July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
PERIPHERAL 2D IMAGE QUALITY METRICS OF DIFFERENT TYPES OF MULTIFOCAL CONTACT LENS
Author Affiliations & Notes
  • Miguel Garcia Garcia
    Carl Zeiss Vision International GmbH, Aalen, Germany
    ZEISS Vision Science Lab, University of Tuebingen, Tübingen, Baden-Württemberg, Germany
  • Siegfried Wahl
    Carl Zeiss Vision International GmbH, Aalen, Germany
    ZEISS Vision Science Lab, University of Tuebingen, Tübingen, Baden-Württemberg, Germany
  • Dibyendu Pusti
    Laboratorio de Optica, Universidad de Murcia, Murcia, Spain
  • Pablo Artal
    Laboratorio de Optica, Universidad de Murcia, Murcia, Spain
  • Arne Ohlendorf
    Carl Zeiss Vision International GmbH, Aalen, Germany
    ZEISS Vision Science Lab, University of Tuebingen, Tübingen, Baden-Württemberg, Germany
  • Footnotes
    Commercial Relationships   Miguel Garcia Garcia, Carl Zeiss Vision International (E), Carl Zeiss Vision International (F), European Union (F); Siegfried Wahl, Carl Zeiss Vision International (E), Carl Zeiss Vision International (F); Dibyendu Pusti, None; Pablo Artal, None; Arne Ohlendorf, Carl Zeiss Vision International (F), Carl Zeiss Vision International (E)
  • Footnotes
    Support  Horizon 2020 Marie Sklodowska-Curie grant agreement No 675137
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 3719. doi:
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      Miguel Garcia Garcia, Siegfried Wahl, Dibyendu Pusti, Pablo Artal, Arne Ohlendorf; PERIPHERAL 2D IMAGE QUALITY METRICS OF DIFFERENT TYPES OF MULTIFOCAL CONTACT LENS. Invest. Ophthalmol. Vis. Sci. 2019;60(9):3719.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To evaluate the impact of different designs of multifocal contact lenses on the peripheral image quality metrics.

Methods : Series of Hartmann-Shack images were recorded by using an open-view high-resolution peripheral wave-front sensor (VPR, Voptica SL, Murcia, Spain) in 13 young myopic subjects (mean spherical equivalent error: -3.25D; range: -0.75 to -6.50D). For each eye, horizontal scans covering 40° of visual field were obtained at five different vertical fixations (0°, +10°, +20°, -10°, -20°). Measurements were taken while subjects were wearing: 1) no lens 2) a multifocal center-distance (MFCD) or 3) a multifocal center-near (MFCN) soft contact lens (Xtensa by mark'ennovy; distance Rx: -0.25DS). At each peripheral angle, the point spread function (PSF) was calculated and used to convolve the “Lena” image. After this, the convolved images passed a low pass filter to remove the spatial frequencies that where above the cutoff spatial frequency for the corresponding eccentricity. Subsequently, the filtered images were cropped and compared with the original image, to obtain 2-D cross-correlation values as an image quality metrics.
This was used to determine 2D significance maps, based on the cross-correlation values for all the subjects and conditions. Additionally, one significance map was defined by using the theoretical spatial frequency limit. The differences (root mean square errors: RMSE) between the 2-D significance maps and the cut-off map were used to quantify the overall differences produced by each condition for each subject.

Results : While analyzing the full visual field, the naked eye condition (1) obtained the highest similarity to the ideal image quality (RMSE=0.2315). In contrast, the MFCN design significantly degraded the overall quality metric (RMSE= 0.2897; p=0.0166), while the MFCD did not (RMSE=0.2720, p=0.3552).

Conclusions : The amount of blur on the retina can be modified by optical treatments such as contact lenses. A better understanding of the image quality with different optical treatments can shed some light on the efficacy of different solutions for myopia management.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

Figure 1. Significance maps regarding the image quality metrics for the different conditions for one subject. Correlation coefficients ranged from 0 to 1, with 1 being perfect image quality.

Figure 1. Significance maps regarding the image quality metrics for the different conditions for one subject. Correlation coefficients ranged from 0 to 1, with 1 being perfect image quality.

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