Skip Navigation LinksHome > October 2013 - Volume 90 - Issue 10 > Predicting Through-Focus Visual Acuity with the Eye’s Natura...
Optometry & Vision Science:
doi: 10.1097/OPX.0000000000000031
Original Articles

Predicting Through-Focus Visual Acuity with the Eye’s Natural Aberrations

Kingston, Amanda C.*; Cox, Ian G.

Free Access
Article Outline
Collapse Box

Author Information

*BS, MS, CCOA

BOptom, PhD, FAAO

Bausch and Lomb, Rochester, New York (ACK, IGC); and University of Rochester, Rochester, New York (ACK).

Amanda C. Kingston Bausch and Lomb 1400 N. Goodman Street Rochester, NY 14609 e-mail: Amanda.C.Kingston@bausch.com

Collapse Box

Abstract

Purpose

To develop a predictive optical modeling process that utilizes individual computer eye models along with a novel through-focus image quality metric.

Methods

Individual eye models were implemented in optical design software (Zemax, Bellevue, WA) based on evaluation of ocular aberrations, pupil diameter, visual acuity, and accommodative response of 90 subjects (180 eyes; 24–63 years of age). Monocular high-contrast minimum angle of resolution (logMAR) acuity was assessed at 6 m, 2 m, 1 m, 67 cm, 50 cm, 40 cm, 33 cm, 28 cm, and 25 cm. While the subject fixated on the lowest readable line of acuity, total ocular aberrations and pupil diameter were measured three times each using the Complete Ophthalmic Analysis System (COAS HD VR) at each distance. A subset of 64 mature presbyopic eyes was used to predict the clinical logMAR acuity performance of five novel multifocal contact lens designs. To validate predictability of the design process, designs were manufactured and tested clinically on a population of 24 mature presbyopes (having at least +1.50 D spectacle add at 40 cm). Seven object distances were used in the validation study (6 m, 2 m, 1 m, 67 cm, 50 cm, 40 cm, and 25 cm) to measure monocular high-contrast logMAR acuity.

Results

Baseline clinical through-focus logMAR was shown to correlate highly (R2 = 0.85) with predicted logMAR from individual eye models. At all object distances, each of the five multifocal lenses showed less than one line difference, on average, between predicted and clinical normalized logMAR acuity. Correlation showed R2 between 0.90 and 0.97 for all multifocal designs.

Conclusions

Computer-based models that account for patient’s aberrations, pupil diameter changes, and accommodative amplitude can be used to predict the performance of contact lens designs. With this high correlation (R2 ≥ 0.90) and high level of predictability, more design options can be explored in the computer to optimize performance before a lens is manufactured and tested clinically.

The unique ocular aberrations and biometry of an individual’s eye play a large role in defining its retinal image quality.1–5 Large strides have been made in ocular measurement systems that are enabling the optical industry to create newer and better ways to predict an individual eye’s subjective image quality. The ultimate goal is to find a single-value metric that is highly correlated with visual quality over a broad range of aberrations and object distances.6 With a suitable single-value metric, a person’s aberrations could be measured and the most appropriate corrective lens selected using these individual modeled results. This could significantly improve the overall visual outcomes of individuals as well as the general efficiency of clinical studies through optimization of the design process.

Computer eye models have been utilized for decades to represent properties of the human eye in terms of ocular aberrations, corneal curvature, anterior chamber depth (ACD), and crystalline lens characteristics.7–10 These eye models are typically based on average anatomic data and rely on certain simplifications such as spherical lens surfaces or a crystalline lens with a single index of refraction.11 Also, current eye models only take into account rotationally symmetric aberrations such as spherical aberration and utilize average population values which are known to vary by age as well as have high individual variation.11 There are only a few models which also incorporate the role of accommodation, and they assume that paraxial best focus is maintained at the retinal plane while a person accommodates.12–15 Given the diversity in people’s accommodative amplitude, depth of focus, and change in aberrations and pupil diameter as a function of object distance, the current models fail to predict the true accommodative response. Individually modeling eyes allows one to predict what the image quality would be for the specific person. This not only helps to understand how a given diverse population would benefit from a certain lens design but it also allows for a better understanding of which aberrations most influence through-focus visual acuity and would be most beneficially corrected.

