Image-Based Simulations Show Important Flow Fluctuations in a Normal Left Ventricle: What Could be the Implications?
- 550 Downloads
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.
KeywordsLeft 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).
- 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.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
- 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
- 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
- 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
- 37.Pasipoularides, A. Diastolic filling vortex forces and cardiac adaptations: probing the epigenetic nexus. Hell. J. Cardiol. 53:458–469, 2012.Google Scholar
- 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