A Neural Network Approach to Quantify Blood Flow from Retinal OCT Intensity Time-Series Measurements

Boy Braaf,Sabine Donner,Brett E Bouma,Benjamin J Vakoc,Néstor Uribe-Patarroyo

doi:10.1038/s41598-020-66158-8

Abstract

Many diseases of the eye are associated with alterations in the retinal vasculature that are possibly preceded by undetected changes in blood flow. In this work, a robust blood flow quantification framework is presented based on optical coherence tomography (OCT) angiography imaging and deep learning. The analysis used a forward signal model to simulate OCT blood flow data for training of a neural network (NN). The NN was combined with pre- and post-processing steps to create an analysis framework for measuring flow rates from individual blood vessels. The framework’s accuracy was validated using both blood flow phantoms and human subject imaging, and across flow speed, vessel angle, hematocrit levels, and signal-to-noise ratio. The reported flow rate of the calibrated NN framework was measured to be largely independent of vessel angle, hematocrit levels, and measurement signal-to-noise ratio. In vivo retinal flow rate measurements were self-consistent across vascular branch points, and approximately followed a predicted power-law dependence on the vessel diameter. The presented OCT-based NN flow rate estimation framework addresses the need for a robust, deployable, and label-free quantitative retinal blood flow mapping technique.

Highlights

Many retinal diseases are associated with abnormalities in perfusion with primary examples including age-related macular degeneration, diabetic retinopathy, and glaucoma[1,2,3,4,5,6]
In order to facilitate the visual comparison between the neural network (NN) and the Doppler optical coherence tomography (OCT) results, the latter was corrected for the Doppler angle and displays total flow
The NN analysis was validated in vivo by showing flow rate preservation for vessel bifurcations and by verifying the expected power function relation between flow rate and vessel diameter

Summary

Introduction

Many retinal diseases are associated with abnormalities in perfusion with primary examples including age-related macular degeneration, diabetic retinopathy, and glaucoma[1,2,3,4,5,6]. Flow stochastically modulates OCT intensity[24,25], and it is not straightforward to estimate flow velocity from a time-series OCT intensity measurement This is especially true when the number of measurements in the time-series is minimized[21], as is critical in retinal imaging as the overall imaging duration is limited by eye motion[26]. Other effects such as Brownian motion[27], multiple-scatting[28], and intravoxel flow velocity gradients[29] affect the time-series intensity modulation and further complicate the extraction of accurate flow information. The framework was further validated in human retina measurements in vivo by confirming the conservation of flow rate across vessel branch points, and by confirming a prior reported power-law relationship between flow rate and blood vessel diameter

Objectives

Results

Conclusion