 |
| Volume 4, Number 4, Article 7, Pages 310-321 |
doi:10.1167/4.4.7 |
http://journalofvision.org/4/4/7/ |
ISSN 1534-7362 |
Predicting subjective judgment of best focus with objective image quality metrics
Xu Cheng |
School of Optometry, Indiana University, Bloomington, IN, USA |
|
Arthur Bradley |
School of Optometry, Indiana University, Bloomington, IN, USA |
|
Larry N. Thibos |
School of Optometry, Indiana University, Bloomington, IN, USA |
|
Abstract
Purpose: To determine the impact of higher-order monochromatic aberrations on lower-order subjective sphero-cylindrical refractions. Methods: Computationally-aberrated, monochromatic Sloan letters were presented on a high luminance display that was viewed by an observer through a 2.5mm pupil. Through-focus visual acuity (VA) was determined in the presence of spherical aberration (Z40) at three levels (0.10, 0.21 and 0.50D). Analogous through-astigmatism experiments measured visual acuity in the presence of secondary astigmatism (Z4±2) or coma (Z3-1). Measured visual acuity was correlated with 31 different metrics of image quality to determine which metric best predicts performance for degraded retinal images. The defocus and astigmatism levels that optimized each metric were compared with those that produced best visual acuity to determine which metric best predicts subjective refraction. Results: Spherical aberration, coma and secondary astigmatism all reduced VA and increased depth of focus. The levels of defocus and primary astigmatism that produced the best performance varied with levels of spherical aberration and secondary astigmatism, respectively. The presence of coma, however, did not affect cylindrical refraction. Image plane metrics, especially those that take into account the neural contrast sensitivity threshold (e.g. the visual Strehl ratio, VSOTF), are good predictors of visual acuity in both the through-focus and through-astigmatism experiments (R = -0.822 for VSOTF). Subjective sphero-cylindrical refractions were accurately predicted by some image-quality metrics (e.g., pupil fraction, VSOTF and standard deviation of PSF light distribution). Conclusion: Subjective judgment of best focus does not minimize RMS wavefront error (Zernike defocus = 0), nor create paraxial focus (Seidel defocus = 0), but makes the retina conjugate to a plane between these two. It is possible to precisely predict subjective sphero-cylindrical refraction for monochromatic light using objective metrics.
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|
History
Received October 1, 2003; published April 23, 2004
Citation
Cheng, X., Bradley, A., & Thibos, L. N. (2004). Predicting subjective judgment of best focus with objective image quality metrics.
Journal of Vision, 4(4):7, 310-321,
http://journalofvision.org/4/4/7/,
doi:10.1167/4.4.7.
Keywords
visual optics, metrics of optical quality, best focus
for related articles by these authors
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With the introduction of modern aberrometry into the
clinical environment, it is now possible to routinely assess the monochromatic
aberrations of the human eye (Cheng, Himebaugh, Kollbaum, Thibos, & Bradley,
2003, 2004). This technology is currently being
employed as a guide for photoablative lasers attempting to correct higher order
aberrations in the optics of the eye (MacRae, Krueger, & Applegate, 2001; Mrochen, Kaemmerer, & Seiler, 2001; Nagy, Palagyi-Deak, Kelemen, &
Kovacs, 2002; Nagy, Palagyi-Deak,
Kovacs, Kelemen, & Forster, 2002).
It is also tempting to anticipate that such a detailed description of the eye's
optics could be employed to generate highly accurate automated
sphero-cylindrical refractions (Chen et al., 2003; Guirao & Williams, 2003; Thibos, Bradley, & Applegate, 2003). We examine this possibility in the
present manuscript.
