Disk halo size measured in individuals with monofocal versus diffractive multifocal intraocular lenses

PURPOSE: To compare disk halo size in response to a glare source in eyes with an aspheric apodized diffractive multifocal intraocular lens (IOL) or aspheric monofocal IOL. SETTING: Rementeria Ophthalmological Clinic, Madrid, Spain. DESIGN: Prospective randomized masked study. METHOD: Halo radius was measured using a vision monitor (MonCv3) with low-luminance optotypes in eyes that had cataract surgery and bilateral implantion of an Acrysof Restor SN6AD1 multifocal IOL or Acrysof IQ monofocal IOL 6 to 9 months previously. The visual angle subtended by the disk halo radius was calculated in minutes of arc (arcmin). Patient complaints of halo disturbances were recorded. Monocular uncorrected distance visual acutity (UDVA) and corrected distance visual acuity (CDVA) were measured using high-contrast (96%) and low-contrast (10%) logMAR letter charts. RESULTS: The study comprised 39 eyes of 39 subjects (aged 70 to 80 years); 21 eyes had a multifocal IOL and 18 eyes a monofocal IOL. The mean halo radius was 35 arcmin larger in the multifocal IOL group than the monofocal group (P<.05). Greater halo effects were reported in the multifocal IOL group (P<.05). The mean monocular high-contrast UDVA and low-contrast UDVA did not vary significantly between groups, whereas the mean monocular high-contrast CDVA and low-contrast CDVA were significantly worse at 0.12 logMAR and 0.13 logMAR in the multifocal than in the monofocal IOL group, respectively (P <.01). A significant positive correlation was detected by multiple linear regression between the halo radius and low-contrast UDVA in the multifocal IOL group (r = 0.72, P<.001). CONCLUSIONS: The diffractive multifocal IOL gave rise to a larger disk halo size, which was correlated with a worse low-contrast UDVA.

Power spectrum model of visual masking: simulations and empirical data

In the study of the spatial characteristics of the visual channels, the power spectrum model of visual masking is one of the most widely used. When the task is to detect a signal masked by visual noise, this classical model assumes that the signal and the noise are previously processed by a bank of linear channels and that the power of the signal at threshold is proportional to the power of the noise passing through the visual channel that mediates detection. The model also assumes that this visual channel will have the highest ratio of signal power to noise power at its output. According to this, there are masking conditions where the highest signal-to-noise ratio (SNR) occurs in a channel centered in a spatial frequency different from the spatial frequency of the signal (off-frequency looking). Under these conditions the channel mediating detection could vary with the type of noise used in the masking experiment and this could affect the estimation of the shape and the bandwidth of the visual channels. It is generally believed that notched noise, white noise and double bandpass noise prevent off-frequency looking, and high-pass, low-pass and bandpass noises can promote it independently of the channel's shape. In this study, by means of a procedure that finds the channel that maximizes the SNR at its output, we performed numerical simulations using the power spectrum model to study the characteristics of masking caused by six types of one-dimensional noise (white, high-pass, low-pass, bandpass, notched, and double bandpass) for two types of channel's shape (symmetric and asymmetric). Our simulations confirm that (1) high-pass, low-pass, and bandpass noises do not prevent the off-frequency looking, (2) white noise satisfactorily prevents the off-frequency looking independently of the shape and bandwidth of the visual channel, and interestingly we proved for the first time that (3) notched and double bandpass noises prevent off-frequency looking only when the noise cutoffs around the spatial frequency of the signal match the shape of the visual channel (symmetric or asymmetric) involved in the detection. In order to test the explanatory power of the model with empirical data, we performed six visual masking experiments. We show that this model, with only two free parameters, fits the empirical masking data with high precision. Finally, we provide equations of the power spectrum model for six masking noises used in the simulations and in the experiments.