High-speed multicontrast dynamic OCT by using deep learning

We proposed a deep learning approach to significantly accelerate multi-contrast dynamic optical coherence tomography (MC-DOCT) imaging. We trained a three-dimensional UNet with a long short-Term memory module to simultaneously generate authentic logarithmic intensity variance (aLIV) and swiftness images from only 4 OCT frames with nonuniform time intervals, instead of conventional 32 frames. This method accurately visualized multiple functional domains in in vitro samples such as breast cancer spheroids, colon cancer spheroids and alveolar organoids, which is consistent with ground truth computed from 32 OCT frames. Our method provides a promising solution for reducing MC-DOCT acquisition time from 1 minute to 6 seconds.