Image Quality Metric Derived Refractions Predicted to Improve Visual Acuity Beyond Habitual Refraction for Patients With Down Syndrome

Ravikumar, Ayeswarya; Benoit, Julia S.; Marsack, Jason D; Anderson, Heather A

doi:10.1167/tvst.8.3.20

Cited by 13 publications

(26 citation statements)

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“…Ravikumar, Sarver, & Applegate, 2012;Shi, Applegate, Wei, Ravikumar, & Bedell, 2013a). Being more robust than measures such as residual diopters or root mean square (RMS) wavefront error in tracking visual performance (Cheng, Bradley, Ravikumar, & Thibos, 2010;Marsack, Thibos, & Applegate, 2004), visual image quality metrics have proven useful for optimizing objective refractions (Hastings, Marsack, Nguyen, Cheng, & Applegate, 2017;Martin, Vasudevan, Himebaugh, Bradley, & Thibos, 2011;A. Ravikumar, Benoit, Marsack, & Anderson, 2019) and have served as a benchmark for comparing both individualized and conventional ophthalmic corrections across modalities (unaided, spectacles, contact lenses) (Hastings, Applegate, Nguyen, Kauffman, Hemmati, & Marsack, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Serving as objective surrogates for subjective measures of visual performance, visual image quality metrics have been employed in the study of accommodation ( López-Gil, Martin, Liu, Bradley, Díaz-Muñoz, & Thibos, 2013 ), myopia ( Collins, Buehren, & Iskander, 2006 ), postnatal visual development ( Candy, Wang, & Ravikumar, 2009 ), and refractive surgery outcomes ( Bühren, Yoon, MacRae, & Huxlin, 2010 ), as well as in applications of extended depth of focus ( Yi, Iskander, & Collins, 2010 ), eye models ( Liu & Thibos, 2019 ), the design of intraocular lenses ( Bonaque-González, Ríos, Amigó, & López-Gil, 2015 ), and predicting changes in visual performance ( A. Ravikumar, Sarver, & Applegate, 2012 ; Shi, Applegate, Wei, Ravikumar, & Bedell, 2013a ). Being more robust than measures such as residual diopters or root mean square (RMS) wavefront error in tracking visual performance ( Cheng, Bradley, Ravikumar, & Thibos, 2010 ; Marsack, Thibos, & Applegate, 2004 ), visual image quality metrics have proven useful for optimizing objective refractions ( Hastings, Marsack, Nguyen, Cheng, & Applegate, 2017 ; Martin, Vasudevan, Himebaugh, Bradley, & Thibos, 2011 ; A. Ravikumar, Benoit, Marsack, & Anderson, 2019 ) and have served as a benchmark for comparing both individualized and conventional ophthalmic corrections across modalities (unaided, spectacles, contact lenses) ( Hastings, Applegate, Nguyen, Kauffman, Hemmati, & Marsack, 2019 ). Although the visual tasks, pupil sizes, and ages have differed across these applications, a constant neural component has typically been used in the visual image quality metrics.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Methods used to generate the simulated acuity charts have been previously reported. 28 To reduce the number of conditions evaluated, charts from metrics that were identified as consistently yielding poor-quality acuity charts were eliminated, as well as any charts from redundant refractions whereby multiple metrics identified the same optimized refraction for a given eye. This methodology has been described in detail elsewhere.…”

Section: Prescription Derivation and Chart Simulationmentioning

confidence: 99%

“…This methodology has been described in detail elsewhere. 28 Experimental acuity charts were also created based on applying refractions derived from WAM-5500 Grand Seiko (RyuSyo Industrial Co., Hiroshima, Japan) autorefraction (AutoRef) measures, each patient's habitual refraction (based on lensometry of their current glasses or plano refraction if they presented unaided), and a theoretical zeroing of all lower-order aberrations (LOAZ). Thus, for this study, a total of 669 unique experimental charts were viewed (out of a possible 1140) and graded (including unique derived prescriptions + habitual + AutoRef + LOAZ).…”

Section: Prescription Derivation and Chart Simulationmentioning

confidence: 99%

Understanding the Impact of Individual Perceived Image Quality Features on Visual Performance

Benoit

Ravikumar

Marsack

et al. 2020

Trans. Vis. Sci. Tech.

