Cheng X, Bradley A, Ravikumar S, Thibos LN. Visual impact of Zernike and Seidel forms of monochromatic aberrations.
Optom Vis Sci 2010;
87:300-12. [PMID:
20351600 PMCID:
PMC3144141 DOI:
10.1097/opx.0b013e3181d95217]
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Abstract
PURPOSE
The aim of this study was to examine the impact of different aberrations modes (e.g., coma, astigmatism, spherical aberration [SA]) and different aberration basis functions (Zernike or Seidel) on visual acuity (VA).
METHODS
Computational optics was used to generate retinal images degraded by either the Zernike or Seidel forms of second through fourth-order aberrations for an eye with a 5-mm pupil diameter. High contrast, photopic VA was measured using method of constant stimuli for letters displayed on a computer-controlled, linearized, quasimonochromatic (lambda = 556 nm) display.
RESULTS
Minimum angle of resolution (MAR) varied linearly with the magnitude (root mean square error) of all modes of aberration. The impact of individual Zernike lower- and higher-order aberrations (HOAs) varied significantly with mode, e.g., arc minutes of MAR per micrometer of root mean square slopes varied from 7 (spherical defocus) to 0.5 (quadrafoil). Seidel forms of these aberrations always had a smaller visual impact. Notably, Seidel SA had 1/17th the impact of Zernike SA with the same wavefront variance, and about 1/4th the impact of Zernike SA with matching levels of r wavefront error. With lower-order components removed, HOAs near the center of the Zernike pyramid do not have a large visual impact.
CONCLUSIONS
The majority of the visual impact of high levels of fourth-order Zernike aberrations can be attributed to the second-order terms within these polynomials. Therefore, the impact of SA can be minimized by balancing it with a defocus term that flattens the central wavefront (paraxial focus) or maximizes the area of the pupil with a flat wavefront. Over this wide range of aberration types and levels, image quality metrics based on the Point Spread Function (PSF) and Optical Transfer Function (OTF) can predict VA as reliably as VA measures can predict retests of VA, and, thus, such metrics may become valuable predictors of both VA and, via optimization, refractions.
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