Automated sphero-cylindrical refractors have been
available since 1970s, but they are currently only employed to provide an
approximate estimate of the refractive error and as a substitute for retinoscopy
but not an alternative to the subjective refraction (Bullimore, Fusaro, &
Adams, 1998; Campbell, Benjanmin,
& Howland, 1998; Goss &
Grosvenor, 1996). Although we generally
consider the subjective refraction as the “gold standard” for
refraction in that it is the sphero-cylindrical lens that provides best
subjective performance, it can be quite variable (Bullimore et al., 1998; Goss & Grosvenor, 1996). Therefore improved automated
refractions have the potential to provide accurate and more reliable
prescriptions than the current “gold standard”. Although
high-resolution aberrometry data might seem to provide a possible advance in
automated refractions, they reveal a fundamental problem for determining the
refractive error. Because the optical system of human eyes is aberrated
(Castejon-Mochon, López-Gil, Benito, & Artal, 2002; Porter, Guirao, Cox, &
Williams, 2001; Thibos, Hong,
Bradley, & Cheng, 2002), a
sphero-cylindrical correcting lens cannot generate a truly focused image.
Irrespective of the correcting lenses used, some parts of the beam generating
the retinal image will always be blurred. Therefore, in this situation, it is
not immediately obvious which sphero-cylindrical lens will provide the best
image quality.
There are many ways to define image quality, e.g. pupil
plane metrics and image plane metrics (Cheng, Thibos, & Bradley, 2003; Thibos, Hong, Bradley, &
Applegate, 2004), and the data generated by an
aberrometer can be used to calculate a wide variety of such image quality
metrics (e.g. wavefront error RMS, PSF widths, volume under the OTF etc.). The
question examined in this paper is whether subjective refractions are affected
by the presence of higher order aberrations and whether the subjective
refractions of aberrated eyes can be accurately predicted by such objective
image quality metrics. Thibos et al (Thibos, Hong et al., 2002) showed that carefully refracted
subjects still had significant amounts of residual Zernike defocus
( Z20), which means that the
RMS wavefront error was not minimized by a subjective refraction. Similar
results were found by Guirao et al who reported that refraction based on minimum
RMS wavefront error made the eye myopic while refraction based on paraxial focus
made the eye hyperopic (Guirao & Williams, 2003). In addition, in a previous study
(Cheng, Bradley, Thibos, & Ravikumar, 2003) we found that, in the presence of
spherical aberration, visual acuity was better with paraxial focus than with the
defocus that minimized RMS. Although subjective refractions achieve approximate
paraxial focus, a systematic paraxial defocus still exists (Thibos, Hong et al.,
2002). Thus, these previous studies
all indicate that subjective refractions in aberrated eyes do not minimize RMS,
and probably do not achieve paraxial focus, which poses the question of what
optical characteristic or image quality property is optimized during a
subjective refraction?
The impact of aberrations on vision can be studied with
two quite different methodologies. First, aberration levels in the eye can be
manipulated directly by interposition of lenses (Bradley, Thomas, Kalaher, &
Hoerres, 1991), phase plates
(Lopez-Gil, Howland, Howland, Charman, & Applegate, 1998), or deformable mirrors (Artal et
al., 2003; Chen et al., 2003; Williams, 2002) into the visual path. Alternately,
the displayed stimulus can be computationally aberrated and degraded (Applegate,
Ballentine, Gross, Sarver, & Sarver, 2003; Applegate, Marsack, & Ramos,
2003; Applegate, Sarver, &
Khemsara, 2002). Chan et al (Chan,
Smith, & Jacobs, 1985) characterized
these two approaches as the “source” (defocused display) and
“observer” (defocusing the eye) methods, and demonstrated their
equivalence for spherical defocus. The computational blurring method has
flexibility and cost advantages over phase plates, lenses and deformable
mirrors. However, in order to simulate aberrated retinal images accurately, all
sources of image degradation between the digital image and the retina (e.g., the
non-linearity and demodulation of the display and the demodulation from the
subject’s optics) must be pre-compensated for in the digital image.
Using computationally aberrated images, we have
examined the relationship between image quality determined subjectively (visual
acuity) and 31 different objective measures of image quality in an attempt to
identify those metrics that correlate with visual performance and could be used
for accurate automated refractions.
