Abstract

We present a new OCT method for flow speed quantification and directional velocimetry: particle streak velocimetry-OCT (PSV-OCT). PSV-OCT generates two-dimensional, 2.5-vector component (vx,|vy|,vz) maps of microscale flow velocity fields. Knowledge of 2.5-vector components also enables the estimation of total flow speed. The enabling insight behind PSV-OCT is that tracer particles in sparsely-seeded fluid flow trace out streaks in (x,z,t)-space. The streak orientations in x-t and z-t yield vx and vz, respectively. The in-plane (x-z plane) residence time yields the out-of-plane speed |vy|. Vector component values are generated by fitting streaks to a model of image formation that incorporates equations of motion in 3D space. We demonstrate cross-sectional estimation of (vx,|vy|,vz) in two important animal models in ciliary biology: Xenopus embryos (tadpoles) and mouse trachea.

© 2016 Optical Society of America

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Corrections

Kevin C. Zhou, Brendan K. Huang, Ute A. Gamm, Vineet Bhandari, Mustafa K. Khokha, and Michael A. Choma, "Particle streak velocimetry-optical coherence tomography: a novel method for multidimensional imaging of microscale fluid flows: erratum," Biomed. Opt. Express 7, 2360-2361 (2016)
https://www.osapublishing.org/boe/abstract.cfm?uri=boe-7-6-2360

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References

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2016 (2)

2015 (7)

B. K. Huang, U. A. Gamm, V. Bhandari, M. K. Khokha, and M. A. Choma, “Three-dimensional, three-vector-component velocimetry of cilia-driven fluid flow using correlation-based approaches in optical coherence tomography,” Biomed. Opt. Express 6(9), 3515–3538 (2015).
[Crossref] [PubMed]

R. Ansari, C. Buj, M. Pieper, P. König, A. Schweikard, and G. Huttmann, “Micro-anatomical and functional assessment of ciliated epithelium in mouse trachea using optical coherence phase microscopy,” Opt. Express 23(18), 23217–23224 (2015).
[Crossref] [PubMed]

K. C. Zhou, B. K. Huang, H. Tagare, and M. A. Choma, “Improved velocimetry in optical coherence tomography using Bayesian analysis,” Biomed. Opt. Express 6(12), 4796–4811 (2015).
[Crossref] [PubMed]

B. K. Huang, U. A. Gamm, S. Jonas, M. K. Khokha, and M. A. Choma, “Quantitative optical coherence tomography imaging of intermediate flow defect phenotypes in ciliary physiology and pathophysiology,” J. Biomed. Opt. 20(3), 030502 (2015).
[Crossref] [PubMed]

U. A. Gamm, B. K. Huang, M. Syed, X. Zhang, V. Bhandari, and M. A. Choma, “Quantifying hyperoxia-mediated damage to mammalian respiratory cilia-driven fluid flow using particle tracking velocimetry optical coherence tomography,” J. Biomed. Opt. 20(8), 080505 (2015).
[Crossref] [PubMed]

A. Buchsbaum, M. Egger, I. Burzic, T. Koepplmayr, M. Aigner, J. Miethlinger, and M. Leitner, “Optical coherence tomography based particle image velocimetry (OCT-PIV) of polymer flows,” Opt. Lasers Eng. 69, 40–48 (2015).
[Crossref]

B. K. Huang and M. A. Choma, “Microscale imaging of cilia-driven fluid flow,” Cell. Mol. Life Sci. 72(6), 1095–1113 (2015).
[Crossref] [PubMed]

2014 (4)

W. Drexler, M. Liu, A. Kumar, T. Kamali, A. Unterhuber, and R. A. Leitgeb, “Optical coherence tomography today: speed, contrast, and multimodality,” J. Biomed. Opt. 19(7), 071412 (2014).
[Crossref] [PubMed]

