June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Analysis of the Photoreceptor Mosaic in Patients with Each Stage of Dry Age-Related Macular Degeneration (AMD): Cone Density and Spacing as Image-Based Biomarkers
Author Affiliations & Notes
  • Adam Boretsky
    Center for Biomedical Engineering, Univ of Texas Medical Branch, Galveston, TX
  • Faraz Khan
    Center for Biomedical Engineering, Univ of Texas Medical Branch, Galveston, TX
  • Frederik van Kuijk
    Ophthalmology and Visual Neuroscience, University of Minnesota, Minneapolis, MN
  • Massoud Motamedi
    Center for Biomedical Engineering, Univ of Texas Medical Branch, Galveston, TX
  • Footnotes
    Commercial Relationships Adam Boretsky, None; Faraz Khan, None; Frederik van Kuijk, None; Massoud Motamedi, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 3441. doi:
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      Adam Boretsky, Faraz Khan, Frederik van Kuijk, Massoud Motamedi; Analysis of the Photoreceptor Mosaic in Patients with Each Stage of Dry Age-Related Macular Degeneration (AMD): Cone Density and Spacing as Image-Based Biomarkers. Invest. Ophthalmol. Vis. Sci. 2013;54(15):3441.

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

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Abstract
 
Purpose
 

To use high resolution imaging to objectively measure photoreceptor density and spacing in patients with dry age-related macular degeneration (AMD).

 
Methods
 

A custom built adaptive optics confocal scanning laser ophthalmoscope (AO-SLO) was used to acquire high resolution reflectance images of the macular cone photoreceptor mosaic in 20 patients comprising each category of dry AMD. An 830 nm Superluminescent diode (SLD) was used for both image acquisition and wavefront sensing. Photoreceptor mosaics were generated by automated registration and averaging of multiple images from video sequences. Particular emphasis was placed on macular disruption associated with the formation of drusen observed using fundus photography and spectral domain optical coherence tomography (SD-OCT). We used an automated algorithm to identify individual photoreceptors and calculate local density values. Additionally, we performed Delaunay triangulation to characterize the mean spacing (center-to-center) of the cone photoreceptors within 0.25° x 0.25° subsets of each mosaic.

 
Results
 

Photoreceptor density measurements varied greatly depending on the retinal eccentricity and were heavily influenced by the presence of vasculature and pathology. Segmenting the image into small regions of interest (ROIs) enabled us to differentiate between decreased photoreceptor density due to vasculature versus a reduction in cones due to drusen or geographic atrophy. This data was used to generate heatmaps of the local photoreceptor density. Mean cone spacing was also measured for each ROI to identify areas with increased spacing above one standard deviation from the mean.

 
Conclusions
 

Cone photoreceptor density and spacing have the potential to be used as image-based biomarkers of AMD. Objective analysis of the photoreceptor mosaic in patients with dry AMD may provide an additional metric to assess disease progression and establish a correlation between photoreceptor loss and a decline in visual function. Additionally, AO-SLO may offer insight into the efficacy of emerging dry AMD therapies.

 
 
Analysis of the local variations in the photoreceptor mosiac was performed to help identify areas of decreased cone density and an increase in photoreceptor spacing in patients with dry age-related macular degeneration.
 
Analysis of the local variations in the photoreceptor mosiac was performed to help identify areas of decreased cone density and an increase in photoreceptor spacing in patients with dry age-related macular degeneration.
 
Keywords: 412 age-related macular degeneration • 551 imaging/image analysis: non-clinical • 504 drusen  
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