Zebrafish brain imaging is very important for the study of brain disease and regeneration. We scanned the adult zebrafish brain before and after skull removal and monitored the recovery process of a head wound by polarization‐sensitive optical coherence tomography (PS‐OCT) in this paper. We analyzed the structure and polarization characteristics of the brain and skull in PS‐OCT images, and found their internal microstructure can be clearly identified with the polarization information. Further, we estimated the pigment distribution of the skull area and found that the density of pigment in skull is a critical factor of affecting zebrafish brain in vivo polarization imaging. Our results demonstrated that more features of brain can be displayed by introducing the polarization information, and proved high‐resolution PS‐OCT will play a great potential role in studying the zebrafish brain and skull.
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.
We present a deep learning-based digital refocusing approach to extend depth of focus for optical coherence tomography (OCT) in this paper. We built pixel-level registered pairs of en face low-resolution (LR) and high-resolution (HR) OCT images based on experimental data and introduced the receptive field block into the generative adversarial networks to learn the complex mapping relationship between LR-HR image pairs. It was demonstrated by results of phantom and biological samples that the lateral resolutions of OCT images were improved in a large imaging depth clearly. We firmly believe deep learning methods have broad prospects in optimizing OCT imaging.
Super-resolution image reconstruction has become a hot topic with the development of deep learning methods, which have been applied in medical images and shown its great potential application. The available simple and uniform bicubic interpolation down-sampling cannot reflect the actual OCT image degradation. A more realistic low-resolution OCT image generation approach is proposed for training deep neural networks. OCT images with high and low resolutions by multiplying two different spectral widths of the light source are obtained. Three kinds of classical deep learning networks are trained to super-resolve OCT images, and the primary results prove their effectiveness. Super-resolution study for the more realistic low-resolution images is of significance for improving the resolution of OCT system in practice.
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