L. Liu, S. Shastry, S. Byan-Parker, G. Houser, K. K Chu, S. E. Birket, C. M. Fernandez, J. A. Gardecki, W. E. Grizzle, E. J. Wilsterman, E. J. Sorscher, S. M. Rowe, and G. J. Tearney, “An autoregulatory mechanism governing mucociliary transport is sensitive to mucus load,” Am. J. Respir. Cell Mol. Biol. 51(4), 485–493 (2014).
[Crossref] [PubMed]

S. E. Birket, K. K. Chu, L. Liu, G. H. Houser, B. J. Diephuis, E. J. Wilsterman, G. Dierksen, M. Mazur, S. Shastry, Y. Li, J. D. Watson, A. T. Smith, B. S. Schuster, J. Hanes, W. E. Grizzle, E. J. Sorscher, G. J. Tearney, and S. M. Rowe, “A functional anatomic defect of the cystic fibrosis airway,” Am. J. Respir. Crit. Care Med. 190(4), 421–432 (2014).
[Crossref] [PubMed]

B. K. Huang and M. A. Choma, “Resolving directional ambiguity in dynamic light scattering-based transverse motion velocimetry in optical coherence tomography,” Opt. Lett. 39(3), 521–524 (2014).
[Crossref] [PubMed]

2013 (4)

M. Mujat, R. D. Ferguson, N. Iftimia, D. X. Hammer, I. Nedyalkov, M. Wosnik, and H. Legner, “Optical coherence tomography-based micro-particle image velocimetry,” Opt. Lett. 38(22), 4558–4561 (2013).
[Crossref] [PubMed]

C.-Y. Chen, P. G. Menon, W. Kowalski, and K. Pekkan, “Time-resolved OCT-μPIV: a new microscopic PIV technique for noninvasive depth-resolved pulsatile flow profile acquisition,” Exp. Fluids 54(1), 1–9 (2013).
[Crossref]

L. Liu, K. K. Chu, G. H. Houser, B. J. Diephuis, Y. Li, E. J. Wilsterman, S. Shastry, G. Dierksen, S. E. Birket, M. Mazur, S. Byan-Parker, W. E. Grizzle, E. J. Sorscher, S. M. Rowe, and G. J. Tearney, “Method for quantitative study of airway functional microanatomy using micro-optical coherence tomography,” PLoS One 8(1), e54473 (2013).
[Crossref] [PubMed]

N. Weiss, T. G. van Leeuwen, and J. Kalkman, “Localized measurement of longitudinal and transverse flow velocities in colloidal suspensions using optical coherence tomography,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 88(4), 042312 (2013).
[Crossref] [PubMed]

2012 (4)

2011 (2)

2010 (2)

2009 (1)

A. Davis, J. Izatt, and F. Rothenberg, “Quantitative Measurement of Blood Flow Dynamics in Embryonic Vasculature Using Spectral Doppler Velocimetry,” Anat. Rec. (Hoboken) 292(3), 311–319 (2009).
[Crossref] [PubMed]

2008 (1)

2007 (1)

2006 (1)

A. Røyset, T. Støren, F. Stabo-Eeg, and T. Lindmo, “Quantitative measurements of flow velocity and direction using transversal Doppler optical coherence tomography,” Proc. SPIE 6079, 607925 (2006).

2004 (1)

E. A. V. Jones, M. H. Baron, S. E. Fraser, and M. E. Dickinson, “Measuring hemodynamic changes during mammalian development,” Am. J. Physiol. Heart Circ. Physiol. 287(4), H1561–H1569 (2004).
[Crossref] [PubMed]

1997 (2)

1991 (2)

C. E. Willert and M. Gharib, “Digital particle image velocimetry,” Exp. Fluids 10(4), 181–193 (1991).
[Crossref]

R. J. Adrian, “Particle-Imaging Techniques for Experimental Fluid-Mechanics,” Annu. Rev. Fluid Mech. 23(1), 261–304 (1991).
[Crossref]

1981 (1)

P. E. Dimotakis, F. D. Debussy, and M. M. Koochesfahani, “Particle Streak Velocity-Field Measurements in a Two-Dimensional Mixing Layer,” Phys. Fluids 24(6), 995–999 (1981).
[Crossref]

Adrian, R. J.

