Transcript Scheimpflug Imaging of the Human Lens using the Oculus

Predicting visual performance
from wavefront quality metrics in
cataract
Konrad Pesudovs
Katja Ullrich
NH&MRC Centre for Clinical Eye Research,
Flinders Medical Centre & Flinders University,
Adelaide, South Australia
Financial disclosure: The authors have no financial interest
Background and Purpose
• Cataract affects visual performance via higher order
aberrations and light scatter
• Wavefront aberrations occurring in cataract have been
described in terms of the Zernike polynomial
decomposition but neither Zernike terms nor RMS
predict visual performance
• Other methods for organising wavefront data exist –
wavefront quality metrics
• Attempts to connect wavefront quality metrics to visual
performance in cataract are lacking
PURPOSE: To determine which wavefront quality
metrics are most predictive of visual performance
in patients with cataract
Population and Visual Performance
• Prospective, cross-sectional
study of consecutive patients
attending the Cataract
Assessment Clinic at Flinders
Medical Centre
• Inclusions – all types of
cataract
• Exclusions – ocular
comorbidity, unable to
measure whole eye wavefront
• 206 eyes, age 73 years, 58%
female
• The clinical assessment was
conducted by one clinician-KP
• Refraction and best corrected
• High contrast visual acuity(VA)
• Pelli-Robson contrast
sensitivity
(PRCS)
• Pelli-Robson contrast
sensitivity under glare
(PRCSglare)
Wavefront quality metrics
• Whole eye wavefront sensing
with Wavefront Sciences
COAS-HD
• Wavefront data exported to
VOLPro software v7.25
(Sarver and Associates) and
10th order Zernike expansion
derived
• Zernike data exported to
GetMetrics v.2.02.006
(University of Houston, College
of Optometry) by Thibos and
Applegate for calculation of
wavefront quality metrics
• 31 metrics of wavefront quality
designed to be predictive of
visual performance were
calculated for the pupil plane
and the image plane as per:
Thibos LN, Hong X, Bradley A,
Applegate RA. Accuracy and
precision of objective refraction
from wavefront aberrations. J
Vis 2004;4(4):329-51.
• Linear Regression with SPSS
Software V15.0 (SPSS Inc)
Results - visual acuity and
wavefront quality metrics
• The strongest correlate of all three measures of
visual performance was the pupil fraction metric
PFWc
• Visual acuity and
logPFWc,
r2=-0.37, p<0.001
Results – contrast sensitivity and
wavefront quality metrics
• The strongest correlate of each measure of contrast
sensitivity was the pupil fraction metric PFWc
• Pelli-Robson contrast
sensitivity and logPFWc,
r2=0.39, p<0.001
• Pelli-Robson contrast
sensitivity glare & logPFWc,
r2=0.32, p<0.001
Pupil fraction metrics
• Pupil fraction is defined
as the fraction of the pupil
area for which the optical
quality of the eye is good
• The critical pupil method
uses an “area of good
pupil”
which
is
a
concentric zone
• The red circle indicate the
largest concentric zone
for which the wavefront
has reasonably good
quality
• PFWc which is a critical
pupil defined as the
concentric area for which
RMSw<criterion (λ/4)
Conclusion
• Pupil fraction metrics are the best correlates with
visual performance in cataract, and also have
performed well in normal eyes
Pupil fraction metrics should be used
to organise wavefront aberration data
so as to be predictive of visual
performance