The experimental set-up was designed to examine visual
acuity with computationally aberrated images. Using the computational methods of
Fourier optics, aberrated images of Sloan letters were computed as the
convolution of the object and point-spread function (PSF) of an eye with an
assigned wavefront aberration function over a 5mm pupil. This convolution was
performed in the frequency domain to facilitate pre-compensation for
demodulation caused by the projection system and the eye’s optics. This
pre-compensation was achieved by dividing the computed image spectra by the
display optical transfer function (OTF) and by the diffraction-limited OTF of
the subject’s eye. The computed images were displayed as visual stimuli on
a high luminance (3045cd/m2) gamma-corrected, rear-projection screen
viewed through an interference filter
(λ = 556nm). The luminance of
this monochromatic stimulus viewed by the subject was 264cd/m2.
Subjects viewed the simulated images through a unit magnification relay
telescope, which conjugated a 2.5mm artificial pupil centered on the primary
line of sight with the subject’s entrance pupil plane. The 2.5mm
artificial pupil ensured that the subject’s eye was approximately
diffraction-limited (confirmed experimentally) and was computationally
convenient. In this way, visual acuity could be evaluated with controlled levels
of monochromatic aberrations.
During a clinical subjective refraction, the impact of
spherical defocus and astigmatic defocus are observed in the presence of
numerous ocular higher order aberrations. Standard nomenclature for Zernike
coefficients (Thibos, Applegate, Schwiegerling, & Webb, 2002) are used in this paper. We simulated
this experience in the current experiment by modulating the amount of spherical
defocus ( Z20) in the presence of three possible levels of spherical aberration ( Z40) when computing blurred stimuli. Similarly, we examined the impact of astigmatic defocus ( Z2±2) in the presence of secondary astigmatism ( Z4±2) or coma
( Z3-1). These experiments
are, therefore, controlled analogues of the standard subjective refraction in
which through-focus or through-astigmatism plots of acuity are obtained.
The aberrations used to degrade the stimuli were
combinations of 3 levels of Zernike higher order aberrations
( Z40,
Z4±2, or
Z3-1) with 7 or 8 of levels
of Zernike defocus ( Z20) or
astigmatism ( Z2±2) for
a 5mm pupil diameter. The three levels of higher-order aberrations were 0.10D,
0.21D and 0.50D of equivalent defocus, where equivalent defocus (in diopters) is
given by the following equation (Thibos, Hong et al., 2002).  | (1) |
The two smaller levels of higher-order aberrations are
equal to the mean and 95th percentile level of spherical aberration found in
young adult eyes (Thibos, Hong et al, 2002). The larger level is
non-physiological for normal eyes but may occur in surgically altered eyes. The
range of Zernike defocus included that value which minimized total RMS wavefront
error (zero Zernike defocus) and that value which produced paraxial focus (e.g.,
√15 times the amount of
spherical aberration (Thibos, Hong et al., 2002)).
Visual acuity was derived from psychometric functions
of percent correct versus letter size. A logarithmic series of eight to ten
letter sizes each presented ten times were used to generate each psychometric
function. Letter sizes and individual Sloan letters were randomly sequenced.
Visual acuity in logMAR units was the letter size that gave a 55% correct
performance (interpolated using a Weibull fit to the data) (Cheng, Bradley et
al., 2003).
Interpolated visual acuities were plotted as a function
of defocus or astigmatism, and these through-focus plots were fitted by the
least-squares method with 5th order polynomials. The “subjective”
best-focus corresponded, therefore, to the amount of the Zernike defocus or
primary astigmatism that maximized visual acuity.
The aberrated wavefronts used to compute the blurred
letter images were also used to compute a large set
( n
= 31) of optical quality metrics for every aberration condition used
experimentally. Detailed descriptions of these metrics are given in the
accompanying paper (Thibos, Hong et al., 2004). A simple clarification of the
acronyms is given in Table 2 in the
Appendix). These metric values were correlated with logMAR acuity to evaluate
their success at predicting visual performance. Since logMAR and metrics are
both dependent variables, we used principal component analysis to determine the
orthogonal regression line. We also determined the level of defocus or
astigmatism that optimized each metric to evaluate the accuracy and precision
with which each metric predicted subjective best focus.
