September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Methods for Quantification of Image Quality in OCT Angiography Images
Author Affiliations & Notes
  • Carmen Jan Yoo
    Clinical & Applications Development, Carl Zeiss Meditec, Inc, Dublin, California, United States
  • Michael Chen
    Clinical & Applications Development, Carl Zeiss Meditec, Inc, Dublin, California, United States
  • Mary K Durbin
    Clinical & Applications Development, Carl Zeiss Meditec, Inc, Dublin, California, United States
  • Zhongdi Chu
    University of Washington, Seattle, Washington, United States
  • Ruikang K Wang
    University of Washington, Seattle, Washington, United States
  • Chieh-Li Chen
    University of Washington, Seattle, Washington, United States
  • Jesse J Jung
    East Bay Retina Consultants, Oakland, California, United States
  • Scott Lee
    East Bay Retina Consultants, Oakland, California, United States
  • Footnotes
    Commercial Relationships   Carmen Yoo, Carl Zeiss Meditec, Inc. (E); Michael Chen, Carl Zeiss Meditec, Inc. (E); Mary Durbin, Carl Zeiss Meditec, Inc. (E); Zhongdi Chu, Carl Zeiss Meditec, Inc. (C); Ruikang Wang, Carl Zeiss Meditec, Inc. (F), Carl Zeiss Meditec, Inc. (C), Carl Zeiss Meditec, Inc. (R), Carl Zeiss Meditec, Inc. (P); Chieh-Li Chen, Carl Zeiss Meditec, Inc. (C); Jesse Jung, Carl Zeiss Meditec, Inc. (C); Scott Lee, Carl Zeiss Meditec, Inc. (C)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 456. doi:
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    • Get Citation

      Carmen Jan Yoo, Michael Chen, Mary K Durbin, Zhongdi Chu, Ruikang K Wang, Chieh-Li Chen, Jesse J Jung, Scott Lee; Methods for Quantification of Image Quality in OCT Angiography Images. Invest. Ophthalmol. Vis. Sci. 2016;57(12):456.

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

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Abstract

Purpose : OCT angiography (OCTA) image quality is an important factor when interpreting scans for assessment and diagnosis of retinal pathology. Our purpose is to determine the inter-grader repeatability for evaluating the quality of spectral-domain optical coherence tomography angiography (SD-OCTA) images and to correlate quantitative measures to subjective assessment.

Methods : Cirrus AngioPlex OCTA (Carl Zeiss Meditec, Dublin, CA) 3X3 images were acquired from 20 eyes of 20 subjects. Superficial retinal capillary plexus (slab) images were obtained of pathologies including wet age-related macular degeneration, retinal angiomatous proliferation, retinal arteriolar macroaneurysm, branch retinal vein occlusion, sickle-cell maculopathy, central serous retinopathy, and diabetic macular edema. Image selection purposely included sub-optimal images in order to have a broad range of quality; 75% of the images were well segmented. Image quality was graded by two optometrists, one with extensive experience in SD-OCTA analysis and another with limited experience, using a scale of 1=unusable, 2=poor quality, able to see some information, 3=fair, image quality affects ability to assess, 4=good, artifacts, if any, do not interfere with assessment, 5=excellent. Intra-class correlation (ICC) was used to determine inter-grader repeatability. In addition, four image quality metrics were calculated: connectivity of angiogram, angiogram contrast, angiogram signal to noise ration (aSNR) and the number of connected components (NCC). These were correlated to mean subjective grade. The range of the metrics in good scans of 15 normal eyes was also established.

Results : Mean and standard deviation (SD) values for the four metrics are shown in the table. Inter-grader qualitative reproducibility was good with an ICC of 0.76.

Conclusions : Image quality can be evaluated by trained graders with good repeatability. Overall, there is a poor correlation between qualitative and quantitative assessment, but an objective parameter that correlates well to subjective assessment is the NCC. This may be due to the emphasis of the qualitative criterion on pathology assessment and diagnosis.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

 

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