Experiments in Fluids

, 55:1821 | Cite as

In vitro post-stenotic flow quantification and validation using echo particle image velocimetry (Echo PIV)

  • Andrew M. Walker
  • Joel Scott
  • David E. Rival
  • Clifton R. Johnston
Research Article


Echo particle image velocimetry (Echo PIV) presents itself as an attractive in vivo flow quantification technique to traditional approaches. Promising results have been acquired; however, limited quantification and validation is available for post-stenotic flows. We focus here on the comprehensive evaluation of in vitro downstream stenotic flow quantified by Echo PIV and validated in relation to digital particle image velocimetry (DPIV). A Newtonian blood analog was circulated through a closed flow loop and quantified immediately downstream of a 50 % axisymmetric blockage at two Reynolds numbers (Re) using time-averaged Echo PIV and DPIV. Centerline velocities were in good agreement at all Re; however, Echo PIV measurements presented with elevated standard deviation (SD) at all measurements points. SD was improved using increased line density (LD); however, frame rate or field of view (FOV) is compromised. Radial velocity profiles showed close agreement with DPIV with the largest disparity in the shear layer and near-wall recirculation. Downstream recirculation zones were resolved by Echo PIV at both Re; however, magnitude and spatial coverage was reduced compared to DPIV that coincided with reduced contrast agent penetration beyond the shear layer. Our findings support the use of increased LD at a cost to FOV and highlight reduced microbubble penetration beyond the shear layer. High local SD at near-wall measurements suggests that further refinement is required before proceeding to in vivo quantification studies of wall shear stress in complex flow environments.


Wall Shear Stress Shear Layer Line Density Centerline Velocity Digital Particle Image Velocimetry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors wish to acknowledge funding provided by the National Sciences and Engineering Research Council (NSERC, Grant Number: 261969-2010). We also wish to thank the Department of Anesthesia at the University of Calgary for providing Definity® echo contrast.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Andrew M. Walker
    • 1
  • Joel Scott
    • 2
  • David E. Rival
    • 1
  • Clifton R. Johnston
    • 2
  1. 1.Department of Mechanical and Manufacturing EngineeringUniversity of CalgaryCalgaryCanada
  2. 2.Department of Mechanical EngineeringDalhousie UniversityHalifaxCanada

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