The through-focus visual acuity data and samples of
blurred stimuli are shown in Figure 1. Each
panel displays images of a letter (D) degraded by the same levels of higher and
lower order aberrations that were used to assess visual acuity. The three rows
of letter images in panels A, C and D represent the three levels of 4th order
aberration (0.10, 0.21, and 0.50D of equivalent defocus). The two rows of letter
images in panel B represent the two levels of 3 rd order coma (0.00D
and 0.50D) tested. The letter images within each row reflect the different
levels of spherical defocus (panel A) or astigmatism (panels B, C and D) used to
generate the through-focus plots of visual acuity. Through-focus logMAR acuities
are shown within panels A, C & D for the three different levels of
4 th order aberrations (triangles = 0.10D, circles = 0.21D and squares
= 0.50D) and for two subjects (solid and open symbols). Only one subject and two
levels (0.00 and 0.50D) of coma were tested.
Figure 1. Through focus logMAR acuity plots and
examples of blurred stimuli. Filled and open symbols show data from two
subjects. A: LogMAR visual acuity as a function of Zernike defocus
(Z20)
in the presence of 3 levels of Zernike spherical aberration
(Z40).
B: LogMAR visual acuity as a function of normal astigmatism
(Z2+2)
in the presence of 2 levels (0.00D, triangles; 0.50D squares) of Zernike
vertical coma
(Z3-1).
C & D: LogMAR visual acuity as a function of Zernike primary astigmatism
(Z2±2)
in the presence of 3 levels (0.10D, triangles; 0.21D, circles; 0.50D, squares)
of Zernike secondary astigmatism
(Z4±2).
Sample stimuli for each aberration conditions are shown at the top of each
panel. The defocus or astigmatism level for each stimulus may be read from the
abscissa of the corresponding graph. Arrows indicate paraxial focus at each
level of higher order aberrations.
The through-focus acuity data in Figure 1A clearly show that the presence of
4 th order spherical aberration
( Z40) impacts the visual
effect of defocus ( Z20) in
several ways. First, the best achievable visual acuity deteriorates with
increasing levels of spherical aberration. Second, confirming Applegate et al.
(Applegate, Marsack, & Ramos, 2003), the amount of Zernike spherical
aberration influenced the amount of defocus needed to produce the best visual
acuity. Third, increasing levels of Zernike spherical aberration significantly
decreased the change in logMAR produced by defocus and thus increased the depth
of focus. The results in panels C&D show that the presence of secondary
astigmatism ( Z4±2) had
a similar influence on the visual effect of second order astigmatism
( Z2±2). The presence of
coma also reduced the best achievable visual acuity and increased the depth of
focus. It did not, however, change the level of astigmatism necessary to achieve
best acuity. When MAR approached its minimum levels (at “best
focus”), one subject’s acuity was consistently 0.1 log units better
than the other. We assume this reflects a genuine neural difference between
these two subjects.
