June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
AOSLO Image Quality: Quantifying the Good, the Bad and the Ugly
Author Affiliations & Notes
  • Brea D Brennan
    Joint Department of Biomedical Engineering, Medical College of Wisconsin and Marquette University, Milwaukee, Wisconsin, United States
  • Heather Heitkotter
    Cell Biology, Neurobiology & Anatomy, Medical College of Wisconsin, Milwaukee, Wisconsin, United States
  • Joseph Carroll
    Cell Biology, Neurobiology & Anatomy, Medical College of Wisconsin, Milwaukee, Wisconsin, United States
    Ophthalmology & Visual Sciences, Medical College of Wisconsin, Milwaukee, Wisconsin, United States
  • Robert F Cooper
    Joint Department of Biomedical Engineering, Medical College of Wisconsin and Marquette University, Milwaukee, Wisconsin, United States
    Ophthalmology & Visual Sciences, Medical College of Wisconsin, Milwaukee, Wisconsin, United States
  • Footnotes
    Commercial Relationships   Brea Brennan None; Heather Heitkotter None; Joseph Carroll AGTC, Code C (Consultant/Contractor), Optovue, AGTC, MeiraGTX, Code F (Financial Support), Translational Imaging Innovations, Code I (Personal Financial Interest); Robert Cooper Translational Imaging Innovations, Code C (Consultant/Contractor), Translational Imaging Innovations, Code I (Personal Financial Interest), US Patent App 16/389,942, Code P (Patent)
  • Footnotes
    Support  NIH Grant UL1TR001436, F31EY033204, R44EY031278, R01EY017607 Foundations Support FFB- CC-CL-0620-0785-MRQ CTSI of Southeast Wisconsin: 2020 Traditional Pilot Award
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 220 – F0067. doi:
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    • Get Citation

      Brea D Brennan, Heather Heitkotter, Joseph Carroll, Robert F Cooper; AOSLO Image Quality: Quantifying the Good, the Bad and the Ugly. Invest. Ophthalmol. Vis. Sci. 2022;63(7):220 – F0067.

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

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Abstract

Purpose : Adaptive optics scanning light ophthalmoscopes (AOSLO) are a valuable tool for assessing retinal health. AOSLO image quality can degrade from factors such as poor media quality and eye motion, resulting in lost subject and analysis time. Here we assess the performance of a novel image quality metric against human graders.

Methods : We retrospectively obtained confocal and split-detection images of normative and pathological retinas (>8 diseases) from two AOSLOs with similar designs (Denoted as AOSLO 1 and 2). The dataset was comprised of 125 images from each modality and AOSLO (500 images total) at 1, 2, 4, and 8° from the fovea with field of views between 1 and 3°. For each image, we first determined its quality using a custom algorithm: First, the radial average of the log power spectrum of each image was obtained and differentiated. Next, the differentiated radial average was divided into “signal” and “noise” ranges by selecting a cutoff based on the plausible frequency range of photoreceptors. Finally, a signal to noise ratio (SNR) was determined as the ratio of the summed “signal” and “noise” ranges in decibels. For comparison to a gold standard, three individuals graded the same images (randomized, masked) for cell identifiability on a scale from 0-5, where 0 was considered unanalyzable and 5 easily identifiable. We examined the correlation between log-transformed SNR and grader scores and assessed grader agreement using intra-class correlation (ICC; R package: irr).

Results : On average (±std dev), confocal images had SNR values of 56.2±3.1 and 38.2±6.9 and split-detection images had SNR values of 72.2±1.3 and 30.2±6.4 for AOSLO 1 and 2, respectively. On average, grader scores for confocal images were 3.5±1.3 and 2.4±1.3, and grader scores for split-detection images were 2.5±1.3 and 1.5±1.2 for AOSLO 1 and 2, respectively. These data are summarized in Table 1. All SNR and grader scores exhibited weak (r2<0.35), but significant correlations (p<0.01). Grader ICC was 0.71, implying moderate agreement amongst the graders.

Conclusions : The algorithm presented here successfully quantifies image quality in both confocal and split-detection AOSLO images. On average, graders showed agreement with our algorithm, and moderate agreement with each other.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

SNR vs Grade for each grader (color coded), modality, and AOSLO. In general, the range of SNR in AOSLO 1 was small compared to AOSLO 2.

SNR vs Grade for each grader (color coded), modality, and AOSLO. In general, the range of SNR in AOSLO 1 was small compared to AOSLO 2.

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