  We demonstrate deep-learning neural network (NN)-based dynamic optical
coherence tomography (DOCT), which generates high-quality
logarithmic-intensity-variance (LIV) DOCT images from only four OCT frames. The
NN model is trained for tumor spheroid samples using a customized loss
function: the weighted mean absolute error. This loss function enables highly
accurate LIV image generation. The fidelity of the generated LIV images to the
ground truth LIV images generated using 32 OCT frames is examined via
subjective image observation and statistical analysis of image-based metrics.
Fast volumetric DOCT imaging with an acquisition time of 6.55 s/volume is
demonstrated using this NN-based method.