The wavefronts used to generate blurred letters with
fourth order aberrations (spherical aberration and secondary astigmatism) were
used to calculate a large number
( n
= 31) of optical and image quality metrics. Metric amplitude was then
correlated with logMAR visual acuity observed for these same conditions (all of
the data in Figure 1A, C and D). The
scattergrams in Figure 2 show 3 examples of
optical and image-quality metrics, which were reasonably well correlated with
logMAR visual acuity (left panels) and 3 examples that were poorly correlated
(right panels). In each case (good correlation and poor correlation), we
illustrate examples of wavefront-based, PSF-based and OTF-based metrics. The
metric of “pupil fraction” (PFSt, the fraction of pupil area for
which the optical quality of the eye is reasonably good, Cheng, Thibos, &
Bradley, 2003) was well correlated with
acuity
( R =
-0.837), whereas RMS wavefront error (RMSw) was poorly correlated
( R =
0.493). Also, two PSF metrics produced different correlations: the
standard deviation of intensity values in the PSF, normalized to
diffraction-limited value (STD,
R =
-0.816) and PSF half-width-at-half height (HWHH,
R =
0.365). Finally, visual Strehl ratio (VSOTF, the
contrast-sensitivity-weighted OTF divided by contrast-sensitivity-weighted OTF
for diffraction limited optics, Cheng, Himebaugh, Kollbaum, Thibos, &
Bradley, 2004) correlated well with
logMAR
( R =
-0.822), whereas the metric designed to capture the phase changes in the
image, OTF/MTF ratio (VOTF), was poorly correlated with acuity
( R =
-0.182). It is interesting to see that the metric of VOTF successfully
divided the data points in to two distinct parts. The data points at the bottom
area of Figure 2F represent visual acuity
obtained under the aberration conditions that introduced large phase shifts
(VOTF < 1), whereas the data points at the top area of Figure 2F represent visual acuity obtained under
aberration conditions that maintained phase (VOTF
≈ 1). For those aberrations that did not introduce phase shifts (VOTF
≈ 1), visual acuity deteriorated due to decreased contrast, which had
little effect on VOTF. Therefore it is not surprising to see many of the data
points spread horizontally around the VOTF value of 1. Interestingly, we found
that good visual acuity was also obtained from some aberration conditions with
large phase shifts. Good acuities under these conditions are possible because
these large phase shifts resulted in multiple ghost images (e.g. sample images
in Figure 1C and D) each of which could be
resolved. Such fortuitous legibility is less likely to occur with multiple
letter presentation and overlapping ghost images. Future studies designed
specifically to examine the importance of spatial phase might clarify the
utility of the metric VOTF for predicting the impact of phase shifts on
vision.
Figure 2. Scattergrams showing the correlations
between metrics values and logMAR visual acuity measures. Left panels (A, C and
E) show examples of three metrics that are well correlated with visual acuity.
Straight lines show the slope of orthogonal regression line (i.e.
1st principle component).
Right panels (B, D and F) show examples of three metrics that are poorly
correlated with visual acuity. Symbols are combined data from the through-focus
(filled dots) and the through-astigmatism (open circles) experiments for all
four subjects. Each color represents one subject.
We anticipated that objective measures of optical
quality that are well correlated with visual acuity are likely to be optimized
under the same conditions that optimize visual acuity. To examine this
prediction we compared the psychophysically determined through focus acuity data
with computationally generated through-focus plots of each image quality metric.
Figure 3 shows an example of such a comparison
for 0.21D Z40. Two examples
show that the RMS wavefront error (RMSw) and the PSF half-width-at-half-height
(HWHH) are clearly optimized by a defocus level that does not provide maximum
acuity. However, visual Strehl ratio (VSOTF) was optimized by almost the exact
level of defocus that produced maximum visual acuity. Therefore, if RMSw or HWHH
were used as a basis for objective refractions, less than optimal visual acuity
would ensue whereas if VSOTF was used, optimal visual acuity would be
achieved.
Figure 3. An example of determining subjective
best focus and objective best focus. Symbols show logMAR acuity measures (filled
dots) and metric values of VSOTF (filled triangles), RMSw (open circles) and
HWHH (open triangles) at each through-focus condition. Smooth curves are the
5th order polynomial fit
of the logMAR acuity (solid) and each of the three sample metric values
(dashed). Arrows indicate best focus that maximize logMAR visual acuity or
optimize the metric values.
The analysis shown in Figure 3 was repeated for all 31 metrics, all
three levels of fourth order aberration, all three types of aberration, and all
subjects. We examined the ability of each metric to accurately predict the
subjective best focus in the presence of different levels of fourth order
aberrations. The top two panels in Figure 4 compare the
optimum level of focus determined subjectively (maximizing visual acuity) with
the optimum level determined objectively (optimizing metric). Panel A shows that
the three examples of metrics that are well correlated with visual acuity ( Figure 2) also predict subjective best focus
successfully. The PFSt, STD and VSOTF are all very successful at predicting best
sphere power. The slopes of principle component analysis shown in Figure 4A for these three metrics were 1.048, 0.992 and 1.036
and the correlation coefficients were 0.998, 0.999, and 0.998, respectively.
