Comparison of methods to quantify macular and peripapillary vessel density in optical coherence tomography angiography

Abstract

PurposeTo compare macular and peripapillary vessel density values calculated on optical coherence tomography angiography (OCT-A) images with different algorithms, elaborate conversion formula, and compare the ability to discriminate healthy from affected eyes.MethodsCross-sectional study of healthy subjects, patients with diabetic retinopathy, and glaucoma patients (44 eyes in each group). Vessel density in the macular superficial capillary plexus (SCP), deep capillary plexus (DCP), and the peripapillary radial capillary plexus (RCP) were calculated with seven previously published algorithms. Systemic differences, diagnostic properties, reliability, and agreement of the methods were investigated.ResultsHealthy eyes exhibited higher vessel density values in all plexuses compared to diseased eyes regardless of the algorithm used (p<0.01). The estimated vessel densities were significantly different at all the plexuses (p<0.0001) as a function of method used. Inter-method reliability and agreement was mostly poor to moderate. A conversion formula was available for every method, except for the conversion between multilevel and fixed at the DCP. Substantial systemic, non-constant biases were evident between many algorithms. No algorithm outperformed the others for discrimination of patients from healthy subjects in all the retinal plexuses, but the best performing algorithm varied with the selected plexus.ConclusionsAbsolute vessel density values calculated with different algorithms are not directly interchangeable. Differences between healthy and affected eyes could be appreciated with all methods with different discriminatory abilities as a function of the plexus analyzed. Longitudinal monitoring of vessel density should be performed with the same algorithm. Studies adopting vessel density as an outcome measure should not rely on external normative databases.

Normative database and determinants of macular vessel density measured by optical coherence tomography angiography

“…21 The CC image offered currently by OCTA shows a granular pattern of bright and dark areas of varying sizes, while individual capillaries in the CC cannot be seen. 8 Therefore, the same algorithm should be used for longitudinal monitoring of VD. While OCTA shows comparable lateral resolution to structural OCT, it can detect CC blood flow, producing contrast between the RPE and CC.…”

“…While OCTA shows comparable lateral resolution to structural OCT, it can detect CC blood flow, producing contrast between the RPE and CC. 8 The present study offers the largest and most complete SS-OCTA normative database of retinal and CC microvasculature in different plexuses and subfields. When we looked at associations between VDs recorded in the different plexuses, weak to good correlation was observed for the SCP vs DCP (R = .256-.698; P < .001), but correlation between VDs in these retinal plexuses and CC measurements was always weak (from R = .207 to .363; P < .001).…”

“…As stated by Rabiolo et al, it should be highlighted that absolute VD values calculated with different algorithms are not directly interchangeable, so studies adopting VD as an outcome measure should not rely on external normative databases. 8 Therefore, the same algorithm should be used for longitudinal monitoring of VD. As mentioned by Magrath et al, measurements are not comparable between OCTA devices and techniques, as image quality may condition VD measurements.…”

“…5,6 The OCTA devices commercially available use different algorithms for VD quantification. 8 Quantitative retinal microvasculature data have important implications for understanding the physiopathology of many diseases and guiding their management. 8 Quantitative retinal microvasculature data have important implications for understanding the physiopathology of many diseases and guiding their management.…”

“…7 Also, different threshold strategies are used for binarization or skeletonization of the images to calculate VD. 8 Quantitative retinal microvasculature data have important implications for understanding the physiopathology of many diseases and guiding their management.…”