  A resolution enhancement technique for optical coherence tomography (OCT),
based on Generative Adversarial Networks (GANs), was developed and
investigated. GANs have been previously used for resolution enhancement of
photography and optical microscopy images. We have adapted and improved this
technique for OCT image generation. Conditional GANs (cGANs) were trained on a
novel set of ultrahigh resolution spectral domain OCT volumes, termed
micro-OCT, as the high-resolution ground truth (~1$\mu$m isotropic resolution).
The ground truth was paired with a low-resolution image obtained by
synthetically degrading resolution 4x in one of (1-D) or both axial and lateral
axes (2-D). Cross-sectional image (B-scan) volumes obtained from in vivo
imaging of human labial (lip) tissue and mouse skin were used in separate
feasibility experiments. Accuracy of resolution enhancement compared to ground
truth was quantified with human perceptual accuracy tests performed by an OCT
expert. The GAN loss in the optimization objective, noise injection in both the
generator and discriminator models, and multi-scale discrimination were found
to be important for achieving realistic speckle appearance in the generated OCT
images. The utility of high resolution speckle recovery was illustrated by an
example of micro-OCT imaging of blood vessels in lip tissue. Qualitative
examples applying the models to image data from outside of the training data
distribution, namely human retina and mouse bladder, were also demonstrated,
suggesting potential for cross-domain transferability. This preliminary study
suggests that deep learning generative models trained on OCT images from
high-performance prototype systems may have potential in enhancing lower
resolution data from mainstream/commercial systems, thereby bringing
cutting-edge technology to the masses at low cost.
