High-speed full-field swept-source dynamic optical coherence tomography enabled by neural-network-based image...

We demonstrate neural-network-based high-speed and high-definition dynamic full-field swept-source optical coherence microscopy (D-FF-SSOCM). FF-SSOCM provides high-definition OCT images, but, particularly in DOCT imaging, it results in a significant enlargement of the data size and subsequently long data streaming and processing time, which prevents high-throughput imaging. We address this issue by introducing an NN-based DOCT method that generates high-definition logarithmic intensity variance (LIV) -based DOCT images from only four OCT volumes, whereas the conventional method required 32 volumes. The NN model successfully generates an LIV image that is qualitatively and quantitatively similar to the LIV image computed from 32 volumes. This approach significantly reduces data size and transfer time by a factor of eight for DOCT imaging, and consequently, the signal processing time is also significantly reduced. Specifically, these were reduced from 41 GB to 5.1 GB, 7.2 min to 54 s, and 3.3 hours to 57 min, respectively.