Real-time neural-network-based denoising for intraoperative 4D-OCT

Noise decreases image quality in optical coherence tomography (OCT) and can obscure important features in real-time visualizations. In this work, we show that a neural network can be applied to denoise volumetric OCT data for intra-surgical visualization in real-time. We adapt a self-supervised training approach, not requiring any paired data for training. Several optimizations and trade-offs in deployment are required, with which we achieved processing times of only few milliseconds. While still being limited by the real-time requirements, denoising in this scenario can enhance surface visibility, and therefore allow guidance for more precise intra-surgical maneuvers.