High-speed volumetric amplitude-spectrum dynamic optical coherence tomography by neural network with multi-burst scanning

Abstract

Dynamic optical coherence tomography (DOCT) enables label-free, three-dimensional (3D) assessment of tissue dynamics. However, it suffers from long acquisition times because conventional time-spectrum DOCT requires hundreds of repeated OCT frames per location. Here we present a neural network (NN) framework integrated with a non-uniform-time scanning protocol (multi-burst scan) to accelerate amplitude-spectrum DOCT (AS-DOCT). Combining 3D convolutional and long-short term memory (LSTM) layers with dual inputs (the temporal OCT sequence and its pseudo-amplitude spectrum), the model generates AS-DOCT images from only 16 frames per location. Validated on 29 cancer spheroids, the proposed method resolved distinct functional domain structures with high fidelity (structural similarity index metric (SSIM) > 0.8) and enabled full volumetric AS-DOCT acquisition in 26.2 seconds. This method will enable high-throughput 3D dynamic tissue screening.