R. J. Adrian, “Particle-Imaging Techniques for Experimental Fluid-Mechanics,” Annu. Rev. Fluid Mech. 23(1), 261–304 (1991).
[Crossref]

Ahn, Y.-C.

Aigner, M.

A. Buchsbaum, M. Egger, I. Burzic, T. Koepplmayr, M. Aigner, J. Miethlinger, and M. Leitner, “Optical coherence tomography based particle image velocimetry (OCT-PIV) of polymer flows,” Opt. Lasers Eng. 69, 40–48 (2015).
[Crossref]

Ansari, R.

Baron, M. H.

E. A. V. Jones, M. H. Baron, S. E. Fraser, and M. E. Dickinson, “Measuring hemodynamic changes during mammalian development,” Am. J. Physiol. Heart Circ. Physiol. 287(4), H1561–H1569 (2004).
[Crossref] [PubMed]

Barry, S.

Barton, J. K.

Baumann, B.

Bhandari, V.

U. A. Gamm, B. K. Huang, M. Syed, X. Zhang, V. Bhandari, and M. A. Choma, “Quantifying hyperoxia-mediated damage to mammalian respiratory cilia-driven fluid flow using particle tracking velocimetry optical coherence tomography,” J. Biomed. Opt. 20(8), 080505 (2015).
[Crossref] [PubMed]

B. K. Huang, U. A. Gamm, V. Bhandari, M. K. Khokha, and M. A. Choma, “Three-dimensional, three-vector-component velocimetry of cilia-driven fluid flow using correlation-based approaches in optical coherence tomography,” Biomed. Opt. Express 6(9), 3515–3538 (2015).
[Crossref] [PubMed]

Bhattacharya, D.

Birket, S. E.

S. E. Birket, K. K. Chu, L. Liu, G. H. Houser, B. J. Diephuis, E. J. Wilsterman, G. Dierksen, M. Mazur, S. Shastry, Y. Li, J. D. Watson, A. T. Smith, B. S. Schuster, J. Hanes, W. E. Grizzle, E. J. Sorscher, G. J. Tearney, and S. M. Rowe, “A functional anatomic defect of the cystic fibrosis airway,” Am. J. Respir. Crit. Care Med. 190(4), 421–432 (2014).
[Crossref] [PubMed]

L. Liu, S. Shastry, S. Byan-Parker, G. Houser, K. K Chu, S. E. Birket, C. M. Fernandez, J. A. Gardecki, W. E. Grizzle, E. J. Wilsterman, E. J. Sorscher, S. M. Rowe, and G. J. Tearney, “An autoregulatory mechanism governing mucociliary transport is sensitive to mucus load,” Am. J. Respir. Cell Mol. Biol. 51(4), 485–493 (2014).
[Crossref] [PubMed]

L. Liu, K. K. Chu, G. H. Houser, B. J. Diephuis, Y. Li, E. J. Wilsterman, S. Shastry, G. Dierksen, S. E. Birket, M. Mazur, S. Byan-Parker, W. E. Grizzle, E. J. Sorscher, S. M. Rowe, and G. J. Tearney, “Method for quantitative study of airway functional microanatomy using micro-optical coherence tomography,” PLoS One 8(1), e54473 (2013).
[Crossref] [PubMed]

Boas, D. A.

Buchsbaum, A.

A. Buchsbaum, M. Egger, I. Burzic, T. Koepplmayr, M. Aigner, J. Miethlinger, and M. Leitner, “Optical coherence tomography based particle image velocimetry (OCT-PIV) of polymer flows,” Opt. Lasers Eng. 69, 40–48 (2015).
[Crossref]

Buj, C.

Burzic, I.

A. Buchsbaum, M. Egger, I. Burzic, T. Koepplmayr, M. Aigner, J. Miethlinger, and M. Leitner, “Optical coherence tomography based particle image velocimetry (OCT-PIV) of polymer flows,” Opt. Lasers Eng. 69, 40–48 (2015).
[Crossref]

Button, B.

Byan-Parker, S.

