature 331:168–170, 1988.CrossRefPubMedGoogle Scholar
  37. 37.
    Pasipoularides, A. Diastolic filling vortex forces and cardiac adaptations: probing the epigenetic nexus. Hell. J. Cardiol. 53:458–469, 2012.Google Scholar
  38. 38.
    Pasipoularides, A. Mechanotransduction mechanisms for intraventricular diastolic vortex forces and myocardial deformations: part 1. J. Cardiovasc. Transl. Res. 8:76–87, 2015.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Pedrizzetti, G., and F. Domenichini. Left ventricular fluid mechanics: the long way from theoretical models to clinical applications. Ann. Biomed. Eng. 43:26–40, 2015.CrossRefPubMedGoogle Scholar
  40. 40.
    Pedrizzetti, G., F. Domenichini, and G. Tonti. On the left ventricular vortex reversal after mitral valve replacement. Ann. Biomed. Eng. 38:769–773, 2010.CrossRefPubMedGoogle Scholar
  41. 41.
    Pedrizzetti, G., G. La Canna, O. Alfieri, and G. Tonti. The vortex—an early predictor of cardiovascular outcome? Nat. Rev. Cardiol. 11:545–553, 2014.CrossRefPubMedGoogle Scholar
  42. 42.
    Pham, D. L., C. Xu, and J. L. Prince. Current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2:315–337, 2000.CrossRefPubMedGoogle Scholar
  43. 43.
    Pope, S. B. Turbulent Flows. Cambridge: Cambridge University Press, 2000. doi: 10.1088/0957-0233/12/11/705.CrossRefGoogle Scholar
  44. 44.
    Pope, S. B. Ten questions concerning the large-eddy simulation of turbulent flows. N. J. Phys. 6:35, 2004.CrossRefGoogle Scholar
  45. 45.
    Querzoli, G., S. Fortini, and A. Cenedese. Effect of the prosthetic mitral valve on vortex dynamics and turbulence of the left ventricular flow. Phys. Fluids 22:1–10, 2010.CrossRefGoogle Scholar
  46. 46.
    Sabbah, H. N., and P. D. Stein. Turbulent blood flow in humans: its primary role in the production of ejection murmurs. Circ. Res. 38:513–525, 1976.CrossRefPubMedGoogle Scholar
  47. 47.
    Saber, N. R., N. B. Wood, A. D. Gosman, R. D. Merrifield, G. Z. Yang, C. L. Charrier, P. D. Gatehouse, and D. N. Firmin. Progress towards patient-specific computational flow modeling of the left heart via combination of magnetic resonance imaging with computational fluid dynamics. Ann. Biomed. Eng. 31:42–52, 2003.CrossRefPubMedGoogle Scholar
  48. 48.
    Schenkel, T., M. Malve, M. Reik, M. Markl, B. Jung, and H. Oertel. MRI-Based CFD analysis of flow in a human left ventricle: methodology and application to a healthy heart. Ann. Biomed. Eng. 37:503–515, 2009.CrossRefPubMedGoogle Scholar
  49. 49.
    Töger, J., M. Kanski, M. Carlsson, S. J. Kovács, G. Söderlind, H. Arheden, and E. Heiberg. Vortex ring formation in the left ventricle of the heart: analysis by 4D Flow MRI and Lagrangian Coherent Structures. Ann. Biomed. Eng. 2012. doi: 10.1007/s10439-012-0615-3.PubMedGoogle Scholar
  50. 50.
    Valen-Sendstad, K., and D. A. Steinman. Mind the gap: impact of computational fluid dynamics solution strategy on prediction of intracranial aneurysm hemodynamics and rupture status indicators. Am. J. Neuroradiol. 35:536–543, 2014.CrossRefPubMedGoogle Scholar
  51. 51.
    Vedula, V., J.-H. Seo, A. C. Lardo, and R. Mittal. Effect of trabeculae and papillary muscles on the hemodynamics of the left ventricle. Theor. Comput. Fluid Dyn. 2015. doi: 10.1007/s00162-015-0349-6.Google Scholar
  52. 52.
    Watanabe, H., S. Sugiura, and T. Hisada. The looped heart does not save energy by maintaining the momentum of blood flowing in the ventricle. Am. J. Physiol. Heart Circ. Physiol. 294:H2191–H2196, 2008.CrossRefPubMedGoogle Scholar
  53. 53.
    Zajac, J., J. Eriksson, P. Dyverfeldt, A. F. Bolger, T. Ebbers, and C.-J. Carlhäll. Turbulent kinetic energy in normal and myopathic left ventricles. J. Magn. Reson. Imaging 41:1021–1029, 2015.CrossRefPubMedGoogle Scholar

Copyright information

© Biomedical Engineering Society 2016

Authors and Affiliations

  1. 1.Université de Montpellier - IMAG CNRS UMR 5149Montpellier Cedex 5France
  2. 2.Biomedical Simulation Laboratory, University of Toronto - Mechanical and Industrial EngineeringTorontoCanada

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