ArticleScientific reports2020
A Neural Network Approach to Quantify Blood Flow from Retinal OCT Intensity Time-Series Measurements.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- Accurate flow speed measurement through correlation ratios using a mode-selective photonic lantern in optical coherence tomography.Biomedical optics express · 2025Article
- Full-field amplitude speckle decorrelation angiography.Biomedical optics express · 2024Article
- Colorimetric Analyses of the Optic Nerve Head and Retina Indicate Increased Blood Flow After Vitrectomy.Translational vision science & technology · 2024Article
- Time-Resolved Dynamic Optical Coherence Tomography for Retinal Blood Flow Analysis.Investigative ophthalmology & visual science · 2024Article
- Experimental and theoretical model of microvascular network remodeling and blood flow redistribution following minimally invasive microvessel laser ablation.Scientific reports · 2024Article
- Article
- Current State of Knowledge in Ocular Blood Flow in Glaucoma: A Narrative Review.Clinical ophthalmology (Auckland, N.Z.) · 2023Review
- Using the dynamic forward scattering signal for optical coherence tomography based blood flow quantification.Optics letters · 2022Article
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.