L. Liu, S. Shastry, S. Byan-Parker, G. Houser, K. K Chu, S. E. Birket, C. M. Fernandez, J. A. Gardecki, W. E. Grizzle, E. J. Wilsterman, E. J. Sorscher, S. M. Rowe, and G. J. Tearney, “An autoregulatory mechanism governing mucociliary transport is sensitive to mucus load,” Am. J. Respir. Cell Mol. Biol. 51(4), 485–493 (2014).
[Crossref] [PubMed]

L. Liu, K. K. Chu, G. H. Houser, B. J. Diephuis, Y. Li, E. J. Wilsterman, S. Shastry, G. Dierksen, S. E. Birket, M. Mazur, S. Byan-Parker, W. E. Grizzle, E. J. Sorscher, S. M. Rowe, and G. J. Tearney, “Method for quantitative study of airway functional microanatomy using micro-optical coherence tomography,” PLoS One 8(1), e54473 (2013).
[Crossref] [PubMed]

Cable, A. E.

Chen, C.-Y.

C.-Y. Chen, P. G. Menon, W. Kowalski, and K. Pekkan, “Time-resolved OCT-μPIV: a new microscopic PIV technique for noninvasive depth-resolved pulsatile flow profile acquisition,” Exp. Fluids 54(1), 1–9 (2013).
[Crossref]

Chen, Z.

Chhetri, R. K.

Choma, M. A.

U. A. Gamm, B. K. Huang, M. Syed, X. Zhang, V. Bhandari, and M. A. Choma, “Quantifying hyperoxia-mediated damage to mammalian respiratory cilia-driven fluid flow using particle tracking velocimetry optical coherence tomography,” J. Biomed. Opt. 20(8), 080505 (2015).
[Crossref] [PubMed]

B. K. Huang and M. A. Choma, “Microscale imaging of cilia-driven fluid flow,” Cell. Mol. Life Sci. 72(6), 1095–1113 (2015).
[Crossref] [PubMed]

B. K. Huang, U. A. Gamm, S. Jonas, M. K. Khokha, and M. A. Choma, “Quantitative optical coherence tomography imaging of intermediate flow defect phenotypes in ciliary physiology and pathophysiology,” J. Biomed. Opt. 20(3), 030502 (2015).
[Crossref] [PubMed]

B. K. Huang, U. A. Gamm, V. Bhandari, M. K. Khokha, and M. A. Choma, “Three-dimensional, three-vector-component velocimetry of cilia-driven fluid flow using correlation-based approaches in optical coherence tomography,” Biomed. Opt. Express 6(9), 3515–3538 (2015).
[Crossref] [PubMed]

K. C. Zhou, B. K. Huang, H. Tagare, and M. A. Choma, “Improved velocimetry in optical coherence tomography using Bayesian analysis,” Biomed. Opt. Express 6(12), 4796–4811 (2015).
[Crossref] [PubMed]

B. K. Huang and M. A. Choma, “Resolving directional ambiguity in dynamic light scattering-based transverse motion velocimetry in optical coherence tomography,” Opt. Lett. 39(3), 521–524 (2014).
[Crossref] [PubMed]

S. Jonas, D. Bhattacharya, M. K. Khokha, and M. A. Choma, “Microfluidic characterization of cilia-driven fluid flow using optical coherence tomography-based particle tracking velocimetry,” Biomed. Opt. Express 2(7), 2022–2034 (2011).
[Crossref] [PubMed]

Chu, K. K.

S. E. Birket, K. K. Chu, L. Liu, G. H. Houser, B. J. Diephuis, E. J. Wilsterman, G. Dierksen, M. Mazur, S. Shastry, Y. Li, J. D. Watson, A. T. Smith, B. S. Schuster, J. Hanes, W. E. Grizzle, E. J. Sorscher, G. J. Tearney, and S. M. Rowe, “A functional anatomic defect of the cystic fibrosis airway,” Am. J. Respir. Crit. Care Med. 190(4), 421–432 (2014).
[Crossref] [PubMed]

L. Liu, K. K. Chu, G. H. Houser, B. J. Diephuis, Y. Li, E. J. Wilsterman, S. Shastry, G. Dierksen, S. E. Birket, M. Mazur, S. Byan-Parker, W. E. Grizzle, E. J. Sorscher, S. M. Rowe, and G. J. Tearney, “Method for quantitative study of airway functional microanatomy using micro-optical coherence tomography,” PLoS One 8(1), e54473 (2013).
[Crossref] [PubMed]

Dai, C.

