Multi-dimensional confocal laser scanning microscopy image correlation for nanoparticle flow velocimetry

  • Brian H. Jun
  • Matthew Giarra
  • Pavlos P. VlachosEmail author
Research Paper


We present a new multi-dimensional confocal laser scanning microscopy (CLSM) image correlation for nanoparticle flow velocimetry that is robust to sources of decorrelating errors. Random and bias errors from nanoparticle flow measurements exacerbate with increased dimensionality in CLSM images, rendering measurements unusable. Our new algorithm tackles these measurement limitations in twofold. First, we model and correct for the bias errors introduced by the effects of the volumetric laser scanning image acquisition. Second, we developed a new spectral filter using a phase-quality masking technique that optimizes its size for the spectral content of CLSM images, without requiring a priori knowledge of displacement fields or flow tracer properties. We validated our algorithm using synthetic images and experimentally obtained 2D and 3D CLSM images of nanoparticle flow through a micro-channel. We show that our technique significantly outperforms the standard cross-correlation (SCC) in reducing both the random and bias errors and accelerated the convergence of ensemble correlation velocity measurements from CLSM images.



  1. Digman MA et al (2005) Fluctuation correlation spectroscopy with a laser-scanning microscope: exploiting the hidden time structure. Biophys J 88(5):L33–L36CrossRefGoogle Scholar
  2. Digman MA, Stakic M, Gratton E (2013) Raster image correlation spectroscopy and number and brightness analysis. Methods Enzymol 518:121–144CrossRefGoogle Scholar
  3. Eckstein A, Vlachos PP (2009a) Assessment of advanced windowing techniques for digital particle image velocimetry (DPIV). Meas Sci Technol 20(7):075402CrossRefGoogle Scholar
  4. Eckstein A, Vlachos PP (2009b) Digital particle image velocimetry (DPIV) robust phase correlation. Meas Sci Technol 20(5):055401CrossRefGoogle Scholar
  5. Eckstein AC, Charonko J, Vlachos P (2008) Phase correlation processing for DPIV measurements. Exp Fluids 45(3):485–500CrossRefGoogle Scholar
  6. Efford N (2000) Digital image processing: a practical introduction using java (with CD-ROM). Addison-Wesley Longman Publishing Co., Inc., BostonGoogle Scholar
  7. Einstein A (1905) Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen. Ann Phys 322(8):549–560CrossRefGoogle Scholar
  8. Ghiglia DC, Pritt MD (1998) Two-dimensional phase unwrapping: theory, algorithms, and software, vol. xiv. Wiley, New York, p 493zbMATHGoogle Scholar
  9. Jonkman J, Brown CM (2015) Any way you slice it-a comparison of confocal microscopy techniques. J Biomol Tech 26(2):54–65Google Scholar
  10. Jun BH et al. (2016) Nanoparticle flow velocimetry with image phase correlation for confocal laser scanning microscopy. Meas Sci Technol. 27(10):104003CrossRefGoogle Scholar
  11. Malone MH et al (2007) Laser-scanning velocimetry: a confocal microscopy method for quantitative measurement of cardiovascular performance in zebrafish embryos and larvae. BMC Biotechnol 7:40CrossRefGoogle Scholar
  12. Meinhart CD, Wereley ST, Santiago JG (1999) PIV measurements of a microchannel flow. Exp Fluids 27(5):414–419CrossRefGoogle Scholar
  13. Meinhart CD, Wereley ST, Santiago JG (2000) A PIV algorithm for estimating time-averaged velocity fields. J Fluids Eng Trans ASME 122(2):285–289CrossRefGoogle Scholar
  14. Olsen MG, Adrian RJ (2000) Brownian motion and correlation in particle image velocimetry. Opt Laser Technol 32(7–8):621–627CrossRefGoogle Scholar
  15. Olsen MG, Adrian RJ (2001) Measurement volume defined by peak-finding algorithms in cross-correlation particle image velocimetry. Meas Sci Technol 12(2):N14–N16CrossRefGoogle Scholar
  16. Pan X et al (2009) Line scan fluorescence correlation spectroscopy for three-dimensional microfluidic flow velocity measurements. J Biomed Opt 14(2):024049CrossRefGoogle Scholar
  17. Raben JS et al (2013) Improved accuracy of time-resolved micro-particle image velocimetry using phase-correlation and confocal microscopy. Microfluid Nanofluid 14(3–4):431–444CrossRefGoogle Scholar
  18. Raffel M (2007) Particle image velocimetry: a practical guide, 2nd edn. Springer, Heidelberg, New York, p 448Google Scholar
  19. Rossow MJ, Mantulin WW, Gratton E (2010a) Scanning laser image correlation for measurement of flow. J Biomed Opt 15(2):026003CrossRefGoogle Scholar
  20. Rossow MJ et al (2010b) Raster image correlation spectroscopy in live cells. Nat Protoc 5(11):1761–1774CrossRefGoogle Scholar
  21. Sironi L et al (2014) In vivo flow mapping in complex vessel networks by single image correlation. Sci Rep 4:7341CrossRefGoogle Scholar
  22. Westerweel J (1997) Fundamentals of digital particle image velocimetry. Meas Sci Technol 8(12):1379–1392CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Brian H. Jun
    • 1
  • Matthew Giarra
    • 2
  • Pavlos P. Vlachos
    • 1
    Email author
  1. 1.Department of Mechanical EngineeringPurdue UniversityWest LafayetteUSA
  2. 2.The Johns Hopkins University Applied Physics LaboratoryLaurelUSA

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