Cardiovascular Engineering and Technology

, Volume 5, Issue 3, pp 261–269 | Cite as

Improving Blood Flow Simulations by Incorporating Measured Subject-Specific Wall Motion

  • Jonas Lantz
  • Petter Dyverfeldt
  • Tino Ebbers


Physiologically relevant simulations of blood flow require models that allow for wall deformation. Normally a fluid–structure interaction (FSI) approach is used; however, this method relies on several assumptions and patient-specific material parameters that are difficult or impossible to measure in vivo. In order to circumvent the assumptions inherent in FSI models, aortic wall motion was measured with MRI and prescribed directly in a numerical solver. In this way is not only the displacement of the vessel accounted for, but also the interaction with the beating heart and surrounding organs. In order to highlight the effect of wall motion, comparisons with standard rigid wall models was performed in a healthy human aorta. The additional computational cost associated with prescribing the wall motion was low (17%). Standard hemodynamic parameters such as time-averaged wall shear stress and oscillatory shear index seemed largely unaffected by the wall motion, as a consequence of the smoothing effect inherent in time-averaging. Conversely, instantaneous wall shear stress was greatly affected by the wall motion; the wall dynamics seemed to produce a lower wall shear stress magnitude compared to a rigid wall model. In addition, it was found that if wall motion was taken into account the computed flow field agreed better with in vivo measurements. This article shows that it is feasible to include measured subject-specific wall motion into numerical simulations, and that the wall motion greatly affects the flow field. This approach to incorporate measured motion should be considered in future studies of arterial blood flow simulations.


Computational fluid dynamics Magnetic resonance imaging Fluid–structure interaction Aorta Time averaged wall shear stress Prescribed wall motion 



This study was funded by the Swedish e-Science Research Centre, the Centre for Industrial Information Technology, the Swedish Research Council, and the European Research Council. The Swedish National Infrastructure for Computing is acknowledged for computational resources provided by the National Supercomputer Centre.

Conflict of interest

The authors declared that they have no conflict of interest.

Statement of Animal Studies

No animal studies were carried out by the authors for this article.

Statement of Human studies

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 (5). Informed consent was obtained from the subject for being included in the study.

Supplementary material

13239_2014_187_MOESM1_ESM.pdf (186 kb)
Supplementary material 1 (PDF 186 kb)