Dave, D.

Davis, A.

A. Davis, J. Izatt, and F. Rothenberg, “Quantitative Measurement of Blood Flow Dynamics in Embryonic Vasculature Using Spectral Doppler Velocimetry,” Anat. Rec. (Hoboken) 292(3), 311–319 (2009).
[Crossref] [PubMed]

Debussy, F. D.

P. E. Dimotakis, F. D. Debussy, and M. M. Koochesfahani, “Particle Streak Velocity-Field Measurements in a Two-Dimensional Mixing Layer,” Phys. Fluids 24(6), 995–999 (1981).
[Crossref]

Dickinson, M. E.

E. A. V. Jones, M. H. Baron, S. E. Fraser, and M. E. Dickinson, “Measuring hemodynamic changes during mammalian development,” Am. J. Physiol. Heart Circ. Physiol. 287(4), H1561–H1569 (2004).
[Crossref] [PubMed]

Diephuis, B. J.

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Srinivasan, V. J.

Stabo-Eeg, F.

A. Røyset, T. Støren, F. Stabo-Eeg, and T. Lindmo, “Quantitative measurements of flow velocity and direction using transversal Doppler optical coherence tomography,” Proc. SPIE 6079, 607925 (2006).

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C. Sun, B. Standish, and V. X. D. Yang, “Optical coherence elastography: current status and future applications,” J. Biomed. Opt. 16(4), 043001 (2011).

Støren, T.

A. Røyset, T. Støren, F. Stabo-Eeg, and T. Lindmo, “Quantitative measurements of flow velocity and direction using transversal Doppler optical coherence tomography,” Proc. SPIE 6079, 607925 (2006).

Sun, C.

C. Sun, B. Standish, and V. X. D. Yang, “Optical coherence elastography: current status and future applications,” J. Biomed. Opt. 16(4), 043001 (2011).

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U. A. Gamm, B. K. Huang, M. Syed, X. Zhang, V. Bhandari, and M. A. Choma, “Quantifying hyperoxia-mediated damage to mammalian respiratory cilia-driven fluid flow using particle tracking velocimetry optical coherence tomography,” J. Biomed. Opt. 20(8), 080505 (2015).
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Tearney, G. J.

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Wartak, A.

Watson, J. D.

S. E. Birket, K. K. Chu, L. Liu, G. H. Houser, B. J. Diephuis, E. J. Wilsterman, G. Dierksen, M. Mazur, S. Shastry, Y. Li, J. D. Watson, A. T. Smith, B. S. Schuster, J. Hanes, W. E. Grizzle, E. J. Sorscher, G. J. Tearney, and S. M. Rowe, “A functional anatomic defect of the cystic fibrosis airway,” Am. J. Respir. Crit. Care Med. 190(4), 421–432 (2014).
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Am. J. Respir. Crit. Care Med. (1)