Equally important to creating individual eye models is to have a metric that can determine predicted retinal image quality. A number of optical metrics have been presented previously to develop a relationship between wavefront aberrations and predicted subjective image quality.16–22 These metrics have primarily been used to correlate with best subjective refraction or clinical acuity at distance.23–25 Very few studies have looked at the correlation of clinical measurements, such as visual acuity, with predicted image quality at multiple object distances and over a large range of natural aberrations and pupil diameters.20,26 Despite the proliferation of wavefront sensors to fully characterize the optical quality of individual eyes at distance and as a function of accommodation, there is not yet a single-value metric that is highly correlated with an individual’s through-focus clinical acuity. There is still uncertainty about how aberrations interact with one another as well as their impact on through-focus retinal image quality.27–29

In general, metrics of optical quality can be divided into two standard approaches: pupil plane metrics and image plane metrics. Pupil plane metrics are defined by the quality of the shape of the wave aberrations in the pupil plane, whereas image plane metrics utilize the point spread function (PFS) or optical transfer function. Work done by Marsack et al in 2004 looked at the change in measured visual acuity and its correlation with 31 image quality metrics proposed in 2004 by Thibos et al.22,24 It was found that visual Strehl optical transfer function was highly correlated with the amount of letters lost in visual acuity at distance (R2 = 0.81). Cheng et al (2004) looked at the same 31 image quality metrics and showed only 3 gave an R2 value ≥0.70 (PFSt, VSMTF, and NS) when looking at the through-focus correlation in the presence of higher-order monochromatic aberrations.25 Both studies used computationally aberrated visual acuity charts and artificial pupil diameters when assessing clinical acuity.

The study presented by Legras et al, which also dealt with the prediction of through-focus visual acuity, showed a reasonable correlation with metrics based on rMTF (R2 between 0.57 and 0.67), whereas optical transfer function-based metrics were less predictive of changes in through-focus acuity (R2 ≤ 0.42).26 A more recent study completed by Ravikumar et al found high correlation to distance visual acuity when taking the log of image quality metrics presented by Thibos et al; specifically logNS, logVSX, logVSMTF, and logPFSt gave R2 ≥0.86.19,20,24

These previous studies show good correlation to distance visual acuity but still present a need for a through-focus image quality metric that takes into account the eye’s natural aberrations, pupil diameter, and residual accommodative amplitude. The goal of this work was to develop a new image quality metric that is highly correlated with through-focus visual acuity and can be easily related to clinical measurements. The image quality metric, in conjunction with individual computer eye models, will provide a more efficient and predictive tool for determining through-focus performance of ophthalmic lens designs.

Back to Top | Article Outline

METHODS

Clinical Study Design for Baseline Eye Models

The study followed the tenets of the Declaration of Helsinki and was approved by the Southwest Independent Institutional Review Board (Fort Worth, TX). Each subject signed an informed consent form following an explicit description of the study.

In order to create individual eye models and develop a retinal image quality metric, 90 subjects (180 eyes; age range, 24–63 years of age) were enrolled in a non-randomized, bilateral, unmasked evaluation of ocular aberrations, pupil diameter, visual acuity, and accommodative response. For the purpose of this paper, a subset of 64 eyes was used to correlate predicted logarithm of the minimum angle of resolution (logMAR) with measured through-focus visual acuity. These subjects were identified as mature presbyopes based on their measured through-focus visual acuity results (>0.30 logMAR at 2.5 D [40 cm] through 4.0 D [25 cm]) and were chosen because one of the most impactful applications of this design process is around multifocal correction for presbyopic eyes.

