Quantifying Microvascular Density and Morphology in Diabetic Retinopathy Using Spectral-Domain Optical Coherence Tomography Angiography

Abstract

Purpose: To quantify changes in retinal microvasculature in diabetic retinopathy (DR) by using spectral-domain optical coherence tomography angiography (SD-OCTA).

Methods: Retrospective, cross-sectional, observational study of healthy and diabetic adult subjects with and without DR. Retinal microvascular changes were assessed by using SD-OCTA images and an intensity-based optical microangiography algorithm. A semiautomated program was used to calculate indices of microvascular density and morphology in nonsegmented and segmented SD-OCTA images. Microvascular density was quantified by using skeleton density (SD) and vessel density (VD), while vessel morphology was quantified as fractal dimension (FD) and vessel diameter index (VDI). Statistical analyses were performed by using the Student's t-test or analysis of variance with post hoc Tukey honest significant difference tests for multiple comparisons.

Results: Eighty-four eyes with DR and 14 healthy eyes were studied. Spearman's rank test demonstrated a negative correlation between DR severity and SD, VD, and FD, and a positive correlation with VDI (ρ = -0.767, -0.7166, -0.768, and +0.5051, respectively; P < 0.0001). All parameters showed high reproducibility between graders (ICC = 0.971, 0.962, 0.937, and 0.994 for SD, VD, FD, and VDI, respectively). Repeatability (κ) was greater than 0.99 for SD, VD, FD, and VDI.

Conclusions: Vascular changes in DR can be objectively and reliably characterized with SD, VD, FD, and VDI. In general, decreasing capillary density (SD and VD), branching complexity (FD), and increasing average vascular caliber (VDI) were associated with worsening DR. Changes in capillary density and morphology were significantly correlated with diabetic macular edema.

Figure 1

Representative SD-OCTA and postprocessed images illustrating the semiautomated analysis algorithm. (A) Original, nonsegmented SD-OCTA image. Yellow circles demonstrate the manually selected area that was used as global thresholding. (B) Top-hat filtered image. (C) A binarized image was obtained by using combined adaptive threshold and hessian filter. This image was used for quantification of vessel density. (D) A skeletonized image was obtained by iteratively deleting the pixels in the outer boundary of the binarized image until 1 pixel remained along the width direction of the vessels. This image was used for calculation of skeleton density. The yellow scale bar in (A) shows a distance of 500 μm. This scale applies to (A–D).

Figure 2

Nonsegmented SD-OCTA images with quantitative image outputs of representative subjects in 3×3-mm areas around the fovea. En face representations of retinal perfusion can be viewed as (A–D) 2D grayscale SD-OCTA images of retinal vasculature, with selection of noise thresholding marked with yellow in the foveal avascular zone. (E–H) Contrast-enhanced binarized and (I–L) skeletonized images of retinal perfusion around the macula corresponding to the group labeled in each column. The yellow scale bar in (A) shows a distance of 500 μm. This scale applies to (A–L).

Figure 3

Quantitative analysis of microvascular density and morphology on SD-OCTA images. Graphs of mean skeleton density (A), vessel density (B), fractal dimension (C), and vessel diameter index (D) of normal eyes and eyes affected by mild NPDR, severe NPDR, and PDR. Retinal perfusion indices were quantified in a 3×3-mm area over the macula by using MATLAB software and a novel quantitative algorithm as described in the Methods. Comparisons of these indices showed decreases in SD, VD, and FD along with increases in VDI in any stage of DR when compared to normal eyes. *P < 0.05.