Repeatability of adaptive optics automated cone measurements in subjects with retinitis pigmentosa and novel metrics for assessment of image quality

Abstract

Purpose: We determine the intersession repeatability of cone measurements via flood-illuminated adaptive optics (AO) imaging in patients with retinitis pigmentosa (RP), to better differentiate variation due to imaging inaccuracies versus pathology-driven change. Methods: A total of 25 4°X4° AO images were acquired three times on the same day in 10 subjects with RP, registered in i2K Retina, and cones were identified using a custom-built MATLAB algorithm. Nine equally spaced regions of interest were selected for each imaging set. A subset of subjectively ‘‘poor’’ and ‘‘good’’ quality images was selected by three independent graders, analyzed using cone density, cone location similarity (CLS) and cone spacing, and compared to age-matched normals. Results: The coefficient of variation (CoV), repeatability, and percent repeatability of automated cone density were slightly higher in patients with RP compared to age-matched normals, but showed no statistically significant difference. The standard deviation of CLS and cone spacing of nearest-neighbor distance demonstrated a statistically significant difference between good-and poor-quality images. Conclusions: Repeatability of automated cone density measurements in patients with RP is comparable to normals. Misidentification of cones due to image quality variability is a major limitation of automated cone counting algorithms in patients with RP. Our study suggests that CLS and cone spacing metrics could be used to help define image quality and, thus, increase confidence in automated cone counts in patients with RP. Translational Relevance: The novel AO image quality assessment metrics described in our study could help to improve patient image interpretation, prognosis, and longitudinal care.