This retrospective study was approved by the University of California Los Angeles Institutional Review Board and adhered to the tenets of the Declaration of Helsinki and the Health Insurance Portability and Accountability Act regulations. Informed consent was obtained from each subject before OCTA imaging. A total of 100 patients were recruited for this study, and a total of 167 normal eyes were evaluated. The patients and OCTA scans included in this study were the same as was used in our previous study
13; an additional group of 25 healthy patients was also evaluated. The inclusion criteria were volunteer patients from the Stein Eye Institute with no evidence of ocular media opacity, retinal disease, or significant refractive error (myopia of 6 diopters [D] or more or hyperopia of 3 D or more) in the study eye as evaluated by dilated fundus examination. Exclusion criteria included poor-quality images with a signal strength index (SSI) less than 40, significant motion artifact, inaccurate or incorrect segmentation at the level of the SCP, ICP, or DCP, or subject's inability to abstain from blinking or movement during image acquisition.
OCTA imaging was performed using the RTVue XR Avanti spectral-domain OCT device with AngioVue software (Optovue, Fremont, CA, USA). This software (version number 2016.200.0.37) was an updated, noncommercially available version of the software used in our previous study
13 and was very similar to the future commercial version, as it included two important advancements: the PAR algorithm and improved segmentation. The proprietary three-dimensional PAR algorithm developed by Optovue removed projection artifacts from the OCTA volume on a per voxel basis, using information from the OCT and OCTA volume to differentiate in situ OCTA signal from projection artifacts, specifically, parameters derived from the OCTA and OCT intensity profiles anterior to, at, and around the voxel of interest (Hsiao Y, et al.
IOVS 2017;58:ARVO E-Abstract 5998; Wolfson Y, et al.
IOVS 2017;58:ARVO E-Abstract 1669). With three-dimensional PAR-enabled software, projection artifact was removed from en face OCTA images and B-scan OCTA images (
Fig. 1). The improved segmentation was afforded by additional segmentation layers to define the en face slabs for the three plexuses, including the ICP, allowing for more consistent slab definitions across subjects
. The OCTA device used a light source at 840 nm, a bandwidth of 45 nm, and an A-scan rate of 70,000 scans per second. A 3 × 3-mm and a 6 × 6-mm cube scan was acquired, each composed of 304 × 304 scans. Each B-scan was repeated at each cross-section in the fast-scan axis to separate static tissue from blood flow signals. Two OCTA volume scans with orthogonal fast-scan directions (horizontal and vertical) were acquired for each eye and then merged to minimize motion artifact.
14,15 The signal-to-noise ratio was improved with split-spectrum amplitude decorrelation technology.
16
To define the three distinct retinal capillary plexuses, the automated segmentations of the OCTA software were adjusted by the user within the software to offset the boundaries. Several different methods of segmentation based primarily on the known anatomic locations of the plexuses
6 and published reports
10–11 were applied to a subset of eyes to determine which segmentation boundaries effectively visualized the three plexuses. The different segmentation methods were qualitatively evaluated using composite color-coded angiograms of the three plexuses. The different segmentation boundaries were also qualitatively evaluated by setting a thin slab at potential boundaries and assessing which methods resulted in slabs of low flow indicating the interplexus spaces. Based on these two evaluations, we qualitatively determined the three distinct plexuses were effectively visualized as follows: the SCP en face OCTA image was segmented with an inner boundary of 3 μm below the internal limiting membrane and an outer boundary set at the inner plexiform layer (IPL)-inner nuclear layer (INL) junction, thus including the nerve fiber layer (NFL), ganglion cell layer (GCL), and IPL. The ICP en face OCTA image was segmented with an inner boundary set at the IPL-INL junction and an outer boundary set at 20 μm below the IPL-INL junction, thus including approximately the inner half of the INL. The DCP en face OCTA image was segmented with an inner boundary set at 20 μm below the IPL-INL junction and an outer boundary set at 15 μm below the outer plexiform layer (OPL)-outer nuclear layer (ONL) junction, thus including approximately the outer half of the INL, the OPL, and a small portion of the ONL.
Vessel density analysis was performed as previously described.
13 The en face OCTA images were exported to an external software program (ImageJ, version 1.50i;
http://imagej.nih.gov/ij/; provided in the public domain by the National Institutes of Health, Bethesda, MD, USA) and opened in image analysis.
17 The microvascular en face scans were binarized and skeletonized and each blood vessel was illustrated as a 1-pixel-wide line. Vessel density was calculated from the skeletonized images of all scans as ([pixels of vessels] × [scan width in mm/304]/[area in mm
2]) in mm
−1.
18,19 The updated AngioVue software automatically calculated the FAZ area in mm
2 for each eye, using a slab from the internal limiting membrane to 75 μm above the RPE. This protocol was used on the basis of recent studies validating a single merged quantitative measurement of the FAZ.
11,12,20
Statistical analysis was performed using Excel software (Microsoft, Redmond, WA, USA) for Macintosh (2011 version 14.5.2; Apple Inc., Cupertino, CA, USA) and Prism 7.0c for Mac OS X (GraphPad Software, La Jolla, CA, USA). Because vessel density may be correlated between the two eyes of a single patient, one primary eye was selected for analysis at random for each patient to ensure that each data point could be assumed to be independent from each other. Differences between males and females, right and left eyes, and predefined age groups for the primary eye were calculated using independent t-tests assuming unequal variances. The paired t-test was used to compare vessel densities and FAZ area between the primary and the fellow eye. A two-tailed statistic was used for all calculations. Missing variables were not imputed. Statistical significance was set at 0.05. The slope of the linear regression line for the vessel density and FAZ area versus age was used to calculate the respective annual change. An analysis of covariance was used to compare the slopes of the linear regression lines between sexes. Spearman correlation coefficient and linear regression models were used to test the association between age and SSI.