RTVue XR AngioVue optical coherence tomography angiography software upgrade impacts on retinal thickness and vessel density measurements. Trans Vis Sci Tech. 2020;9(3):10, https://doi.org/10.1167/tvst.9.3.10Purpose: To determine the impact of an AngioVue software upgrade on total retinal thickness (RT) and inner retinal vessel density (VD) measurements derived from optical coherence tomography angiography (OCTA). Methods:Optovue OCTA images (3 × 3 mm) from 126 individuals (105 healthy eyes and 72 eyes with retinal disease) were acquired before an upgrade of the AngioVue software, which resulted in an inward shift of the outer boundary of the inner retinal vessels and improved Bruch's membrane segmentation. Total RT and inner retinal VD values were extracted before and after the software upgrade for comparison. Bias and limits of agreement (LA) were calculated. Results:The mean (SD) age of participants was 46 (17) years. Mean (LA) foveal RT increased by 3.0 (-11 to +17) and 3.7 (-11 to +18) μm (P < 0.001) and parafoveal RT increased by 9.7 (-3.8 to +23) and 6.4 (-2.5 to +15) μm (P < 0.001) in healthy and diseased retina, respectively. Mean (LA) foveal inner retinal VD decreased by 6.6 (2.5-11) and 7.7 (0.4-15) percentage units (P < 0.001) and parafoveal inner retinal VD decreased by 4.1 (1.2-7.0) and 4.7 (0.5-8.9) percentage units (P < 0.001) in healthy and diseased retina, respectively. Conclusions:The AngioVue software upgrade resulted in an unexpected increase in total RT and an expected reduction in inner retinal VD measurements in all regions due to altered segmentation.Translational Relevance: RT and VD measures derived from the newer AngioVue software version are not directly comparable to the reported normative data derived from the older software.

Importance All automated image quality indicators for en face optical coherence tomography angiography (OCTA) images require gold standard validation for determining optimum thresholds. Background A manual grading system (gold standard) for OCTA images was validated and compared to two automated image quality indicators: signal strength index (SSI) and scan quality index (SQI) generated by different software versions of the Optovue OCTA device. Design Retrospective cross‐sectional study. Participants A total of 52 eyes of 52 healthy individual and 77 eyes of 51 patients with retinal vascular diseases. Methods A total of 129 OCTA images of the superficial vascular plexus were graded manually by three independent examiners. Each image was assigned grades 1 to 4 (1‐2, unacceptable; 3‐4, acceptable) masked to the software‐generated quality indicators. Main Outcome Measures Inter‐grader agreement and comparison of the utility of SSI and SQI in discriminating between acceptable and unacceptable OCTA images. Results There was a substantial agreement between the three graders (κ = 0.63). Mean SSI and SQI was significantly different between acceptable and unacceptable images (P < .001). SQI outperformed SSI in separating acceptable from unacceptable images (areas under the receiver operating characteristic curve: 0.87 vs 0.80) and the optimum cut‐off was ≥7 for SQI and ≥70 for SSI for acceptable images. Up to 30% of images with quality indicators reaching the optimum SQI and SSI cut‐off thresholds still had unacceptable quality on manual grading. Unacceptable images were found in 33% and 66% of healthy and diseased eyes, respectively. Conclusions and Relevance SQI is closely related to manual grading but we caution reliance on the optimized threshold to determine image quality. SQI is superior to SSI in discriminating between acceptable and unacceptable images.

Background To generate and validate a method to estimate axial length estimated (ALest) from spherical equivalent (SE) and corneal curvature [keratometry (K)], and to determine if this ALest can replace actual axial length (ALact) for correcting transverse magnification error in optical coherence tomography angiography (OCTA) images using the Littmann-Bennett formula. Methods Data from 1301 participants of the Raine Study Gen2-20 year follow-up were divided into two datasets to generate (n = 650) and validate (n = 651) a relationship between AL, SE, and K. The developed formula was then applied to a separate dataset of 46 participants with AL, SE, and K measurements and OCTA images to estimate and compare the performance of ALest against ALact in correcting transverse magnification error in OCTA images when measuring the foveal avascular zone area (FAZA). Results The formula for ALest yielded the equation: ALest = 2.102K − 0.4125SE + 7.268, R2 = 0.794. There was good agreement between ALest and ALact for both study cohorts. The mean difference [standard deviation (SD)] between FAZA corrected with ALest and ALact was 0.002 (0.015) mm2 with the 95% limits of agreement (LoA) of − 0.027 to 0.031 mm2. In comparison, mean difference (SD) between FAZA uncorrected and corrected with ALact was − 0.005 (0.030) mm2, with 95% LoA of − 0.064 to 0.054 mm2. Conclusions ALact is more accurate than ALest and hence should be used preferentially in magnification error correction in the clinical setting. FAZA corrected with ALest is comparable to FAZA corrected with ALact, while FAZA measurements using images corrected with ALest have a greater accuracy than measurements on uncorrected images. Hence, in the absence of ALact, clinicians should use ALest to correct for magnification error as this provides for more accurate measurements of fundus parameters than uncorrected images.