June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Validation of model based hyperspectral retinal oximetry algorithms using systemic gas provocations in healthy individuals
Author Affiliations & Notes
  • Susith Kulasekara
    Ophthalmology & Vision Sciences, University of Toronto, Toronto, ON, Canada
  • Kalpana Rose
    School of Optometry and Vision Science, University of Waterloo, Waterloo, ON, Canada
  • Michèle Desjardins
    Institut de génie biomédical, École Polytechnique de Montréal, Montréal, QC, Canada
  • Reza Jafari
    Optina Diagnostics, Montreal, QC, Canada
  • J Daniel Arbour
    Optina Diagnostics, Montreal, QC, Canada
    Department of Ophthalmology, University of Montreal, Montreal, QC, Canada
  • Frédéric Lesage
    Institut de génie biomédical, École Polytechnique de Montréal, Montréal, QC, Canada
  • Jean-Philippe Sylvestre
    Optina Diagnostics, Montreal, QC, Canada
  • Christopher Hudson
    Ophthalmology & Vision Sciences, University of Toronto, Toronto, ON, Canada
    School of Optometry and Vision Science, University of Waterloo, Waterloo, ON, Canada
  • Footnotes
    Commercial Relationships Susith Kulasekara, None; Kalpana Rose, None; Michèle Desjardins, None; Reza Jafari, Optina Diagnostics (E); J Daniel Arbour, Optina Diagnostics (I); Frédéric Lesage, None; Jean-Philippe Sylvestre, Optina Diagnostics (E), Optina Diagnostics (I); Christopher Hudson, Optina Diagnostics (F), Thornhill Research Inc. (P)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 3317. doi:
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      Susith Kulasekara, Kalpana Rose, Michèle Desjardins, Reza Jafari, J Daniel Arbour, Frédéric Lesage, Jean-Philippe Sylvestre, Christopher Hudson; Validation of model based hyperspectral retinal oximetry algorithms using systemic gas provocations in healthy individuals. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):3317.

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

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Abstract

Purpose: Retina is a highly metabolically active tissue with a very high demand for oxygen. Dysregulation of retinal oxygen supply and demand is associated with many ocular and systemic diseases. Changes in retinal tissue oxygen tension may take place before these changes are reflected in retinal vessels. The purpose of this study is to validate the use of a model based on a hyperspectral algorithm for measuring retinal tissue oxygen saturation (tSO2).

Methods: One eye of 12 healthy non-smoking volunteers was chosen for the study. End-tidal O2 concentration (PETO2) was adjusted using a model based prospective targeting device (RespirAct) to induce normoxia (PETO2=100mmHg), hypoxia (PETO2=50mmHg) and hyperoxia (PETO2=300mmHg), while maintaining normocarbia. The order of hyperoxia and hypoxia was randomized between subjects. Heart rate, blood pressure, and finger pulse oximetry were monitored throughout. A prototype metabolic hyperspectral retinal camera (MHRC, Optina Diagnostics) was used to image the fundus from 500-600nm in 5nm steps (3 repeats per condition) after stabilization of finger pulse oximetry for over 3 min. The reflectance intensity data was fit in MATLAB to a model where oxy- and deoxyhemoglobin are the main absorbers and scattering is modeled by a log(1/ wavelength) term. The fitted parameters were used to extract an estimation of tSO2 and total hemoglobin content (HbT) in each pixel of the images and values obtained in the different PETO2 conditions were compared for a region of the retina, free of any visible blood vessels, at a half disc diameter distance from the disc margin.

Results: The preliminary results show that as the breathing air PETO2 was increased from normoxia to hyperoxia tSO2 significantly increased (p=0.001) from 41%(+11) to 53%(+10). Lowering the PETO2 from normoxia to hypoxia significantly decreased (p=0.001) tSO2 from 41%(+11) to 34%(+14). The mean HbT at hypoxia, normoxia, and hyperoxia, were not significantly different (p=0.3) from each other: 2.8(+0.7); 2.5(+0.6); 2.4(+0.8). However, there was a trend towards an increase in HbT in hypoxia.

Conclusions: As the breathing air oxygen composition ( PETO2) is changed from normoxia to hypoxia and hyperoxia, retinal tissue oxygen saturation (tSO2) measurements, based on a hyperspectral algorithm, showed parallel changes, suggesting that the method could be used to monitor the health of the retina.

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