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

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

    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 ...

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