Hyperspectral Image Analysis for Oxygen Saturation Automated Localization of the Eye

saturation readings were obtained using a blood gas analyzer (Instrumentation Laboratory, Lexington, MA, USA) with a hemoglobin saturation readout. Suspensions were drawn into 0.2-mm path length, flat capillary tubes (Vitro Dynamics, Rockaway, NJ, USA), sealed on both ends with wax, and hyperspectral images of the samples were recorded. The spectral curve from the red-cell samples were compared with those obtained from hyperspectral recordings of retinal blood during room air and pure oxygen breathings, and during elevated IOP, using the spectral transform methods described above.

4.3.2. Results

4.3.2.1.Calibrated red-cell samples and retinal blood under controlled conditions

In Fig. 4.14, a vertical line marks the wavelength (561.5 nm) of maximum amplitude change between the curves. Saturations of all samples were determined at this wavelength by the method of Hammer,66 assuming a value of 100% for the arterial sample during pure oxygen and 0% for the deoxygenated red cells. Red cells reduced with Na-dithionate yielded a hemoglobin saturation reading slightly below zero; this number was replaced by the physically realizable 0%. Table 4.3 gives the saturations of red-cell and retinal-vessel samples that were found by determining the value of the sample curve at the vertical line (Fig. 4.14) as a percentage of the difference between the 100% curve and the 0% curve. For the oxygenated red cells, the value agrees with that of the oximeter to within 1%.

4.3.2.2. Oxygen saturation of the ONH

Figure 4.15 contains a representation of typical regions of the ONH in one monkey. Table 4.4 shows values obtained for percentage oxygen saturation within the separate regions of the ONH and overlying retinal vessels at 10 mmHg and 55 mmHg. At 55 mmHg, perfusion pressure was near zero. RA and vein saturation were reduced, respectively, by 35.7% and 6.5% saturation units at the higher pressure. Of the ONH structures, the temporal cup showed the highest saturation at both low and high pressure (77.3 ± 1.0%, 10 mmHg; 60.1 ± 4.0%, 55 mmHg), and the smallest reduction in saturation at high pressure (22.3%) compared with the average reduction

151

Bahram Khoobehi and James M. Beach

Fig. 4.14. Spectral curves from blood samples: artery (pure O2) ; red cells (room air) +; artery (room air) ♦; vein (room air) ; vein (60 mmHg IOP) ˆ; and red cells (Na-dithionate) . End value wavelengths are isosbestic points (522.9 nm, 569.2 nm). Curves cross at a third isosbestic point (548.0 nm). The curves were aligned as described in Sec. 4.3.1. Comparison of relative positions of the sample curves at the vertical line (561.5 nm) was used to determine saturations in Table 4.3 at a single wavelength. Curve fits to linear combinations of artery (pure O2) and red cells (dithionate) curves (25 wavelengths) were used to determine vessel and ONH percentage saturation (Table 4.4). Reprinted with permission from Beach, J., Ning, J., and Khoobehi, B. Oxygen saturation in optic nerve head structures by hyperspectral image analysis. Curr Eye Res 32:161–70, 2007. ©2007 Informa Medical and Pharmaceutical Science.

in saturation over the ONH of 39%. At high pressure, the saturation of the temporal cup was 43.1% greater than the average ONH saturation. The saturation findings were not significantly different across monkeys.

In Fig. 4.16, IOP was set to 10, 30, 45, and 55 mmHg. OSC values near the high end of the saturation scale, which represent artery points, all fall well above the line. Although three regression lines had similar slopes, the fourth slope was different. The average regression slope, over all IOPs, was (mean ± SD, n = 4) 0.2169 ± 0.054. The goodness-of-fit ranged between 0.2 and 0.74, and the coefficient of variation (SD/mean) was 0.253. These results indicate that OSC does not bear a linear relationship with saturation. After correction for blood volume (right panel), all points fall along straight lines. Over the range of saturation established at the four IOPs, all slopes had similar values. The average regression slope was (mean ± SD, n = 4)

152

Hyperspectral Image Analysis for Oxygen Saturation Automated Localization of the Eye

Table 4.3. Oxygen saturation in retinal vessels and calibrated red cells.

aMethod of Hammer et al.34 using one measurement wavelength.

bLeast-squares fit to calibrated red cell sample with 25 wavelengths.

cFrom oxygen saturation readout.

Note: Reprinted with permission from Beach, J., Ning, J., and Khoobehi, B. Oxygen saturation in optic nerve head structures by hyperspectral image analysis. Curr Eye Res 32:161–70, 2007. ©2007 Informa Medical and Pharmaceutical Science.

1.3639± 0.0819. The goodness of the fit ranged between 0.74 and 0.98, and the coefficient of variation was 0.06. The linear regression of RSIv against the percentage saturation, where both quantities are obtained from the same hyperspectral data, defines the calibration of the saturation map.

Figure 4.17 shows color-coded oxygen saturation maps of the ONH and vessels from one monkey at 10 mmHg (left panels) and 55 mmHg (right panels). The color scale is the same for both IOPs. In the upper row at 10 mmHg, the OSC map shows distinctly red-yellow codes for arteries. Veins and ONH tissue are similar (cyan-blue), and the veins are not clearly visible. At 55 mmHg, color codes of all structures correspond with lower saturation. Veins are visible as blue codes. In the middle row, maps of the BVC are shown. At 10 mmHg, volume is highest in arteries and veins (redyellow codes) and lowest in the ONH (green-cyan codes). At 55 mmHg, all structures show less blood volume. Horizontal patterns of red and yellow in arteries represent volume changes during pulsatile blood flow as the image is scanned vertically. Pulsation is not clearly visible in veins.

Saturation maps of RSIv after blood volume correction are shown in the bottom row. At 10 mmHg, veins (cyan) are distinguishable from areas

153