June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Repeatability of Cone Density Measurements in Healthy Subjects Using Commercially Available Flood-Illuminated Adaptive Optics
Author Affiliations & Notes
  • Shu Feng
    Casey Eye Institute, Oregon Health & Sciences University, Portland, OR
  • Anupam Garg
    Casey Eye Institute, Oregon Health & Sciences University, Portland, OR
  • Ambar Faridi
    Casey Eye Institute, Oregon Health & Sciences University, Portland, OR
  • Jonathan Fay
    Casey Eye Institute, Oregon Health & Sciences University, Portland, OR
  • Hope Titus
    Casey Eye Institute, Oregon Health & Sciences University, Portland, OR
  • Travis Smith
    Casey Eye Institute, Oregon Health & Sciences University, Portland, OR
  • Keith Michaels
    Casey Eye Institute, Oregon Health & Sciences University, Portland, OR
  • Mark Pennesi
    Casey Eye Institute, Oregon Health & Sciences University, Portland, OR
  • Footnotes
    Commercial Relationships Shu Feng, None; Anupam Garg, None; Ambar Faridi, None; Jonathan Fay, None; Hope Titus, None; Travis Smith, Pfizer, Inc. (F); Keith Michaels, Pfizer, Inc. (F); Mark Pennesi, Pfizer (F)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 3437. doi:https://doi.org/
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      Shu Feng, Anupam Garg, Ambar Faridi, Jonathan Fay, Hope Titus, Travis Smith, Keith Michaels, Mark Pennesi; Repeatability of Cone Density Measurements in Healthy Subjects Using Commercially Available Flood-Illuminated Adaptive Optics. Invest. Ophthalmol. Vis. Sci. 2013;54(15):3437. doi: https://doi.org/.

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

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Abstract

Purpose: To evaluate a commercially available flood-illuminated adaptive optics camera for its repeatability in measuring the cone mosaic and to determine average cone density among healthy subjects

Methods: We used the RTX1 flood-illuminated adaptive optics camera (Imagine Eyes: Orsay, France) to image the cone mosaic in 54 healthy subjects, ages 14 to 69 years. For each subject, a series of 25 4°x4° retinal images were obtained. Using MosaicJ, these images were combined to create a retinal montage spanning a 12°x12° field of the central macula. Retinal montages were analyzed for regional differences in cone density. Images were post processed in ImageJ and cone counting was performed with a MATLAB algorithm. To determine repeatability of the device and montaging process, a subset of 7 subjects ranging in age from 19 to 36 years were imaged on 3 separate occasions. To assess the validity of automated cone counting for each montage, automated cone counts were compared to manual counts in sample retinal areas 2° and 4° temporal to the foveal center.

Results: Image quality was excellent in most young subjects, but increasingly variable in older subjects. Cones within 1.5° of the foveal center could not be resolved with this camera. Angular density of cones averaged 1587 ± 91 cones/degree2 at 1.6° - 4.3° eccentricity and 1428 ± 64 cones/degree2 between 4.3° and 5.4° eccentricity. When adjusted for axial length, cone density averaged 18,688 ± 2081 cones/mm2 between 1.6° and 4.3° and 16,763 ± 1669 cones/mm2 between 4.3° and 5.4°. Angular cone density between 1.6° and 4.3° decreased with age, but between 4.3° and 5.4° increased. Repeated measurements of cone density in the same subject from separate imaging sessions resulted in an intraclass correlation coefficient of 0.98 (p < 0.001, 95% CI: 0.93-1.00) between 1.6° and 4.3° and 0.95 (p < 0.001, 95% CI: 0.83-0.99) between 4.3° and 5.4°. However, validity of the automated cone counting algorithm depended on image quality, with poor quality images producing higher variability.

Conclusions: Flood-illuminated adaptive optics provides measurements of cone density that are consistent with anatomical studies. Repeated measurements in a subset of younger subjects resulted in very strong intraclass correction coefficients, which indicate the system produced consistent measurements. However, challenges remain with older subjects.

Keywords: 550 imaging/image analysis: clinical  
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