June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Population-based simulation using image quality metrics to predict visual acuity in pseudophakic patients implanted with trifocal IOLs
Author Affiliations & Notes
  • Lin He
    Alcon Laboratories Inc., Ft Worth, Texas, United States
  • Xin Hong
    Alcon Laboratories Inc., Ft Worth, Texas, United States
  • Ramesh Sarangapani
    Alcon Laboratories Inc., Ft Worth, Texas, United States
  • Footnotes
    Commercial Relationships   Lin He, Alcon Laboratories Inc. (E); Xin Hong, Alcon Laboratories Inc. (E); Ramesh Sarangapani, Alcon Laboratories Inc. (E)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 4209. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Lin He, Xin Hong, Ramesh Sarangapani; Population-based simulation using image quality metrics to predict visual acuity in pseudophakic patients implanted with trifocal IOLs. Invest. Ophthalmol. Vis. Sci. 2017;58(8):4209.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose : Visual acuity is an essential clinical endpoint to assess the effectiveness of intraocular lenses (IOLs) in clinical trials. Recent innovative trifocal IOLs have been shown to provide patients with great visual acuity at distance, intermediate and near. Population-based simulation of visual acuity at a range of defocuses (i.e., defocus curve) can help to optimize IOL optical design and guide clinical study design.

Methods : One-hundred virtual eyes have been generated using two-surface reduced eye model. Monte-Carlo approach was adopted by iterating different corneal power and aberration, anterior chamber depth and pupil size. The IOL surface was extracted from normative lens design in grid-sag format. Two major metrics, MTF area from 0 to 50 c/mm (MTFa50) and light-in-the-bucket (LIB), were calculated at varied defocuses from -3.5 D to +1.0D. The metric-visual acuity correlation was established using existing clinical data of a bifocal IOL. The correlation functions (MTFa50: a*x^b+c; LIB: a*log(LIB)+b) were applied to the new trifocal IOL to simulate its visual acuity and defocus curve.

Results : The LIB has shown better correlation with clinical binocular visual acuity than MTFa50 (R2=0.92 vs 0.64). The correlation functions were 6.7*x^(-0.047)-5.2 for the MTFa50 and -0.24*log(LIB)-0.20 for the LIB. Similar to the clinical binocular visual acuity, both MTFa50 and LIB metrics predicted the trifocal lens could provide visual acuity better than 0.12 logMAR from 0D (distance) to -2.5D (near) (Figure 1). Simulated difference (MTFa50-LIB) ranged from -0.07 to 0.03 logMAR, all within 1 line. MTFa50 predicted defocus curve was more smooth and monotonic without a distinct secondary peak. Bland-Altman analysis suggested that MTFa50 tended to predict worse visual acuity than LIB when the visual acuity was good and to predict better visual acuity when the visual acuity was poor (Figure 2).

Conclusions : The predicted visual acuity using population-based MTFa50 and LIB metrics have been generally consistent with each other. Calculating MTF area metric only up to 50 c/mm rather than traditional 100c/mm may act as a low-pass filter in predicting visual acuity and defocus curve.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

 

 

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×