To be eligible for entry into the study, all subjects needed to have no greater than 0.75 D of refractive cylinder, a physiologically normal anterior segment, no active ocular disease, and not be using any topical ocular medications. The cylinder requirement was imposed in order to ensure accurate measurement of through-focus wavefront aberrations using the Complete Ophthalmic Analysis System (COAS HD VR; AMO WaveFront Sciences, Albuquerque, NM).30 A best distance spherical equivalent refraction was performed using trial spectacle lenses in a trial frame. To minimize the reflections from the trial lens, subjects were instructed to tip their heads slightly.20 Head tilt with a cylindrical lens would induce unwanted off-axis aberrations; therefore, only subjects with little to no refractive cylinder were included. The anterior and posterior corneal radius, corneal aberrations, and corneal thickness of each eye were measured using the Bausch + Lomb Orbscan IIz corneal topographer (Rochester, NY). ACD and axial length were measured using the Carl Zeiss Meditec IOL Master (Oberkochen, Germany).

High-contrast visual acuity was assessed under conditions of typical room illumination (360 lumens) at 6 m, 2 m, 1 m, 67 cm, 50 cm, 40 cm, 33 cm, 28 cm, and 25 cm using an ETDRS eye chart designed for 40 cm presentation (Precision Vision, LaSalle, IL). All visual acuity results were corrected to account for the non-standard test distances used in this study. While the subject fixated on the lowest readable line, three separate measurements of total ocular aberrations and pupil diameter were performed using the COAS HD VR aberrometer for each object distance.

The COAS is an open-field-of-view Shack-Hartmann wavefront sensor that provides a real-time display of the pupil image. The COAS utilizes an 840 nm wavelength super-luminescent diode as the light source and an array of 33 × 44 square lenslets with each lenslet having a width of 144 μm. The wavefront data generated from the lenslet array images was output as a set of OSA format Zernike coefficients (up to sixth order) that quantify the type and magnitude of aberrations present.31 All measurements were performed with natural pupils under a room illumination of 360 lumens. Each eye was tested monocularly, with the fellow eye occluded.

Back to Top | Article Outline
Designing Individual Computer Eye Models

Commercially available optical design software (Zemax) was used to develop the individual computer eye models. The anterior and posterior corneal radius and corneal thickness were set to the measured values from the Orbscan IIz corneal topographer. Each corneal topography elevation map was fit with Zernike coefficients and imported onto the anterior and posterior corneal surfaces in Zemax. The ACD and axial length were assigned the biometry values measured on the IOL Master, and all indices of refraction were defined by the baseline Arizona eye model (Table 1).32

TABLE 1
TABLE 1
Image Tools

In order to match the appropriate measured total ocular aberrations for a particular eye for each testing distance, a Zernike surface was overlaid on the crystalline lens within the model, the pupil diameter was matched to the measured value, and the eye was optimized accordingly. The pupil was centered relative to the optical axis and located in front of the crystalline lens anterior surface. During the optimization process, the Zernike surface was allowed to vary until the total ocular aberrations of the Zemax file were equal to the measured values from the COAS HD VR aberrometer. The addition of the Zernike surface circumvents the fact that technology does not currently exist to measure the wavefront aberration contribution of the crystalline lens directly. Use of a Zernike surface on the crystalline lens anterior surface allowed the indirect deduction of the lenticular contribution to the total aberrations of the eye.33 This process was repeated for all nine measured object distances resulting in an individual eye model representing each eye’s wavefront aberration and pupil size through the full measured focus range.

Back to Top | Article Outline
Predicted logMAR Image Quality Metric

The predicted logMAR image quality metric consists of a template created using a perfect letter (binary, 1’s and 0’s) of the same magnification as each eye’s optical system. Then, rays are geometrically traced through the individual eye in Zemax and the corresponding convolved evaluation letter is compared with the template using cross-correlation. A score is given from 0 to 1000 on how closely the evaluation letter matches the perfect letter (1000 being an exact match).21,34,35Fig. 1 shows an example of a perfect E and geometrically convolved evaluation E.

