Advertisement

Optimization of MR phase-contrast-based flow velocimetry and shear stress measurements

  • Taeho Kim
  • Ji-Hyea Seo
  • Seong-Sik Bang
  • Hyeon-Woo Choi
  • Yongmin Chang
  • Jongmin Lee
Original Paper

Abstract

This study was designed to measure the pixel-by-pixel flow velocity and shear stress from phase-contrast MR images. An optimized method was suggested and the use of the method was confirmed. A self-developed, straight steady flow model system was scanned by MRI with a velocity-encoded phase-contrast sequence. In-house developed software was used for the pixel-by-pixel flow velocity and shear stress measurements and the measurements were compared with physically measured mean velocity and shear stress. A comparison between the use of the in-house velocimetry software and a commercial velocimetry system was also performed. Curved steady flow models were scanned by phase-contrast MRI. Subsequently, velocity and shear stress were measured to confirm the shifted peak flow velocity and shear stress toward the outer side of the lumen. Peak velocity and shear stress were calculated for both the inner and outer half of the lumen and were statistically compared. The mean velocity measured with the use of in-house software had a significant correlation with the physical measurements of mean velocity; in addition, the measurement was more precise compared to the commercial system (R 2 = 0.85 vs. 0.75, respectively). The calculated mean shear stress had a significant correlation with the physical measurements of mean shear stress (R 2 = 0.95). The curved flow model showed a significantly shifted peak velocity and shear stress zones toward the outside of the flow (P < 0.0001). The technique to measure pixel-by-pixel velocity and shear stress of steady flow from velocity-encoded phase-contrast MRI was optimized. This technique had a good correlation with physical measurements and was superior to a commercially available system.

Keywords

Flow velocity Shear stress MRI Phase-contrast MRI 

Notes

Acknowledgments

“This work was supported by the Korea Research Foundation (KRF) grant funded by the Korea government (MEST).” (No. 2009-0071901).

