  We present a deep learning framework for volumetric speckle reduction in
optical coherence tomography (OCT) based on a conditional generative
adversarial network (cGAN) that leverages the volumetric nature of OCT data. In
order to utilize the volumetric nature of OCT data, our network takes partial
OCT volumes as input, resulting in artifact-free despeckled volumes that
exhibit excellent speckle reduction and resolution preservation in all three
dimensions. Furthermore, we address the ongoing challenge of generating ground
truth data for supervised speckle suppression deep learning frameworks by using
volumetric non-local means despeckling-TNode to generate training data. We show
that, while TNode processing is computationally demanding, it serves as a
convenient, accessible gold-standard source for training data; our cGAN
replicates efficient suppression of speckle while preserving tissue structures
with dimensions approaching the system resolution of non-local means
despeckling while being two orders of magnitude faster than TNode. We
demonstrate fast, effective, and high-quality despeckling of the proposed
network in different tissue types acquired with three different OCT systems
compared to existing deep learning methods. The open-source nature of our work
facilitates re-training and deployment in any OCT system with an all-software
implementation, working around the challenge of generating high-quality,
speckle-free training data.
