  Reducing the bit-depth is an effective approach to lower the cost of optical
coherence tomography (OCT) systems and increase the transmission efficiency in
data acquisition and telemedicine. However, a low bit-depth will lead to the
degeneration of the detection sensitivity thus reduce the signal-to-noise ratio
(SNR) of OCT images. In this paper, we propose to use deep learning for the
reconstruction of the high SNR OCT images from the low bit-depth acquisition.
Its feasibility was preliminarily evaluated by applying the proposed method to
the quantized $3\sim8$-bit data from native 12-bit interference fringes. We
employed a pixel-to-pixel generative adversarial network architecture in the
low to high bit-depth OCT image transition. Retinal OCT data of a healthy
subject from a homemade spectral-domain OCT system was used in the study.
Extensively qualitative and quantitative results show this deep-learning-based
approach could significantly improve the SNR of the low bit-depth OCT images
especially at the choroidal region. Superior similarity and SNR between the
reconstructed images and the original 12-bit OCT images could be derived when
the bit-depth $\geq 5$. This work demonstrates the proper integration of OCT
and deep learning could benefit the development of healthcare in low-resource
settings.