References

  1. 1.
    Pyorala K, Laakso M, Uusitupa M (1987) Diabetes and atherosclerosis: an epidemiologic view. Diabetes Metab Rev 3(2):463–524CrossRefPubMedGoogle Scholar
  2. 2.
    Wissler RW, Strong JP (1998) Risk factors and progression of atherosclerosis in youth. PDAY Research Group. Pathological determinants of atherosclerosis in youth. Am J Pathol 153(4):1023–1033PubMedGoogle Scholar
  3. 3.
    Simon A, Gariepy J, Chironi G et al (2002) Intimal-media thickness: a new tool for diagnosis and treatment of cardiovascular risk. J Hypertens 20(2):159–169CrossRefPubMedGoogle Scholar
  4. 4.
    Gamble G, Zorn J, Sanders G et al (1994) Estimation of arterial stiffness, compliance, and distensibility from M-mode ultrasound measurements of the common carotid artery. Stroke 25(1):11–16PubMedGoogle Scholar
  5. 5.
    Younis HF, Kaazempur-Mofrad MR, Chan RC et al (2004) Hemodynamics and wall mechanics in human carotid bifurcation and its consequences for atherogenesis: investigation of inter-individual variation. Biomech Model Mechanobiol 3(1):17–32CrossRefPubMedGoogle Scholar
  6. 6.
    Nerem RM (1992) Vascular fluid mechanics, the arterial wall, and atherosclerosis. J Biomech Eng 114(3):274–282CrossRefPubMedGoogle Scholar
  7. 7.
    Efstathopoulos EP, Patatoukas G, Pantos I et al (2008) Wall shear stress calculation in ascending aorta using phase contrast magnetic resonance imaging. Investigating effective ways to calculate it in clinical practice. Phys Med 24(4):175–181PubMedCrossRefGoogle Scholar
  8. 8.
    Shin SH (2005) Hemodynamics. In: Yoo SS (ed) Fluid mechanics, 1st edn. Scitech Media, Seoul, pp 632–715Google Scholar
  9. 9.
    Christopher WM (1994) Elastic Solid. In: Christopher WM (ed) RHEOLOGY principles, measurements, and applications, 1st edn. WILEY-VCH, New York, pp 5–62Google Scholar
  10. 10.
    Gatehouse PD, Keegan J, Crowe LA et al (2005) Application of phase-contrast flow and velocity imaging in cardiovascular MRI. Eur Radiol 15(10):2172–2184CrossRefPubMedGoogle Scholar
  11. 11.
    Oyre S, Ringgaard S, Kozerke S et al (1998) Quantitation of circumferential subpixel vessel wall position and wall shear stress by multiple sectored three-dimensional paraboloid modeling of velocity encoded cine MR. Magn Reson Med 40(5):645–655CrossRefPubMedGoogle Scholar
  12. 12.
    Groβe S, Schroeder W (2008) Dynamic wall-shear stress measurements in turbulent pipe flow using the micro-pillar sensor MPS. Int J Heat Fluid Fl 29(3):830–840CrossRefGoogle Scholar
  13. 13.
    Hom JJ, Ordovas K, Reddy GP (2008) Velocity-encoded cine MR imaging in aortic coarctation: functional assessment of hemodynamic events. Radiographics 28(2):407–416CrossRefPubMedGoogle Scholar
  14. 14.
    Walker MF, Souza SP, Dumoulin CL (1988) Quantitative flow measurement in phase contrast MR angiography. J Comput Assist Tomogr 12(2):304–313CrossRefPubMedGoogle Scholar
  15. 15.
    Firmin DN, Nayler GL, Klipstein RH et al (1987) In vivo validation of MR velocity imaging. J Comput Assist Tomogr 11(5):751–756CrossRefPubMedGoogle Scholar
  16. 16.
    Tarnawski M, Padayachee S, West DJ et al (1990) The measurement of time-averaged flow by magnetic resonance imaging using continuous acquisition in the carotid arteries and its comparison with Doppler ultrasound. Clin Phys Physiol Meas 11(1):27–36CrossRefPubMedGoogle Scholar
  17. 17.
    Varaprasathan GA, Araoz PA, Higgins CB et al (2002) Quantification of flow dynamics in congenital heart disease: applications of velocity-encoded cine MR imaging. Radiographics 22(4):895–905PubMedGoogle Scholar
  18. 18.
    Didier D, Ratib O, Lerch R et al (2000) Detection and quantification of valvular heart disease with dynamic cardiac MR imaging. Radiographics 20(5):1279–1299PubMedGoogle Scholar
  19. 19.
    Ku DN, Giddens DP, Zarins CK et al (1985) Pulsatile flow and atherosclerosis in the human carotid bifurcation. Positive correlation between plaque location and low oscillating shear stress. Arteriosclerosis 5(3):293–302PubMedGoogle Scholar
  20. 20.
    Xue Y, Gao P, Lin Y et al (2007) Preliminary study of hemodynamics in human carotid bifurcation by computational fluid dynamics combined with magnetic resonance angiography. Acta Radiol 48(7):788–797CrossRefPubMedGoogle Scholar
  21. 21.
    Michal MM, Lars RS (2004) Microparticle image velocimetry—an overview. J Turbul 10:83–90Google Scholar
  22. 22.
    Poelma C, Vennemann P, Heiden K et al. (2000) Wall shear stress measurements using μPIV in the outflow tract of a chick embryo. 14th internatinal symposium on applications of lawer techniques to fluid mechanics Lisbon, Portugal 2008:1–6Google Scholar

Copyright information

© Springer Science+Business Media, B.V. 2009

Authors and Affiliations

  • Taeho Kim
    • 1
  • Ji-Hyea Seo
    • 1
  • Seong-Sik Bang
    • 1
  • Hyeon-Woo Choi
    • 1
  • Yongmin Chang
    • 1
  • Jongmin Lee
    • 1
  1. 1.Kyungpook National University HospitalDaeguSouth Korea

Personalised recommendations