Deep learning-based inpainting of saturation artifacts in optical coherence tomography images

Limited by the dynamic range of the detector,saturation artifacts usually occur in optical coherence tomography(OCT)imaging for high scattering media.The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images.We proposed a deep learning-based inpainting method of saturation artifacts in this paper.The generation mechanism of saturation artifacts was analyzed,and experimental and simulated datasets were built based on the mechanism.Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs.The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility,strong generalization,and robustness.

High-resolution polarization-sensitive optical coherence tomography and optical coherence tomography angiography for zebrafish skin imaging

Zebrafish is an important animal model,which is used to study development,pathology,and genetic research.The zebrafish skin model is widely used in cutaneous research,and angiogenesis is critical for cutaneous wound healing.However,limited by the penetration depth,the available optical methods are difficult to describe the internal skin structure and the connection of blood vessels between the skin and subcutaneous tissue.By a homemade high-resolution polarization-sensitive optical coherence tomography(PS-OCT)system,we imaged the polarization contrast of zebrafish skin and the zebrafish skin vasculature with optical coherence tomography angiography(OCTA).Based on these OCT images,the spatial distribution of the zebrafish skin vasculature was described.Furthermore,we monitored the healing process of zebrafish cutaneous wounds.We think the high-resolution PS-OCT system will be a promising tool in studying cutaneous models of zebrafish.