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Updated: Jun 18, 2026

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography
Published on: March 26, 2020
Computational analysis of oxygen transport in the retinal arterial network
Purpose:
The retina has a high oxygen consumption, making it particularly vulnerable to vascular insults, impairing oxygen and nutrient supply. The aim of this study was to develop a detailed computational model for quantitative analysis of blood flow and oxygen transport in physiologically realistic retinal arterial networks. Such a model will allow us to examine the effect of topological changes in retinal vasculature on hemodynamics and oxygen distribution in the retinal circulation.
Materials And Methods:
The Navier-Stokes equations for blood flow and the convection-diffusion equation for oxygen transfer were solved numerically to obtain detailed blood flow and oxygen distribution patterns in a retinal arterial tree. The geometrical outlines of the central retinal artery and its major branches were extracted from retinal images acquired from a healthy young adult by a Zeiss FF450+ fundus camera. The reconstructed subject-specific retinal arterial network geometry was combined with a structured tree model for the distal peripheral vessels. The non-Newtonian rheological properties of blood were incorporated by using an empirical viscosity model to account for the Fahraeus-Lindqvist effect.
Results:
The model predicted pressure drops in the range of 11-14.6 mmHg between the inlet and outlets of the reconstructed network and non-uniform oxygen tension, which varied with the vessel diameter and distance from the optic disc. The mean oxygen saturation in retinal arteries was 93.1% for vessels larger than 50 mum in diameter and 82.2% for smaller arterioles.
Conclusions:
Our numerical results are in good agreement with in vivo measurements reported in the literature, demonstrating the potential of our model for prediction of oxygen distribution and intravascular oxygen tension profiles in the retinal arterial network. This paves the way for investigating the effects of parameter variation, simulating cases not available from experimental studies.
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