S. E. Birket, K. K. Chu, L. Liu, G. H. Houser, B. J. Diephuis, E. J. Wilsterman, G. Dierksen, M. Mazur, S. Shastry, Y. Li, J. D. Watson, A. T. Smith, B. S. Schuster, J. Hanes, W. E. Grizzle, E. J. Sorscher, G. J. Tearney, and S. M. Rowe, “A functional anatomic defect of the cystic fibrosis airway,” Am. J. Respir. Crit. Care Med. 190(4), 421–432 (2014).
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A. S. G. Singh, C. Kolbitsch, T. Schmoll, and R. A. Leitgeb, “Stable absolute flow estimation with Doppler OCT based on virtual circumpapillary scans,” Biomed. Opt. Express 1(4), 1047–1058 (2010).
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S. Jonas, D. Bhattacharya, M. K. Khokha, and M. A. Choma, “Microfluidic characterization of cilia-driven fluid flow using optical coherence tomography-based particle tracking velocimetry,” Biomed. Opt. Express 2(7), 2022–2034 (2011).
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V. J. Srinivasan, H. Radhakrishnan, E. H. Lo, E. T. Mandeville, J. Y. Jiang, S. Barry, and A. E. Cable, “OCT methods for capillary velocimetry,” Biomed. Opt. Express 3(3), 612–629 (2012).
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B. K. Huang, U. A. Gamm, V. Bhandari, M. K. Khokha, and M. A. Choma, “Three-dimensional, three-vector-component velocimetry of cilia-driven fluid flow using correlation-based approaches in optical coherence tomography,” Biomed. Opt. Express 6(9), 3515–3538 (2015).
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K. C. Zhou, B. K. Huang, H. Tagare, and M. A. Choma, “Improved velocimetry in optical coherence tomography using Bayesian analysis,” Biomed. Opt. Express 6(12), 4796–4811 (2015).
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L. Qi, J. Zhu, A. M. Hancock, C. Dai, X. Zhang, R. D. Frostig, and Z. Chen, “Fully distributed absolute blood flow velocity measurement for middle cerebral arteries using Doppler optical coherence tomography,” Biomed. Opt. Express 7(2), 601–615 (2016).
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J. Biomed. Opt. (4)

C. Sun, B. Standish, and V. X. D. Yang, “Optical coherence elastography: current status and future applications,” J. Biomed. Opt. 16(4), 043001 (2011).

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B. K. Huang, U. A. Gamm, S. Jonas, M. K. Khokha, and M. A. Choma, “Quantitative optical coherence tomography imaging of intermediate flow defect phenotypes in ciliary physiology and pathophysiology,” J. Biomed. Opt. 20(3), 030502 (2015).
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PLoS One (1)

L. Liu, K. K. Chu, G. H. Houser, B. J. Diephuis, Y. Li, E. J. Wilsterman, S. Shastry, G. Dierksen, S. E. Birket, M. Mazur, S. Byan-Parker, W. E. Grizzle, E. J. Sorscher, S. M. Rowe, and G. J. Tearney, “Method for quantitative study of airway functional microanatomy using micro-optical coherence tomography,” PLoS One 8(1), e54473 (2013).
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Proc. SPIE (1)

A. Røyset, T. Støren, F. Stabo-Eeg, and T. Lindmo, “Quantitative measurements of flow velocity and direction using transversal Doppler optical coherence tomography,” Proc. SPIE 6079, 607925 (2006).

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Supplementary Material (2)

NameDescription
» Visualization 1: MOV (7905 KB)      video of polystyrene beads being pushed by the ciliated surface of a tadpole embryo
» Visualization 2: MOV (10067 KB)      the same video presented in (x,z,t)-space

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Figures (7)

Fig. 1
Fig. 1

3D (x,z,t) particle streaks generated by the ciliated skin of a Xenopus embryo. a) A single frame from a time series of 2D images (Visualization 1). The bright dots are particles to be tracked. b-d) The entire 3D stack (Visualization 2).

Fig. 2
Fig. 2

Two-dimensional (z,t) streak model. The streak is the product of the in-plane trajectory and the in-plane residence time, σt = σxy / |vy|. If σt is small (top row), the in-plane residence is small and thus the out-of-plane speed is fast. If σt is large (bottom row), the speed is slow. Sign ambiguity in vy arises because σt is insensitive to the sign of vy.

Fig. 3
Fig. 3

A sample streak fit. The three panels show the three possible 2D projections from the 3D streak (x-z, x-t, and z-t). The smaller, background circles are the image pixels, whose color represents the pixel intensity. As such, the yellow-white circles are where the streak is located. The large region around the streak was the region over which the fitting was done; the orientation of this region is derived from PCA. The fit itself is the continuous region in the background. The turquoise circles represent particle tracking based on center of mass per frame.