Most importantly, the average error in refraction generated by these three
objective measures was less than 0.10D. Not surprisingly, panel B shows that the
three examples of metrics that were poorly correlated with acuity ( Figure 2) were unsuccessful at predicting
subjective best focus. Panel B also shows the relationship between paraxial
focus and subjective best focus. For each level of 4th order aberration,
paraxial focus is consistently hyperopic relative to subjective best focus.
Figure 4.
Comparison between objective (metric predicted) and subjective determined best
focus. Top panels (A, B) show data from through-focus experiment, bottom panels
(C, D) show data from through-astigmatism experiment. A & C: Best focus
predicted by three metrics (PFSt, solid lines; STD, dashed lines; and VSOTF,
dotted lines) that were well correlated with logMAR visual acuity measures. B
& D: Best focus predicted by three metrics (RMSw, solid lines; HWHH, dashed
lines; and VOTF, dotted lines) that were poorly correlated with logMAR visual
acuity measures. Each symbol shows data from one subject at each through focus
condition.
A similar analysis was performed on the
through-astigmatism data for the three levels of secondary astigmatism. We have
grouped both normal and oblique astigmatism data, which are shown in the bottom
two panels of Figure 4. Unlike the sphere data, the same
three metrics that were well correlated with logMAR visual acuity were less
successful at predicting the optimum astigmatic correction in the presence of
secondary astigmatism ( Figure 4C). Slopes and correlation
coefficients for the astigmatic case are 1.149, 1.189, 1.187, and 0.973, 0.965,
and 0.970 for PFSt, STD and VSOTF, respectively. The residual error in
astigmatic correction generated by these “good” metrics was, on
average 0.13D. By comparison, the errors in spherical and astigmatic correction
that would be introduced by the three “poor” metrics were on average
0.53D and 0.26D respectively.
The correlation analysis shown in Figure 2, and the accuracy of the objective sphere
and cylinder refractions was assessed for all 31 metrics. The results
( R and mean absolute error of residual
diopters of sphere and cylinder) are summarized for each metric in Table 1 in the Appendix. Table 1 also shows the refractive accuracy of
paraxial focus.
The two primary goals of this study were (1) to
quantify the impact of fourth order Zernike aberrations (spherical aberration
and secondary astigmatism) on subjective best focus, and (2) to identify those
objective optical quality metrics that can predict the changes in subjective
spherical and cylindrical refractions generated by different levels of the
radially symmetric Z40 and
the meridionally varying
Z4±2. We confirmed that
the level of Z40 and
Z4±2 had a profound
impact on the subjective spherical and cylindrical refraction ( Figure 1), and that some but not all objective
metrics of optical quality predicted subjective best focus with great accuracy
over a wide range of aberration levels ( Figure 4 and Table 1).
Although the three objective metrics that were well
correlated with visual acuity predicted subjective spherical refractions almost
perfectly ( Figure 4A), the same metrics were less accurate
for predicting subjective cylinder power ( Figure 4C, Table 1). This may reflect a genuine inability
of these metrics to predict astigmatic refractions, which in turn, may reflect
variability in the response of individual subjects to astigmatic blur. We
confirmed that the less accurate objective astigmatic refractions did not
reflect higher levels of variability in the original astigmatic acuity data.
Examinations of the slopes of the psychometric functions (psychophysical
variability) revealed equivalent slopes for spherical and astigmatic defocus.