FIGURE 1
FIGURE 1
Image Tools

To account for any contrast changes in an image and to better predict the image quality that a person would perceive at the retina, the cross-correlation score is multiplied by the ratio of the contrast between the evaluation and perfect image. This is necessary since cross-correlation takes into account resolution differences between the template and convolved image, but is insensitive to contrast changes. In practice, calculation of this metric for letters of sizes representing optotype sizes on a logMAR acuity chart (1.0 logMAR to −0.30 logMAR in 0.10 logMAR steps) is completed, and then by comparing the metric score to a predetermined "cutoff" score, the just discernable image size for each individual eye model at each of the calculated object distances is determined. This modeling process emulates the clinical test of a patient reading down the lines of a logMAR chart to the lowest, resolvable line of acuity.21,35

Back to Top | Article Outline
Clinical Study Design for Validation of Model Predictability

The study followed the tenets of the Declaration of Helsinki and was approved by the Southwest Independent Institutional Review Board (Fort Worth, TX). Each subject signed an informed consent form following an explicit description of the study.

In order to validate the design process using individual eye models in conjunction with the predicted logMAR image quality metric, 24 subjects were enrolled in a randomized, single-eye, single-masked, repeated-measures insertion study. To be eligible for entry into the study, all subjects needed to have at least +1.50 D spectacle addition (at 40 cm), no greater than 2.50 D refractive cylinder, a physiologically normal anterior segment, no active ocular disease, and not be using any topical ocular medications.

To minimize fatigue in subjects, a total of seven object distances were used (6 m, 2 m, 1 m, 67 cm, 50 cm, 40 cm, and 25 cm) to measure monocular high-contrast logMAR acuities. All test lenses had a back vertex power of −3.00 D. Subjects were over-refracted with the best spherocylinderical correction for distance (6 m chart position) prior to acuity measurement. Normalized logMAR was calculated for each contact lens relative to the subject’s best distance spectacle baseline. This gives a value of how visual acuity changed compared to distance correction but with no add power. The goal of the validation study was to determine if the through-focus predicted logMAR from the 64 mature presbyopic eye models could predict visual performance of each contact lens when tested on a typical presbyopic clinical population.

Back to Top | Article Outline

RESULTS

Baseline Predicted logMAR Versus Clinical Acuity

Predicted logMAR was calculated for each of the 64 mature presbyopic eyes at all nine object distances, corresponding with the actual clinical measures. These calculated values were compared to the measured visual acuity scores for each subject. Regression of errors as well as residual differences between predicted logMAR and measured acuity were used to determine which model gave the highest correlation. “Cutoff” scores from 200 to 500 were analyzed to determine how correlation was impacted by the chosen threshold score. Empirical analysis demonstrated that using a quadratic weighting factor along with a “cutoff” score of 400 (range of possible scores is 0 to 1000) to find the individual model’s just discernable letter size gave the highest correlation to measured clinical through-focus visual acuity. The quadratic relationship between clinical acuity and predicted logMAR stems from the dependence of the visual system on spatial frequency components within the clinical acuity assessment. As shown previously, there is a scale invariance in visual acuity measurements with standard broadband optotypes that gives a non-linear relationship between spatial frequency and measured acuity.36

Fig. 2 shows the relationship between predicted logMAR from the Zemax eye models and the actual measured visual acuity from the clinical study. The solid black line represents the 1:1 trend line, whereas the dashed line represents the best-fit trend line for this data set. Linear regression analysis demonstrated a correlation of R2 = 0.85 between through-focus clinical visual acuity and predicted logMAR for the combined 64-eye population.

FIGURE 2
FIGURE 2
Image Tools

In order to better visualize the two datasets as a function of object distance, the 64 eyes were averaged to determine the mean through-focus logMAR for clinically measured and predicted populations. Fig. 3 shows the average results along with the standard deviation at each object distance. Because this is a mature presbyopic population, there is an obvious loss of visual acuity as a function of object distance.

