Focal loss analysis of nerve fiber layer reflectance for glaucoma diagnosis

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Abstract

Purpose: To evaluate nerve fiber layer (NFL) reflectance for glaucoma diagnosis. Methods: Participants were imaged with 4.5 × 4.5 mm volumetric disc scans using spectral-domain optical coherence tomography. The normalized NFL reflectance map was processed by an azimuthal filter to reduce directional reflectance bias caused by variation of beam incidence angle. The peripapillary area of the map was divided into 160 super pixels. Average reflectance was the mean of super pixel reflectance. Low reflectance super pixels were identified as those with NFL reflectance below the fifth percentile normative cutoff. Focal reflectance loss was measured by summing loss in low-reflectance super pixels. Results: Thirty-five normal, 30 preperimetric, and 35 perimetric glaucoma participants were enrolled. Azimuthal filtering improved the repeatability of the normalized NFL reflectance, as measured by the pooled super pixel standard deviation (SD), from 0.73 to 0.57 dB (P < 0.001, paired t-test) and reduced the population SD from 2.14 to 1.78 dB (P < 0.001, t-test). Most glaucomatous reflectance maps showed characteristic patterns of contiguous wedge or diffuse defects. Focal NFL reflectance loss had significantly higher diagnostic sensitivity than the best NFL thickness parameter (from map or profile): 77% versus 55% (P < 0.001) in glaucoma eyes with the specificity fixed at 99%. Conclusions: Azimuthal filtering reduces the variability of NFL reflectance measurements. Focal NFL reflectance loss has excellent glaucoma diagnostic accuracy compared to the standard NFL thickness parameters. The reflectance map may be useful for localizing NFL defects. Translational Relevance: The high diagnostic accuracy of NFL reflectance may make population-based screening feasible.

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Tan, O., Liu, L., You, Q., Wang, J., Chen, A., Ing, E., … Huang, D. (2021). Focal loss analysis of nerve fiber layer reflectance for glaucoma diagnosis. Translational Vision Science and Technology, 10(6). https://doi.org/10.1167/tvst.10.6.9

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