The pathophysiology of glaucoma is still unclear, and the velocity of disease progression is hard to predict. OCT allows to quantify anatomical characteristics that can be correlated with glaucoma stage, but new dynamic biomarkers based on tissue biomechanics are actively sought. However, noise in OCT images hampers the detailed analysis of time series. Here, we present a method to stabilize and denoise OCT videos using the redundancy of the data to create single heart cycle videos, which allow a precise analysis of the movement of the tissues. This approach is computationally low-cost, simple to execute and easy to implement in a clinical setting.
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