June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Quantitative Analysis of Capillary Network Density in Diabetic Retinopathy Using Optical Coherence Tomography with Split-Spectrum Amplitudinal Decorrelation Angiography
Author Affiliations & Notes
  • Steven Agemy
    Ophthalmology, New York Eye & Ear Infirmary of Mount Sinai, New York, NY
  • Jessica Lee
    Ophthalmology, New York Eye & Ear Infirmary of Mount Sinai, New York, NY
  • Patricia Garcia
    Ophthalmology, New York Eye & Ear Infirmary of Mount Sinai, New York, NY
  • Yi-Sing Hsiao
    OptoVue, Inc., Fremont, CA
  • Toco Yuen Ping Chui
    Ophthalmology, New York Eye & Ear Infirmary of Mount Sinai, New York, NY
  • Richard B Rosen
    Ophthalmology, New York Eye & Ear Infirmary of Mount Sinai, New York, NY
  • Footnotes
    Commercial Relationships Steven Agemy, None; Jessica Lee, None; Patricia Garcia, None; Yi-Sing Hsiao, OptoVue, Inc. (E); Toco Chui, None; Richard Rosen, OptoVue, Inc. (C)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 3342. doi:
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      Steven Agemy, Jessica Lee, Patricia Garcia, Yi-Sing Hsiao, Toco Yuen Ping Chui, Richard B Rosen; Quantitative Analysis of Capillary Network Density in Diabetic Retinopathy Using Optical Coherence Tomography with Split-Spectrum Amplitudinal Decorrelation Angiography. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):3342.

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

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Abstract
 
Purpose
 

To quantitatively visualize retinal vascular flow in patients with diabetic retinopathy using Optical Coherence Tomography Angiography and a novel perfusion density mapping software.

 
Methods
 

OCT volumetric images of the macula were obtained (3mm x 3mm and 6mm x 6mm) at 70 kHz A-scans per second using the Optovue Avanti XR OCT system (Fremont, CA). Prototype software employing the split-spectrum amplitudinal decorrelational algorithm (SSADA) was used to construct SD-OCT angiograms of the macular microvasculature. These images were then skeletonized using Matlab software and processed to create topographic maps of vascular density. Average perfusion density was also calculated for the whole volumetric image.

 
Results
 

18 eyes of 10 subjects with nonproliferative diabetic retinopathy, 18 eyes of 9 subjects with proliferative diabetic retinopathy, and 8 eyes of 4 control subjects were imaged. The average perfusion density for the control group was 0.2477 ±0.0639 (3x3) and 0.2702 ±0.1006 (6x6). While the average perfusion density for the NPDR group was significantly reduced at 0.2012 ±0.0694 (3x3) and 0.2474 ±0.1048 (6x6). The PDR group appeared futher reduced at 0.1944 ±0.0692 (3x3) and 0.2402 ±0.1047 (6x6).

 
Conclusions
 

Topographic perfusion density mapping based upon OCT angiography provides an easily interpretable quantitative picture of retinal vascular flow. Using these novel perfusion density maps, differences between normals and diabetic eyes at various stages of retinopathy were easily recognizable. The ability to derive quantitative values for average volumetric perfusion may also prove useful for detecting progression and anticipating the need for more aggressive interventions.  

 
SD-OCT angiography images (3mm x 3mm) of a 42 year old male with proliferative diabetic retinopathy. Top: Enhanced image Bottom: Topographic vessel density map with average perfusion density of 0.1809 ± 0.0701 (skeletonized vessel count/total pixel count)
 
SD-OCT angiography images (3mm x 3mm) of a 42 year old male with proliferative diabetic retinopathy. Top: Enhanced image Bottom: Topographic vessel density map with average perfusion density of 0.1809 ± 0.0701 (skeletonized vessel count/total pixel count)
 
 
SD-OCT angiography images (6mm x 6mm) of a 61 year old female with nonproliferative diabetic retinopathy. Top: Enhanced image Bottom: Topographic vessel density map with average perfusion density of 0.2676 ± 0.1002 (skeletonized vessel count/total pixel count)
 
SD-OCT angiography images (6mm x 6mm) of a 61 year old female with nonproliferative diabetic retinopathy. Top: Enhanced image Bottom: Topographic vessel density map with average perfusion density of 0.2676 ± 0.1002 (skeletonized vessel count/total pixel count)

 
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