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Imaging Oxygen Pressure in the Retina of the Mouse Eye

  • David F. Wilson
  • Sergei A. Vinogradov
  • Pavel Grosul
  • Akiko Kuroki
  • Jean Bennett
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 566)

Abstract

The phosphorescence lifetime imaging system previously used to image oxygen in the retina of the cat eye1 was modified to allow imaging of phosphorescence lifetimes in the much smaller mouse eye. Following the lead of Shonat and coworkers,2 a frequency domain approach was used in which the excitation light source was modulated in a 50% on:50% off square wave while the gate of the intensified CCD camera was similarly modulated but delayed with respect to the excitation. These were analyzed by fitting the intensity at each pixel to a sinusoid. The phase of the phosphorescence relative to the excitation was determined and from the phase shift and frequency, the phosphorescence lifetime was calculated. The Stern-Volmer relationship was then used to calculate the oxygen pressure at each pixel of the image array. High resolution maps of phosphorescence lifetime and oxygen pressure in the retina of the mouse eye have been attained. The retinal veins draining into the optic head appear as large, highly phosphorescent vessels against a lower phosphorescence background with a network of smaller vessels. The oxygen pressure in the retinal veins is typically from 20 to 30 mm Hg while the background has somewhat higher oxygen pressures. Experiments are underway to resolve the oxygen in the choroid from that in the retina. The arteries on the retinal surface can be observed, but their small diameter, relatively high oxygen pressures (> 90 mm Hg), and surrounding tissue with much lower oxygen pressures, makes accurate determination of the oxygen pressure a challenge.

Keywords

Phase Shift Oxygen Pressure Phase Delay Oxygen Measurement Mouse Retina 
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.

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Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • David F. Wilson
  • Sergei A. Vinogradov
  • Pavel Grosul
  • Akiko Kuroki
  • Jean Bennett

There are no affiliations available

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