June 2013
Volume 54, Issue 15
ARVO Annual Meeting Abstract  |   June 2013
Rapid, automated measurements of single cell blood velocity in the living eye
Author Affiliations & Notes
  • Jesse Schallek
    Center for Visual Science, University of Rochester, Rochester, NY
  • Christina Schwarz
    Laboratorio de Óptica, Universidad de Murcia, Murcia, Spain
  • David Williams
    Center for Visual Science, University of Rochester, Rochester, NY
    The Institute of Optics, University of Rochester, Rochester, NY
  • Footnotes
    Commercial Relationships Jesse Schallek, None; Christina Schwarz, AMO (F); David Williams, Bausch and Lomb (F), Polgenix (F), Canon (F), Welch Allyn (F), Pfizer (C), US 5,777,719 (P), US 5,949,521 (P), US 6,095,651 (P), US 6,379,005 (P), US 6,338,559 (P), US 6,264,328 (P), US 6,948,818 (P), US 7,416,305 B2 (P), US 6,199,986 (P), US 6,299,311 (P), US 6,827,444 (P), US 6,511,180 (P), US 8,226,236 (P), US DIV 13/461,880 (P)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 398. doi:https://doi.org/
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    • Get Citation

      Jesse Schallek, Christina Schwarz, David Williams; Rapid, automated measurements of single cell blood velocity in the living eye. Invest. Ophthalmol. Vis. Sci. 2013;54(15):398. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Adaptive optics has enabled non-invasive tracking of single blood cells in the living retina without the use of contrast agents. However, the time required to manually track millions of blood cells across the full range of vessel diameters is prohibitive. Here, we provide a method that automates single cell blood velocity measurement.


Mice, monkeys and humans were imaged with an adaptive optics scanning laser ophthalmoscope (AOSLO). A reflectance channel imaged at >75 Hz revealing single blood cells with 796 nm light. Raster scan videos were transformed into an image of cell displacement along a capillary path as a function of time. In this image, each blood cell appears as a diagonal line, with many such lines per image. The slope of each line reports cell velocity. To automate velocity detection, we used the Radon transform to rotate the space-time plot into a new image that shows intensity variation as a function of rotation angle. The angle with greatest contrast variance is proportional to cell velocity. This process was automated over large data sets containing thousands of vessels. The method was also applied to fast, 1-dimensional line scans across a vessel (~16 kHz) enabling sufficient bandwidth to track blood velocity in larger vessels.


1) Automated analysis could free-run on large data sets containing identified vessels from mice, monkeys and humans without user input, providing blood cell velocity with confidence intervals for each of thousands of capillary paths. 2) The algorithm was robust, providing results on >75% of all imaged vessels from anesthetized animals when using a stringent detection criterion of 2.5 standard deviations from mean Radon variance. Automated velocity measurements agreed with manual estimates (correlation coefficient>0.927). 3) The velocity bandwidth of our system identified cells moving from 1 to 4000 micron/sec in raster imaging mode and tens of mm/sec in line-scan mode. 4) Blood velocity showed dynamic variations associated flow of different blood cell types, cardiac pulsatility and laminar-like flow patterns.


High-spatiotemporal resolution imaging provides a measure of single blood cell velocity in the living eye. The bottleneck of blood cell speed determination has been overcome by a strategy that measures many blood cells facilitating the study of diseases that impact the retinal vasculature including capillaries.

Keywords: 436 blood supply • 549 image processing • 551 imaging/image analysis: non-clinical  

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