July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Investigating the feasibility of classifying the cone spectral topography via selective wavelength densitometry.
Author Affiliations & Notes
  • Ramkumar Sabesan
    Ophthalmology, University of Washington, Seattle, Washington, United States
  • Xiaoyun Jiang
    Ophthalmology, University of Washington, Seattle, Washington, United States
  • Footnotes
    Commercial Relationships   Ramkumar Sabesan, None; Xiaoyun Jiang, None
  • Footnotes
    Support  Unrestricted grant from the Research to Prevent Blindness, NIH grant P30EY001730, Research to Prevent Blindness Career Development Award, Burroughs Wellcome Fund Careers at the Scientific Interfaces, Murdock Charitable Trust
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 4051. doi:
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      Ramkumar Sabesan, Xiaoyun Jiang; Investigating the feasibility of classifying the cone spectral topography via selective wavelength densitometry.. Invest. Ophthalmol. Vis. Sci. 2018;59(9):4051.

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

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Purpose : An adaptive optics (AO) fundus camera and an AO scanning laser ophthalmoscope(AOSLO) have been previously used to map the spectral topography of the cone mosaic via selective bleaching followed by 543nm wavelength imaging. Given the increasing resolution, contrast and signal to noise ratio of current generation AO imagers, this study sought to examine whether densitometry with different wavelengths chosen preferentially to favor either L or M cones can isolate their spectral classes.

Methods : A multi-wavelength AOSLO was used to image the cone mosaic following 5 min. of dark adaptation. A 1 deg. retinal field was imaged with a narrow wavelength band (15-20nm) centered at one of 496nm, 543nm, 578nm and 596nm. These wavelengths were chosen based on a tradeoff between optimizing photopigment absorption and simultaneously, maximizing the difference in L and M cone sensitivities. The power at the cornea was kept low and ranged between 2.5-4.6µW to facilitate a relatively slow bleach of photopigment and recovery of image intensity. Seven to 15 videos were recorded per wavelength and registered offline. The intensity of each cone in a selected region-of-interest was tracked and fit with an exponential function. The change in intensity from time t=0 to t=3 was calculated and used for Gaussian mixture model clustering analysis.

Results : The measured optical density at 496nm, 543m,578nm and 596nm averaged over cones was 0.13±0.08, 0.41±0.14, 0.30±0.14 and 0.29±0.12 respectively. Imaging at 496nm with low power at the cornea was impaired by macular pigment absorption resulting in low signal-to-noise and insufficient optical density. The 543nm imaging timecourse was used to delineate S-cones and confirmed with their lack of absorption at 578nm and 596nm as well. Following both 578nm and 596nm densitometry, two clusters of cones emerged from a Gaussian mixture model of changes in intensity following bleach. The mean ratio of cones lying in these two clusters was ~2:1. For 578nm (and 596nm), number of cones with probability less than 0.8 and 0.9 of lying in either cluster was 4.2%(and 8.7%) & 9.1%(and 13.9%) respectively. The two emerging clusters denote putative L and M-cones.

Conclusions : Selective wavelength densitometry offers an efficient way to map cone types without apriori knowledge of individual L:M cone ratios for selective bleaching.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.


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