Fig. 4
Fig. 4

PSV-OCT in a capillary flow phantom. (a) The intersection of the imaging plane (x0,z0) with a cylinder of radius r’ leads to an ellipse with major and minor axes a and b, determined by the angle of imaging with respect to the tube. The tube was rotated about the global x-axis by an angle φ = 9°. The imaging plane was then rotated about the global z-axis by an angle ψ, from 0° to 90°. Intensity images at three ψ are shown. Flow was estimated using PSV-OCT at each of these ψ. (b) Predicted and estimated in-plane speed (vx2 + vz2)1/2, out-of-plane speed |vy|, and total speed v = (vx2 + vy2 + vz2)1/2 at three angles with respect to flow. When ψ = 0°, the imaging plane is orthogonal to the direction of flow except for a small component due to vertical tilt φ. At ψ = 90°, flow is predicted to be completely in plane. (c) Peak flow speed of the parabolic flow profile, estimated by non-linear least squared fitting to Poiseuille flow in a tube, as a function of ψ. The error here, a 95% confidence interval (CI), is estimated by calculating the Jacobian during non-linear fitting. (d) Lateral point spread function (PSF) width σxy as a function of axial location (ψ = 0).

Fig. 5
Fig. 5

Quantification of cilia-driven flow in X. tropicalis tadpole epithelium. (a) Maximum intensity projection image showing streaks traced out by particles, with primary direction of flow from head (h) to tail (t). Overlay of vector flow field estimated with PSV-OCT showing in-plane velocity (vx,vz) and direction indicated by length and direction of arrow, and out-of-plane speed |vy| indicated by size and color of circle placed at base of each arrow. Axes: h- head; t- tail; l-left, r-right. (b,c) inset of location of region of higher out-of-plane flow near the (a) head of the embryo and (b) tail of the embryo.

Fig. 6
Fig. 6

Quantification of cilia-driven flow in mouse trachea. (a) Maximum intensity projection of flow in tracheal lumen showing tail-head flow directed along the bottom and top surface, and recirculatory flow in the center of the trachea due to closed outflow. Overlay of vector flow field estimated with PSV-OCT showing in-plane velocity (vx,vz) and direction indicated by length and direction of arrow, and out-of-plane speed |vy| indicated by size and color of circle placed at base of each arrow. Axes: h- head; t- tail; d- dorsal; v- ventral. (b,c) Quantification of relative values of out-of-plane flow |vy| versus in-plane flow (vx2 + vz2)1/2. In two regions of interest (ROI), one near the surface of the tracheal wall (ROI 1, red) and one in the center of the lumen (ROI 2, green). (c) Histogram of ratio of values of |vy|/(vx2 + vz2)1/2 . Note the logarithmic scale. Values less than one indicate flows that are primarily in-plane, while values greater than 1 indicate flows that are primarily out-of-plane.

Fig. 7
Fig. 7

The correlation coefficients and root mean-square errors (with arbitrary intensity units) of all streak fits (prior to any goodness-of-fit filtering) for the tadpole data and the mouse trachea data. Ideally, the fits will be of low mean-square error and correlation coefficients close to 1. It is thus reassuring that the points in the figure are clustered close to the lower right corner.

Tables (1)

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Table 1 A comparison of velocimetry techniques in OCT

Equations (5)

Equations on this page are rendered with MathJax. Learn more.

image( r ) n=1 N [ ps f y ( y ) δ y ( y ) ] y= y n [ ps f r ( r ) δ r ( r r n ) ] .
r n ( t )= r 0,n + v r,n ×(t t 0 )
y n ( t )=| v y,n |(t t 0 ).
I( x,z,t )= I o exp( ( t t o ) 2 2 σ t 2 )×exp( ( x[ x o + v x (t t o ) ] ) 2 2 σ xy 2 ( z[ z o + v z (t t o ) ] ) 2 2 σ z 2 )+ I back
w( z )= w o 1+ [ λ( z z f ) π w o 2 ] 2

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