June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
What is the smallest change in visual acuity that is correlated with a change in image quality?
Author Affiliations & Notes
  • Ayeswarya Ravikumar
    College of Optometry, University of Houston, Houston, TX
  • Jason Marsack
    College of Optometry, University of Houston, Houston, TX
  • Yue Shi
    College of Optometry, University of Houston, Houston, TX
  • Raymond Applegate
    College of Optometry, University of Houston, Houston, TX
  • Footnotes
    Commercial Relationships Ayeswarya Ravikumar, None; Jason Marsack, University of Houston (P); Yue Shi, None; Raymond Applegate, University of Houston (P)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 1283. doi:
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      Ayeswarya Ravikumar, Jason Marsack, Yue Shi, Raymond Applegate; What is the smallest change in visual acuity that is correlated with a change in image quality?. Invest. Ophthalmol. Vis. Sci. 2013;54(15):1283.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract
 
Purpose
 

The 95% confidence interval for logMAR acuity testing is ~ ±5 letters (±1 line)[ Optom Vis Sci, 1998. 75(5): 342]. Metrics of image quality (IQ) can quantify 6 just noticeable differences in blur before 1 line of acuity is lost [J Cataract Refract Surg, 2011. 37(8): 1523] and visual acuity (VA) is highly correlated (R2> 0.8) to change in metrics of image quality when inducing several lines of change in acuity. However, the correlation between acuity and metric values is not known as the change in acuity approaches the test re-test reliability of acuity. The purpose here is to determine how the coefficient of determination (R2 ) between change in VA and change in IQ metric/s value changes as the range is decreased from 8 lines to the test re-test reliability of acuity measurement (1 line).

 
Methods
 

The Thibos virtual eye generator [Opththalmic Physiol opt, 2009;29(3):288-291] was used to generate 60 WFEs whose log visual Strehl ratio varied between 0.0 and -1.8 in approximately equal steps. For each of the 60 WFEs, three unique logMAR acuity charts were generated (0.8 to -0.3 logMAR). For each chart, VA was measured up to the 5th letter missed for each of 3 normal subjects. Change in visual acuity was linearly regressed against change in 29 IQ metrics to determine the coefficient of determination when the change in visual acuity was decreased in 1 line steps from 8 lines to 1 line.

 
Results
 

See Figure. Extrapolation from the fitted line shows that R2 goes to zero at ~3 letters (0.6 of a line), which is below the test retest reliability for acuity measurement (1 line). With a 2 line loss (or one line above the 95% confidence level for the measurement of acuity), 42% of the variance in acuity is accounted for by the metrics. The function begins to asymptote between 4 and 5 lines, and little improvement in correlation is seen with additional lines of acuity change.

 
Conclusions
 

Image quality metrics can detect blur prior to an acuity loss (prior work), begin to be correlated with acuity below the re-test reliability for acuity testing and reach a maximum R 2 > 0.8. These two experimental observations suggest that IQ metrics can serve as an objective tool for evaluating and designing therapy intended to improve visual performance, and that correlations are primarily limited by the acuity task and not by the IQ metrics.

  
Keywords: 754 visual acuity • 626 aberrations  
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