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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)
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).
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.
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.
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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