In an earlier study of monochromatic aberrations in 200
well refracted adult eyes (Thibos, Hong et al., 2002), we found that subjective best
focus was positively correlated with the magnitude of Zernike spherical
aberration ( Z40) and it did
not minimize RMS wavefront error. In our simulated aberration paradigm, we also
found that subjective refractions (level of positive
Z20 required for maximum
acuity) were affected by the levels of
Z40, and maximum acuity was
not achieved by minimal RMS. In both studies, acuity was maximized in the
presence of positive spherical aberration by a positive
Z20, which was also reported
in two studies by Applegate et al. (Applegate, Marsack, & Ramos, 2003; Applegate, Sarver, &
Khemsara, 2002). Thus, relative to
the defocus that minimizes RMS, both real and virtual eyes were myopic when
acuity was maximized. In both cases, this reflects a shift in subjective
refraction toward the spherical power required to focus paraxial
rays. In the real eyes of Thibos’ study (Thibos, Hong
et al., 2002), subjective spherical
equivalents were sufficient to almost perfectly focus paraxial rays from an
infinitely distant target (their Figure 15). However, in our virtual eyes,
acuity was maximized by spherical equivalents intermediate to those necessary
for minimal RMS (zero Z20)
and for paraxial focus (zero
r2) ( Figure 1A). This difference may reflect the
influence of pupil apodization (Stiles-Crawford effect (Zhang, Ye, Bradley,
& Thibos, 1999)), which biases
visual responses toward the pupil center (Charman, Jennings, & Whitefoot, 1978; Koomen, Scolnik, & Tousey, 1951; Koomen, Tousey, & Scolnik, 1949; Thibos, Hong et al., 2002) in the real eye subjective
refractions. In addition, as argued by Thibos et al (Thibos, Hong, et al., 2004), clinical subjective refractions are
designed to bring the hyperfocal distance rather than infinity into focus ( Figure 5A), therefore coincidentally rendering paraxial rays
from an infinite target well focused. This coincidence indicates that half of
the depth of focus (dioptric difference between hyperfocal distance and
infinity) is approximately equal to the dioptric difference between optimal
focus and paraxial focus. However, in the current study, we aimed to maximize
visual acuity at a target distance of infinity, therefore the retina is
conjugated to a point at infinity, and is conjugated to a point beyond infinity
(hyperopia) for paraxial rays ( Figure
5B).
Figure 5. Schematics showing A: In
Thibos’ study, clinical subjective refractions conjugate the retina to the
hyperfocal plane that is 1/2 depth of field in front of infinity, and is in
between the retina conjugate plane for paraxial focus and minimum RMS. B: In
current study, optimum refraction is achieved when the retina is conjugated to
infinity, and again in between paraxial focus and minimum RMS. Any objective
image quality metric (e.g., VSOTF) that accurately predicted subjective best
focus would conjugate the retina to infinity.
If the results of our analysis of 31 image quality
metrics performed on our virtual eyes is also observed in real eyes, we would
anticipate that subjective refractions would optimize VSOTF and PFSt. Thibos et
al (Thibos, Hong et al., 2004) have
employed the same 31 metrics to examine the predicted spherical equivalent
refractions for the 200 real eyes described in Thibos et al., 2002 (Thibos, Hong
et al., 2002). They argue that
subjective refractions should optimize vision at the hyperfocal distance, but
they did not measure the hyperfocal distance, and thus it is impossible with
certainty to identify which metric was actually optimized during the subjective
refraction. They found that in order for VSOTF and PFSt to be optimized the
target would have to be placed at approximately –0.25 diopters closer than
infinity. Of course, this is approximately one half of the depth of focus in
front of infinity (Atchison, Charman, & Woods, 1997), that is at the hyperfocal
distance. And as described above, the standard maximum plus/minimum minus
refraction technique used to generate the subjective refractions is specifically
designed to optimize vision at the hyperfocal distance. Therefore, in both our
simulated eyes under monochromatic testing and in 200 real eyes refracted in
polychromatic light, it appears that some objective image quality metrics (e.g.
VSOTF and PFSt) are optimized when visual acuity is maximized.
The discrepancies seen between the subjective
refraction in our computational “virtual eye” and those of real eyes
(the latter approximates a paraxial refraction), emphasize that the objective
metrics that were so accurate at predicting refractions in our study may
systematically over-minus the patient. This discrepancy will likely be larger
for larger pupils. Therefore, a successful implementation of aberrometry for
autorefraction may require the inclusion of an apodized pupil into the
computational algorithms (Thibos, Hong et al., 2002) and consideration of the maximum
plus/minimum minus philosophy employed during subjective refractions.