FIGURE 3
FIGURE 3
Image Tools
Back to Top | Article Outline
Validation of the Model’s Predictive Capability

To be a useful design tool, a computer-based model needs to be able to predict the optical performance of new designs when tested on a clinical population. Validation of the modeling process and image quality metric presented here was achieved by using the 64 mature presbyopic computer models to predict the average clinical logMAR acuity performance of five different multifocal contact lens designs. A description of the optical designs is presented in Table 2 and the optical power profile for each design (lens only) is shown in Fig. 4, as calculated from Zemax.

TABLE 2
TABLE 2
Image Tools
FIGURE 4
FIGURE 4
Image Tools

During the modeling process in Zemax, a spectacle trial lens was added to the baseline lens model to replicate the over-refraction process for each contact lens design on each model. The spectacle trial lens was placed 14 mm away from the apex of the cornea to represent the average vertex distance of a spectacle correction. Defocus was minimized and the closest 0.25 D lens (in the plus power direction) was used to peak image quality for an object at distance. The modeling process described previously was applied to determine the just discernable letter size at each of the seven object distances that were utilized in the validation clinical with each multifocal contact lens in place.

Predicted logMAR with the contact lens on the eye was normalized to the predicted baseline logMAR value, which represents the individual eye’s acuity with a best-corrected distance spectacle correction. This was done to match the primary outcome measure of normalized clinical acuity from the validation study. Mean normalized clinically measured logMAR visual acuities were compared with the mean normalized predicted logMAR acuity results from the 64-eye database. Fig. 5 shows the correlation in through-focus acuity between the mean clinical and predicted acuity measures for each of the five lens designs. The solid black line represents the best-fit trend line for each data set. The Zemax models with predicted logMAR had high correlation with mean through-focus visual acuity from the clinic (R2 ≥ 0.90 for all lenses).

FIGURE 5
FIGURE 5
Image Tools
Back to Top | Article Outline

DISCUSSION

Computer-based predictive optical modeling is a powerful tool when designing relatively complex optical surfaces, such as multifocal contact lenses. However, to be useful and relevant, these models must be predictive using metrics that are easily compared to clinical measures that are typically used to define the optical performance of a multifocal contact lens in situ, such as high-contrast visual acuity. Previously presented computer-based eye models have used retinal image quality metrics that are derived from classical optical design measures such as MTF, Strehl ratio, or point spread functions.16–22 While these metrics are suitable for providing relative comparisons between contact lens designs (typically on a scale from 0 to 1), the lack of direct correlation to traditional clinical metrics means that the magnitude of differences in performance cannot be easily estimated in terms of clinical impact (i.e., logMAR acuity). There are currently very few image quality metrics that can be easily translated to clinical outcome measures.21,34

The model outlined in this study uses an outcome metric based on logMAR acuity, a measurement familiar to all clinicians and contact lens designers, and one where the relative impact of changes caused by a lens design can be easily interpreted. Moreover, the analysis is performed using a number of Zemax-based eye models, each designed to replicate the biometry of an individual human eye, providing mean results from a multitude of individual analyses, rather than a single analysis of a model built to represent the mean biometry of a population. The modeling process described here showed high correlation with through-focus visual acuity for the predicted logMAR baseline models (R2 = 0.85). Accounting for patient’s natural aberrations, pupil diameter changes, and residual accommodation appears to improve the correlation with clinical visual acuity. The addition of complex optical elements, such as multifocal contact lens designs, to the baseline individual computer eye models and the use of normalized predicted logMAR to determine the performance of the lens designs clinically maintained equally high correlations (R2 ≥ 0.90 was found between the predicted computer results and mean clinical results with five multifocal contact lens designs).

