Applying a Pix2Pix Generative Adversarial Network to a Fourier-Domain Optical Coherence Tomography System for Artifact Elimination

Abstract

The presence of artifacts, including conjugate, DC, and auto-correlation artifacts, is a critical limitation of Fourier-domain optical coherence tomography (FD-OCT). Many methods have been proposed to resolve this problem to obtain high-quality images. Furthermore, the development of deep learning has resulted in many prospective advancements in the medical field; image-to-image translation by using generative adversarial networks (GANs) is one such advancement. In this study, we propose applying the Pix2Pix GAN to eliminate artifacts from FD-OCT images. The first experiment results showed that the proposed framework could translate conventional FD-OCT depth profiles into artifact-free FD-OCT depth profiles. In addition, the FD-OCT depth profile and optical distance of translated images matched those of ground truth images. Second experiment verified that the proposed GAN-based FD-OCT can be applied to generate artifact-free FD-OCT image with different parameters of sample refractive index, the front surface of the sample toward the zero-delay position, and the physical thickness of the sample. Third experiment proved that the proposed model could translated the conventional FD-OCT depth profiles with additional Gaussian noises source image into artifacts-free FD-OCT and successfully relieved the noise.