Advertisement

Annals of Biomedical Engineering

, Volume 27, Issue 1, pp 32–41 | Cite as

Accuracy of Computational Hemodynamics in Complex Arterial Geometries Reconstructed from Magnetic Resonance Imaging

  • J. A. Moore
  • D. A. Steinman
  • D. W. Holdsworth
  • C. R. Ethier
Article

Abstract

Purpose: Combining computational blood flow modeling with three-dimensional medical imaging provides a new approach for studying links between hemodynamic factors and arterial disease. Although this provides patient-specific hemodynamic information, it is subject to several potential errors. This study quantifies some of these errors and identifies optimal reconstruction methodologies. Methods: A carotid artery bifurcation phantom of known geometry was imaged using a commercial magnetic resonance (MR) imager. Three-dimensional models were reconstructed from the images using several reconstruction techniques, and steady and unsteady blood flow simulations were performed. The carotid bifurcation from a healthy, human volunteer was then imaged in vivo, and geometric models were reconstructed. Results: Reconstructed models of the phantom showed good agreement with the gold standard geometry, with a mean error of approximately 15% between the computed wall shear stress fields. Reconstructed models of the in vivo carotid bifurcation were unacceptably noisy, unless lumenal profile smoothing and approximating surface splines were used. Conclusions: All reconstruction methods gave acceptable results for the phantom model, but in vivo models appear to require smoothing. If proper attention is paid to smoothing and geometric fidelity issues, models reconstructed from MR images appear to be suitable for use in computational studies of in vivo hemodynamics. © 1999 Biomedical Engineering Society.

PAC99: 8719Uv, 8761-c, 0705Pj, 8710+e

Blood flow Magnetic resonance imaging Numerical flow modeling Carotid artery Three-dimensional Wall shear stress Atherosclerosis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

REFERENCES

  1. 1.
    Ethier, C. R., D. A. Steinman, and M. Ojha. Comparisons between computational hemodynamics, photochromic dye flow visualization and magnetic resonance velocimetry. In: Hemodynamics of Internal Organs, edited by M. W. Collins and Y. Xu. Ashurst, UK: Computational Mechanics Publications (in press).Google Scholar
  2. 2.
    Friedman, M. H., C. B. Bargeron, D. D. Duncan, G. M. Hutchins, and F. F. Mark. Effects of arterial compliance and non-Newtonian rheology on correlations between intimal thickness and wall shear. J. Biomech. Eng. 114:317–320, 1992.Google Scholar
  3. 3.
    Holdsworth, D. W., C. J. Norley, R. Frayne, D. Steinman, and B. K. Rutt. Variability in common carotid blood flow waveforms (abstract). Radiology 197(P):451, 1995.Google Scholar
  4. 4.
    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 and oscillating shear stress. Arteriosclerosis (Dallas) 5:293–302, 1985.Google Scholar
  5. 5.
    Milner, J., J. A. Moore, B. K. Rutt, and D. A. Steinman. Hemodynamics of human carotid artery bifurcations: computational studies in models reconstructed from magnetic resonance imaging of normal subjects. J. Vasc. Surg. 28:143–156, 1998.Google Scholar
  6. 6.
    Moore, J. A. Computational Blood Flow Modelling In Realistic Arterial Geometries. University of Toronto, Department of Mechanical Engineering: PhD Thesis, 1998.Google Scholar
  7. 7.
    Moore, J. A., D. A. Steinman, and C. R. Ethier. Computational blood flow modelling: Errors associated with reconstructing finite element models from magnetic resonance images. J. Biomech. 31:179–184, 1998.Google Scholar
  8. 8.
    Moore, J. E. J., D. N. Ku, C. K. Zarins, and S. Glagov. Pulsatile flow visualization in the abdominal aorta under differing physiologic conditions: Implications for increased susceptibility to atherosclerosis. J. Biomech. Eng. 114:391–397, 1992.Google Scholar
  9. 9.
    Robb, R. A. Three-dimensional Biomedical Imaging: Principles and Practice. New York: VCH Publishers, 1995.Google Scholar
  10. 10.
    Smith, R. F. Geometric Models of the Stenosed Human Carotid Bifurcation. Dept. Medical Biophysics, U. Western Ontario: MSc Thesis, 1997.Google Scholar
  11. 11.
    Smith, R. F., B. K. Rutt, A. J. Fox, R. N. Rankin, and D. W. Holdsworth. Geometric characterization of stenosed human carotid arteries. Acad. Radiol. 3:898–911, 1996.Google Scholar
  12. 12.
    Sun, H., B. D. Kuban, P. Schmalbrock, and M. H. Friedman. Measurement of the geometric parameters of the aortic bifurcation from magnetic resonance images. Ann. Biomed. Eng. 22:229–239, 1994.Google Scholar
  13. 13.
    Taylor, C. A., T. J. Hughes, and C. K. Zarins. Computational investigations in vascular disease. Comput. Phys. 10:224–232, 1996.Google Scholar

Copyright information

© Biomedical Engineering Society 1999

Authors and Affiliations

  • J. A. Moore
    • 1
  • D. A. Steinman
    • 2
    • 3
  • D. W. Holdsworth
    • 2
    • 3
    • 4
  • C. R. Ethier
    • 1
  1. 1.Department of Mechanical and Industrial Engineering and Institute for Biomedical EngineeringUniversity of TorontoTorontoCanada
  2. 2.Imaging Research LaboratoriesJohn P. Robarts Research InstituteLondonCanada
  3. 3.Department of Medical BiophysicsUniversity of Western OntarioLondonCanada
  4. 4.Department of Diagnostic Radiology and Nuclear MedicineUniversity of Western OntarioLondonCanada

Personalised recommendations