  1. 1.
    Assemat, P., and K. Hourigan. Evolution and rupture of vulnerable plaques: a review of mechanical effects. ChronoPhysiol. Ther. 3:23–40, 2013.Google Scholar
  2. 2.
    Barakat, A. I. Blood flow and arterial endothelial dysfunction: mechanisms and implications. C.R. Phys. 14:479–496, 2013.CrossRefGoogle Scholar
  3. 3.
    Caballero, A., and S. Laín. A review on computational fluid dynamics modelling in human thoracic aorta. Cardiovasc. Eng. Technol. 4(2):103–130, 2013.CrossRefGoogle Scholar
  4. 4.
    Gao, F., Z. Guo, M. Sakamoto, and T. Matsuzawa. Fluid–structure interaction within a layered aortic arch model. J. Biol. Phys. 32:435–454, 2006.CrossRefGoogle Scholar
  5. 5.
    He, X., and D. N. Ku. Pulsatile flow in the human left coronary artery bifurcation: average conditions. J. Biomech. Eng. 118:74–82, 1996.CrossRefGoogle Scholar
  6. 6.
    Heiberg, E., J. Sjogren, M. Ugander, M. Carlsson, H. Engblom, and H. Arheden. Design and validation of segment—freely available software for cardiovascular image analysis. BMC Med. Imaging 10:1, 2010.CrossRefGoogle Scholar
  7. 7.
    Jin, S., J. Oshinski, and D. P. Giddens. Effects of wall motion and compliance on flow patterns in the ascending aorta. J. Biomech. Eng. 125:347–354, 2003.CrossRefGoogle Scholar
  8. 8.
    Khanafer, K., J. Bull, and R. Berguer. Fluid–structure interaction of turbulent pulsatile flow within a flexible wall axisymmetric aortic aneurysm model. Eur. J. Mech. B 28:88–102, 2009.CrossRefzbMATHGoogle Scholar
  9. 9.
    Ku, D. N., D. P. Giddens, C. K. Zarins, and S. Glagov. Pulsatile flow and atherosclerosis in the human carotid bifurcation. Positive correlation between plaque location and low oscillating shear stress. Arteriosclerosis 5:293–302, 1985.CrossRefGoogle Scholar
  10. 10.
    Lantz, J., J. Renner, and M. Karlsson. Wall shear stress in a subject specific human aorta—influence of fluid–structure interaction. Int. J. Appl. Mech. 3:759–778, 2011.CrossRefGoogle Scholar
  11. 11.
    Malek, A. M., S. L. Alper, and S. Izumo. Hemodynamic shear stress and its role in atherosclerosis. JAMA 282:2035–2042, 1999.CrossRefGoogle Scholar
  12. 12.
    Moireau, P., N. Xiao, M. Astorino, C. A. Figueroa, D. Chapelle, C. A. Taylor, and J. F. Gerbeau. External tissue support and fluid–structure simulation in blood flows. Biomech. Model. Mechanobiol. 11:1–18, 2012.CrossRefGoogle Scholar
  13. 13.
    Mynard, J. P., B. A. Wasserman, and D. A. Steinman. Errors in the estimation of wall shear stress by maximum Doppler velocity. Atherosclerosis 227:259–266, 2013.CrossRefGoogle Scholar
  14. 14.
    Petersson, S., P. Dyverfeldt, and T. Ebbers. Assessment of the accuracy of MRI wall shear stress estimation using numerical simulations. J. Magn. Reson. Imaging 36:128–138, 2012.CrossRefGoogle Scholar
  15. 15.
    Resnick, N., H. Yahav, A. Shay-Salit, M. Shushy, S. Schubert, L. C. M. Zilberman, and E. Wofovitz. Fluid shear stress and the vascular endothelium: for better and for worse. Prog. Biophys. Mol. Biol. 81:177–199, 2003.CrossRefGoogle Scholar
  16. 16.
    Steinman, D. A. Image-based computational fluid dynamics modeling in realistic arterial geometries. Ann. Biomed. Eng. 30:483–497, 2002.CrossRefGoogle Scholar
  17. 17.
    Taylor, C. A., M. T. Draney, J. P. Ku, D. Parker, B. N. Steele, K. Wang, and C. K. Zarins. Predictive medicine: computational techniques in therapeutic decision-making. Comput Aided Surg. 4:231–247, 1999.CrossRefGoogle Scholar
  18. 18.
    Taylor, C. A., and C. Figueroa. Patient-specific modeling of cardiovascular mechanics. Annu. Rev. Biomed. Eng. 11:109–134, 2009.CrossRefGoogle Scholar
  19. 19.
    Taylor, C. A., and D. A. Steinman. Image-based modeling of blood flow and vessel wall dynamics: applications, methods and future directions. Ann. Biomed. Eng. 38:1188–1203, 2010.CrossRefGoogle Scholar
  20. 20.
    Torii, R., J. Keegan, N. B. Wood, A. W. Dowsey, A. D. Hughes, G.-Z. Yang, D. N. Firmin, S. A. Thom, and X. Y. Xu. MR image-based geometric and hemodynamic investigation of the right coronary artery with dynamic vessel motion. Ann. Biomed. Eng. 38:2606–2620, 2010.CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2014

Authors and Affiliations

  • Jonas Lantz
    • 1
    • 4
  • Petter Dyverfeldt
    • 2
    • 3
  • Tino Ebbers
    • 1
    • 2
    • 3
    • 4
  1. 1.Department of Science and TechnologyLinköping UniversityLinköpingSweden
  2. 2.Department of Medical and Health SciencesLinköping UniversityLinköpingSweden
  3. 3.Center for Medical Image Science and Visualization (CMIV)Linköping UniversityLinköpingSweden
  4. 4.Swedish e-Science Research Centre (SeRC)LinköpingSweden

Personalised recommendations