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Citation: Benoit JS, Ravikumar A, Marsack JD, Anderson HA. Understanding the impact of individual perceived image quality features on visual performance. Trans Vis Sci Tech. 2020;9(5):7, https://doi.org/10. 1167/tvst.9.5.7 Purpose: This study aimed to quantify the impact of blur, contrast, and ghosting on perceived overall image quality (IQ) as well as resultant predicted visual acuity, utilizing simulated acuity charts from objective refraction among eyes of individuals with Down syndrome (DS).Methods: Acuity charts were produced, simulating the retinal image when applying 16 different metric-derived sphero-cylindrical refractions for each eye of 30 adult patients with DS. Fourteen dilated adult observers (normal vision) viewed subsets of logMAR acuity charts displayed on an LCD monitor monocularly through a unit magnification 3-mm aperture telescope. Observers rated features blur, ghosting, and contrast on 10-point scales (10 = poorest) and overall IQ on a 0-to 100-point scale (100 = best) and read each chart until five total letters were missed (logMAR technique). Mixed modeling was used to estimate feature influence on overall perceived IQ and relative acuity (compared with an unaberrated chart), separately. Results:Perceived IQ spanned the entire scale (mean = 59 ± 22) and average reduction in relative acuity was two lines (0.2 ± 0.14 logMAR). Perceived blur, ghosting, and contrast were individually correlated with overall IQ and relative acuity. Blur, contrast, and ghosting exert unique effects on overall perceived IQ (P < 0.05). Blur (b = -.009, P < 0.001) and ghosting (b = -.003, P < 0.001) influence relative acuity over and beyond their effects on overall IQ (b = .001, P < 0.0001) and contrast. Conclusions:Objectively identified refractions would ideally provide high contrast, low blur, and low ghosting. These data suggest that blur and ghosting may be given priority over contrast when improving acuity is the goal.Translational Relevance: Findings may guide objective refraction in clinical care.

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“…A detailed description of the method employed here has been published previously. 27,31 In brief, monocular visual acuity was recorded for the simulated logMAR acuity charts through a custom MATLAB program using a letter-by-letter scoring system (subjects start with the line that they are able to most easily read all five letters correctly, and credit was given for each letter read correctly up to the fifth miss). 32 The charts were digitally displayed on a high contrast, gamma corrected, 11-bit depth monitor, which the subject viewed while dilated through a unit magnification telescope with a 3.0-mm pupil aperture conjugate to the pupil plane of the eye.…”

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Visual interaction of 2nd to 5th order Zernike aberration terms with vertical coma

Ravikumar

Hastings

et al. 2020

Ophthalmic Physiologic Optic

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Purpose In order to better understand the optical consequence of residual aberrations during conventional rigid contact lens wear in keratoconus, this study aimed to quantify the visual interaction between positive vertical coma (C(3, −1)) and other individual 2nd to 5th radial order Zernike aberration terms. Methods The experiment proceeded in two parts. First, two levels of C(3, −1) (target term) were simulated. Individual Zernike aberration terms from the 2nd to 5th radial orders (test terms) were combined in 0.05‐µm steps a) from −2.00 µm to +2.00 µm with +1.00 µm of C(3, −1) and b) from −1.00 µm to +1.00 µm with +0.50 µm of C(3, −1). The resulting combinations were used to calculate the logarithm of the visual Strehl ratio (logVSX) and predict the relative beneficial or deleterious impact of the interaction. Second, for test terms where an interaction was predicted to provide more than a 0.25 logVSX benefit compared to C(3, −1) alone, high contrast logMAR acuity charts were constructed (simulating the manner in which the test + target term combinations would impact the retinal image of the chart), and randomly read by three well‐corrected, typically‐sighted individuals through a 3.0‐mm diameter artificial pupil. Results When combined with positive C(3, −1), C(3, −3), C(4, −4), C(5, −5), C(5, −3), and C(5, −1) exhibited better visual image quality compared with C(3, −1) alone. Ratios of the test terms to target term providing maximal benefit remained constant for both +0.50 µm and +1.00 µm of C(3, −1). C(3, −3) and C(5, −1) had the largest predicted beneficial effect, with the maximal effect for +1.00 µm of C(3, −1) occurring with +0.35 µm of C(5, −1) and −1.00 µm of C(3, −3). When individuals read letter charts convolved with the point spread function derived from C(3, −1) combined with C(3, −3) and C(3, −1) combined with C(5, −1), the maximal beneficial effect was 0.27 logMAR (13.5 letters) for C(3, −3) and 0.36 logMAR (18 letters) for C(5, −1). Conclusions While most interactions reduced visual image quality, combinations of C(3, −3) (vertical trefoil) and C(5, −1) (vertical secondary coma) provided a clinically relevant beneficial effect in the presence of C(3, −1) (vertical coma) which was demonstrated in both through‐focus simulation and chart reading tests. Future work will examine whether these effects persist in the presence of the entire spectrum of residual aberrations seen in the eyes of individuals with keratoconus.

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Image Quality Metric Derived Refractions Predicted to Improve Visual Acuity Beyond Habitual Refraction for Patients With Down Syndrome

Cited by 13 publications

References 31 publications

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

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

Understanding the Impact of Individual Perceived Image Quality Features on Visual Performance

Visual interaction of 2nd to 5th order Zernike aberration terms with vertical coma

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