Quantitative optical coherence tomography angiography: A review

Abstract

As a new optical coherence tomography (OCT) modality, OCT angiography (OCTA) provides a noninvasive method to detect microvascular distortions correlated with eye conditions. By providing unparalleled capability to differentiate individual plexus layers in the retina, OCTA has demonstrated its excellence in clinical management of diabetic retinopathy, glaucoma, sickle cell retinopathy, diabetic macular edema, and other eye diseases. Quantitative OCTA analysis of retinal and choroidal vasculatures is essential to standardize objective interpretations of clinical outcome. Quantitative features, including blood vessel tortuosity, blood vessel caliber, blood vessel density, vessel perimeter index, fovea avascular zone area, fovea avascular zone contour irregularity, vessel branching coefficient, vessel branching angle, branching width ratio, and choroidal vascular analysis have been established for objective OCTA assessment. Moreover, differential artery–vein analysis has been recently demonstrated to improve OCTA performance for objective detection and classification of eye diseases. In this review, technical rationales and clinical applications of these quantitative OCTA features are summarized, and future prospects for using these quantitative OCTA features for artificial intelligence classification of eye conditions are discussed.

Impact statement: OCT angiography (OCTA) provides a noninvasive method to detect microvascular distortions correlated with eye conditions. Quantitative analysis of OCTA is essential to standardize objective interpretations of clinical outcome. This review summarizes technical rationales and clinical applications of quantitative OCTA features.

Keywords: Optical coherence tomography angiography; classification; diabetic retinopathy; eye condition; eye disease; quantitative analysis; retinopathy; sickle cell retinopathy.

Figure 1.

Feature extraction for quantitative OCTA analysis. (a) Representative OCTA image from a DR patient. (b) Segmented blood vessel map. (c) Skeletonized blood vessel map (red) with segmented fovea (blue region) and FAZ contour (green curve). One representative vessel branch is highlighted in green with X and Y endpoints identified with yellow dots. (d) Vessel perimeter map. (e) FD contour map. Source: Modified from Alam et al. (A color version of this figure is available in the online journal.)

Figure 2.

(a) Sample branchpoint. (b) Branchpoint in a vessel skeleton, where the green pixel represents the branchpoint, the red pixels represent the end points, the blue pixels represent our vessels of interest, and the yellow circle represents the dilated area. (c) A composite image of the branchpoint (green) and endpoint (red), where the yellow square represents the window area. (D) Branch angle measurement. Angles A and B in the left image are complementary angles used to calculate θ, α1, and α2 in the right image. Source: Modified from Le et al. (A color version of this figure is available in the online journal.)

Figure 3.

A–V classification using fundus guided (row 1) and en-face OCT guided (row 2) techniques developed by Alam et al.^, (a) Sample fundus image, (b) corresponding OCTA image, (c) OCTA A–V map overlaid on the fundus image, (d) sample OCT en-face image, (e) A–V information from en-face OCT overlaid on OCTA binary vessel map, and (f) OCTA A–V map. Source: Modified from Alam et al.^, (A color version of this figure is available in the online journal.)

Figure 4.

Method of determining CNV area and skeleton. Illustrative steps of generating the CNV area and skeleton from the original outer retinal en-face OCTA image using a saliency model. Source: Reprinted from Patel et al.(A color version of this figure is available in the online journal.) CNV: choroidal neovascular.