Combining optical and neural components in physiological visual image quality metrics as functions of luminance and age

Abstract

Visual image quality metrics combine comprehensive descriptions of ocular optics (from wavefront error) with a measure of the neural processing of the visual system (neural contrast sensitivity). To improve the ability of these metrics to track real-world changes in visual performance and to investigate the roles and interactions of those optical and neural components in foveal visual image quality as functions of age and target luminance, models of neural contrast sensitivity were constructed from the literature as functions of (1) retinal illuminance (Trolands, td), and (2) retinal illuminance and age. These models were then incorporated into calculation of the visual Strehl ratio (VSX). Best-corrected VSX values were determined at physiological pupil sizes over target luminances of 10 4 to 10 −3 cd/m 2 for 146 eyes spanning six decades of age. Optical and neural components of the metrics interact and contribute to visual image quality in three ways. At target luminances resulting in >900 td at physiological pupil size, neural processing is constant, and only aberrations (that change as pupil size changes with luminance) affect the metric. At low mesopic luminances below where pupil size asymptotes to maximum, optics are constant (maximum pupil), and only the neural component changes with luminance. Between these two levels, both optical and neural components of the metrics are affected by changes in target luminance. The model that accounted for both retinal illuminance and age allowed VSX, termed VSX(td,a), to best track visual acuity trends (measured at 160 and 200 cd/m 2) as a function of age (20s through 70s) from the literature. Best-corrected VSX(td,a) decreased by 2.24 log units between maximum and minimum target luminances in the youngest eyes and by 2.58 log units in the oldest. The decrease due to age was more gradual at high target luminances (0.70 log units) and more pronounced as target luminance decreased (1.04 log units).

Resolution acuity across the visual field for mesopic and scotopic illumination

“…If so, those classic sensitivity functions represent the optimum detection performance achievable for that frequency when eccentricity is optimized, which might account for major differences compared to contrast sensitivity functions obtained at a fixed retinal eccentricity (Thibos et al, 1996). Such an interpretation has implications for phenomenological models of vision based on published data (Rovamo, Mustonen, & Nasanen, 1994) and on the applications of those models to clinical and basic visual science (Hastings, Marsack, Thibos, & Applegate, 2020;Xu, Wang, Thibos, & Bradley, 2017).…”

Clinical applications of personalising the neural components of visual image quality metrics for individual eyes

“…39 Presently, we limited the comparison to two neural functions at the same retinal illuminance because, in reality, a change in retinal illuminance is inevitably accompanied by a change in physiological pupil size, which varies the aberrations being experienced. 33…”

“… Refraction “Own neural” was determined using the personalised (measured) nCSF in the VSX metric; refraction “Model neural” was determined using a model 33 of the nCSF of typical eyes at the same retinal illuminance. The Euclidian dioptric distance between refractions was calculated via conversion to power vectors 45 …”

“…Two versions of the VSX metric were calculated for each of the six keratoconic eyes. One metric version used the measured neural contrast sensitivity function of the eye, and the other used the generic neural function from a model of typical eyes at the same retinal illuminance 33 . In both of these calculations, wavefront error measured while wearing a wavefront‐guided scleral lens was used.…”

“…The VSX metric was calculated for each resultant (corrected) wavefront error and the corrections were ranked in terms of their VSX values to identify the one that optimised the metric. This search process was repeated for two calculations of the metric: one that used a neural weighting function specific to each individual eye, and another that used a generic neural weighting function from a model of typical healthy eyes at the same retinal illuminance 33 …”