Conventional Optical Coherence Tomography (OCT) suffers from the frame-rate/resolution tradeoff, whereby increasing image resolution leads to decreases in the maximum achievable frame rate. We extended the conventional probabilistic adaptive scanning technique that overcomes this tradeoff with machine-learning-based scene prediction and kinodynamic path planning based on the Clustered Traveling Salesperson Problem. In online imaging, we found that our new technique produces an equivalent frame rate speed-up as previously reported while creating higher quality output OCT images. These results generalized across scenes of varying types, including those of intrasurgical relevance.
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