Three-dimensional spatiotemporal analysis for the assessment of retinal capillary perfusion using a clinical OCT system

Y Yudan Chen J Jun Song (Department of Mining ang Materials Engineering, McGill University) H Hoyoung Jung T Tiffany Tse V Valerie Mok J Jennifer Tsang Z Zaid Mammo M Myeong Jin Ju

Abstract

Abstract Growing evidence suggests that subtle changes in retinal microcirculation may precede structural damage in vision-threatening diseases. Among these, perfusion heterogeneity within the retinal capillary network has emerged as a promising biomarker for early detection and disease monitoring. Recent advances in optical coherence tomography (OCT) and OCT-based angiography (OCTA) have enabled high-resolution, three-dimensional imaging of retinal morphology and vasculature. However, commercial systems remain limited in their ability to accurately analyze retinal perfusion dynamics due to reliance on proprietary and undisclosed post-processing algorithms. This paper introduces an effective protocol for spatial and temporal analysis of capillary perfusion heterogeneity using unprocessed OCTA volume data acquired by a commercial retinal imaging system. The proposed method employs a novel analysis utilizing the depth-resolved pixel-wise coefficient of variation (CoV) to quantitatively estimate retinal capillary perfusion heterogeneity. Comparison between the proposed method and conventional CoV analysis emphasizes the reliability of the new approach, incorporating depth-dependent signals. By using unprocessed OCTA data, the proposed method can provide more accurate measurements of retinal perfusion heterogeneity. Furthermore, the image processing techniques developed in this study could serve as a foundation for future research in other retinal vascular disorders.

Article Details

Volume / Issue Vol. 15, Issue 1
Published October 22, 2025
ISSN 2045-2322
Publisher Nature Portfolio

Journal Info

Scientific Reports

Nature Portfolio

ISSN: 2045-2322 Open Access Life Sciences

Authors (8)

Y

Yudan Chen

J

Jun Song

Department of Mining ang Materials Engineering, McGill University

H

Hoyoung Jung

T

Tiffany Tse

V

Valerie Mok

J

Jennifer Tsang

Z

Zaid Mammo

M

Myeong Jin Ju