Table 1 and a simple
clarification of the acronyms of the metrics ( Table 2) are provided
below.
|
|
Metric acronym
|
R
|
SE error (D)
|
Astigmatic error (D)
|
|
1
|
WF(1): RMSw
|
0.4931
|
0.5383
|
0.4642
|
|
2
|
WF(2): PV
|
0.3759
|
0.5383
|
0.5392
|
|
3
|
WF(3): RMSs
|
0.5375
|
0.6083
|
0.5375
|
|
4
|
WF(4): PFWc
|
-0.6999
|
0.1583
|
0.1092
|
|
5
|
WF(5): PFWt
|
-0.7293
|
0.505
|
0.0942
|
|
6
|
WF(6): PFSt
|
-0.8374
|
0.0417
|
0.1025
|
|
7
|
WF(7): PFSc
|
-0.8016
|
0.095
|
0.1392
|
|
8
|
WF(8): Bave
|
0.5783
|
0.7283
|
0.6542
|
|
9
|
WF(9): PFCt
|
-0.7434
|
0.295
|
0.3242
|
|
10
|
WF(10): PFCc
|
-0.3308
|
0.4617
|
0.4625
|
|
11
|
PS(1): D50
|
0.5452
|
0.4583
|
0.3558
|
|
12
|
PS(2): EW
|
0.7377
|
0.375
|
0.1508
|
|
13
|
PS(3): SM
|
0.536
|
0.655
|
0.4942
|
|
14
|
PS(4): HWHH
|
0.3652
|
0.6617
|
0.1292
|
|
15
|
PS(5): CW
|
0.4879
|
0.455
|
0.3042
|
|
16
|
PS(6): SRX
|
-0.7053
|
0.3917
|
0.1442
|
|
17
|
PS(7): LIB
|
-0.7301
|
0.1383
|
0.2092
|
|
18
|
PS(8): STD
|
-0.8158
|
0.0483
|
0.1408
|
|
19
|
PS(9): ENT
|
0.7198
|
0.6217
|
0.3358
|
|
20
|
PS(10): NS
|
-0.8464
|
0.0283
|
0.1242
|
|
21
|
PS(11): VSX
|
-0.7942
|
0.075
|
0.1625
|
|
22
|
SF(1): SFcMTF
|
-0.5338
|
0.1683
|
0.4175
|
|
23
|
SF(2): AreaMTF
|
-0.7445
|
0.035
|
0.1508
|
|
24
|
SF(3): SFcOTF
|
-0.6416
|
0.1583
|
0.2308
|
|
25
|
SF(4): AreaOTF
|
-0.7671
|
0.0317
|
0.1442
|
|
26
|
SF(5): SROTF
|
-0.6542
|
0.3917
|
0.1608
|
|
27
|
SF(6): VOTF
|
-0.1815
|
0.385
|
0.1975
|
|
28
|
SF(7): VSOTF
|
-0.8216
|
0.0717
|
0.1508
|
|
29
|
SF(8): VNOTF
|
-0.2938
|
0.2283
|
0.4992
|
|
30
|
SF(9): SRMTF
|
-0.7658
|
0.3883
|
0.2625
|
|
31
|
SF(10): VSMTF
|
-0.8456
|
0.075
|
0.1542
|
|
32
|
Paraxial
|
|
0.525
|
0.5925
|
Table 1. Correlation coefficients (metrics values
vs. logMAR acuity measures, R) and mean absolute spherical (SE) and astigmatic
errors between subjective and objective best focus for each of the 31 objective
image quality metrics (Thibos, 2003).
Shadowed metrics were the ones used as examples in the text. Metrics in red
indicate poor correlations with logMAR visual acuity, whereas metrics in blue
indicate good correlations with logMAR visual acuity. The errors produced by
paraxial focus are also included.
Table 2. Acronyms of the metrics.
This research was supported by National Institutes of
Health Grant EY-05109 (LNT)
Commercial relationships: Thibos has a proprietary
interest in the development of optical metrics predictive of visual
performance.
Corresponding author: Xu Cheng.
Email: xcheng@indiana.edu.
Address: 800 E. Atwater Ave, Bloomington, IN
47405.
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