The benefits of a highly predictive model for an optical designer are numerous. Traditionally, theoretical modeling and metrology assessment results cannot easily transfer to those found clinically because of the individual differences in subject pupil size, accommodative response, and higher-order wavefront aberrations. Hence, designers are limited to an empirical approach to designing new multifocal lenses, making actual lens prototypes and testing them clinically. Time and cost invariably limit the number of design options and iterations that can be studied, resulting in a less-than-optimum final design. Incorporation of individual subject variations in biometry in concert with an output metric that has a high predictive correlation with through-focus clinical measurements frees the designer to test significantly more design options prior to any clinical testing, significantly expanding the design space that is investigated and increasing the probability that the optimal design solution will be tested and incorporated into the final lens design. Just as importantly, the final design will have been optimized for clinical robustness, meaning that the design will provide the best performance across a wide range of eyes, rather than being successful only on those eyes whose biometry closely matches those of the population mean.

The design approach described here is not limited to contact lens design; it can easily be adapted to intraocular lens designs, and the benefits may even be greater because the invasive nature of the clinical procedure used for implantation of the surgical lens in a clinical study tends to lead to a conservative design philosophy when designing multifocal optics. Although this study focuses on lens designs for mature presbyopic eyes, the modeling methodology is the same for both early presbyopic and non-presbyopic populations, as any impact of accommodation for intermediate and near targets on pupil size, defocus, astigmatism, and higher-order wavefront aberrations are accounted for from the base clinical data adapted to the individual eye models. This expands the many design opportunities that would benefit from this improved retinal image quality metric and optical design process.

Amanda C. Kingston
Bausch and Lomb
1400 N. Goodman Street
Rochester, NY 14609
e-mail: Amanda.C.Kingston@bausch.com

Back to Top | Article Outline

Acknowledgements

Received: February 18, 2013; accepted June 23, 2013.

Back to Top | Article Outline

REFERENCES

1. Liang J, Williams DR. Aberrations and retinal image quality of the normal human eye. J Opt Soc Am (A). 1997; 14: 2873–83.

2. Li YJ, Choi JA, Kim H, Yu SY, Joo CK. Changes in ocular wavefront aberrations and retinal image quality with objective accommodation. J Cataract Refract Surg. 2011; 37: 835–41.

3. Guirao A, Porter J, Williams DR, Cox IG. Calculated impact of higher-order monochromatic aberrations on retinal image quality in a population of human eyes. J Opt Soc Am (A). 2002; 19: 620–8.

4. Lombardo M, Lombardo G. Wave aberration of human eyes and new descriptors of image optical quality and visual performance. J Cataract Refract Surg. 2010; 36: 313–31.

5. Marcos S. Image quality of the human eye. Int Ophthalmol Clin. 2003; 43: 43–62.

6. Sarver EJ, Applegate RA. The importance of the phase transfer function to visual function and visual quality metrics. J Refract Surg. 2004; 20: S504–7.

7. Bennett AG, Rabbetts RG . Clinical Visual Optics. London: Butterworths; 1984; : 219–21.

8. Liou HL, Brennan N. Anatomically accurate, finite model eye for optical modeling. J Opt Soc Am (A). 1997; 14: 1684–95.

9. Doshi JB, Sarver EJ, Applegate RA. Schematic eye models for simulation of patient visual performance. J Refract Surg. 2001; 17: 414–9.

10. Atchison DA, Smith G . Optics of the Human Eye. Boston: Butterworth-Heinemann; 2000; .

11. Rosales P, Marcos S. Customized computer models of eyes with intraocular lenses. Opt Express. 2007; 15: 2204–18.

12. Greivenkamp JE, Schwiegerling J, Miller JM, Mellinger MD. Visual acuity modeling using optical raytracing of schematic eyes. Am J Ophthalmol. 1995; 120: 227–40.

13. Popiolek-Masajada A, Kasprzak HT. A new schematic eye model incorporating accommodation. Optom Vis Sci. 1999; 76: 720–7.

14. Popiolek-Masajada A, Kasprzak H. Model of the optical system of the human eye during accommodation. Ophthal Physl Opt. 2002; 22: 201–8.

15. Navarro R, Santamaria J, Bescos J. Accommodation-dependent model of the human eye with aspherics. J Opt Soc Am (A). 1985; 2: 1273–81.

16. Chen L, Singer B, Guirao A, Porter J, Williams DR. Image metrics for predicting subjective image quality. Optom Vis Sci. 2005; 85: 358–69.

17. Cheng X, Thibos LN, Bradley A. Estimating visual quality from wavefront aberration measurements. J Refract Surg. 2003; 19: S579–84.

18. Schwiegerling J. Predicting clinical visual acuity of presbyopia treatments. J Refract Surg. 2010; 26: 66–70.

19. Ravikumar A, Applegate RA, Shi Y, Bedell HE. Six just-noticeable differences in retinal image quality in 1 line of visual acuity: toward quantification of happy versus unhappy patients with 20/20 acuity. J Cataract Refract Surg. 2011; 37: 1523–9.

20. Ravikumar A, Sarver EJ, Applegate RA. Change in visual acuity is highly correlated with change in six image quality metrics independent of wavefront error and/or pupil diameter. J Vis. 2012; 12: 1–13.

21. Watson AB, Ahumada AJ. Predicting visual acuity from wavefront aberrations. J Vis. 2008; 8: 1–19.

22. Marsack JD, Thibos LN, Applegate RA. Metrics of optical quality derived from wave aberrations predict visual performance. J Vis. 2004; 4: 322–8.

23. Guirao A, Williams DR. A method to predict refractive errors from wave aberration data. Optom Vis Sci. 2003; 80: 36–42.

24. Thibos LN, Hong X, Bradley A, Applegate RA. Accuracy and precision of objective refraction from wavefront aberrations. J Vis. 2004; 4: 329–51.

25. Cheng X, Bradley A, Thibos LN. Predicting subjective judgment of best focus with objective image quality metrics. J Vis. 2004; 4: 310–21.

26. Legras R, Benard Y, Rouger H. Through-focus visual performance measurements and predictions with multifocal contact lenses. Vis Res. 2010; 50: 1185–93.

27. Applegate RA, Sarver EJ, Khemsara V. Are all aberrations equal? J Refract Surg. 2002; 18: S556–62.

28. Applegate RA, Marsack JD, Ramos R, Sarver EJ. Interaction between aberrations to improve or reduce visual performance. J Cataract Refract Surg. 2003; 29: 1487–95.

29. Cheng H, Barnett JK, Vilupuru AS, Marsack JD, Kasthurirangan S, Applegate RA. A population study on changes in wave aberrations with accommodation. J Vis. 2004; 4: 272–80.

30. Cheng X, Himebaugh NL, Kollbaum PS, Thibos LN, Bradley A. Validation of a clinical Shack-Hartmann aberrometer. Optom Vis Sci. 2003; 80: 587–95.

31. Thibos LN, Applegate RA, Schwiegerling JT, Webb R. Standards for reporting the optical aberrations of eyes. J Refract Surg. 2002; 8: S652–60.

32. Schwiegerling J . Field Guide to Visual and Ophthalmic Optics. Bellingham: SPIE Press; 2004; .

33. Artal P, Guirao A, Berrio E, Williams DR. Compensation of corneal aberrations by the internal optics in the human eye. J Vis. 2001; 1: 1–8.

34. Watson AB, Ahumada AJ. Modeling acuity for optotypes varying in complexity. J Vis. 2012; 12: 1–19.

35. Pinto CD, Kingston AC, Ludington P, Venkiteshwar MS. Pattern recognition as a retinal image quality metric. Invest Ophthalmol Vis Sci. 2008; 49:

E-Abstract 995


36. McAnany JJ, Alexander KR, Lim JI, Shahidi M. Object frequency characteristics of visual acuity. Invest Ophthalmol Vis Sci. 2011; 52: 9534–8.

Keywords:

presbyopia; image quality; through-focus; accommodation; computer eye model

Copyright © 2013 American Academy of Optometry

Login

Search for Similar Articles
You may search for similar articles that contain these same keywords or you may modify the keyword list to augment your search.