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 criti...

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

Achieving high resolution in optical coherence tomography typically requires the continuous extension of the spectral bandwidth of the light source. This work demonstrates an alternative approach: combining two discrete spectral windows located in the visible spectrum with a trained conditional generative adversarial network (cGAN) to reconstruct a high-resolution image equivalent to that generated using a continuous spectral band. The cGAN was trained using OCT image pairs acquired with the continuous and discontinuous visible range spectra to learn the relation between low- and high-resolution data. The reconstruction performance was tested using 6000 B-scans of a layered phantom, micro-beads and ex-vivo mouse ear tissue. The resultant cGAN-generated images demonstrate an image quality and axial resolution which approaches that of the high-resolution system.

Reconstruction of visible light optical coherence tomography images retrieved from discontinuous spectral data using a conditional generative adversarial network.

Here we demonstrate a long-depth-of-focus imaging method using polarization sensitive optical coherence tomography (PS-OCT). This method involves a combination of Fresnel-diffraction-model-based phase sensitive computational refocusing and Jones-matrix based PS-OCT (JM-OCT). JM-OCT measures four complex OCT images corresponding to four polarization channels. These OCT images are computationally refocused as preserving the mutual phase consistency. This method is validated using a static phantom, postmortem zebrafish, and ex vivo porcine muscle samples. All the samples demonstrated successful computationally-refocused birefringence and degree-of-polarization-uniformity (DOPU) images. We found that defocusing induces polarization artifacts, i.e., incorrectly high birefringence values and low DOPU values, which are substantially mitigated by computational refocusing. 

Computational refocusing of Jones matrix polarization-sensitive optical coherence tomography and investigation of defocus-induced polarization artifacts

Here we demonstrate a long-depth-of-focus imaging method using polarization sensitive optical coherence tomography (PS-OCT). This method involves a combination of Fresnel-diffraction-model-based phase sensitive computational refocusing and Jones-matrix based PS-OCT (JM-OCT). JM-OCT measures four complex OCT images corresponding to four polarization channels. These OCT images are computationally refocused as preserving the mutual phase consistency. This method is validated using a static phantom, postmortem zebrafish, and ex vivo porcine muscle samples. All the samples demonstrated successful computationally-refocused birefringence and degree-of-polarization-uniformity (DOPU) images. We found that defocusing induces polarization artifacts, i.e., incorrectly high birefringence values and low DOPU values, which are substantially mitigated by computational refocusing.

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.

Digital refocusing based on deep learning in optical coherence tomography.

Optical coherence tomography (OCT) is a noninvasive, high resolution, and real-time imaging technology that has been used in ophthalmology and other medical fields. Limited by the point spread function of OCT system, it is difficult to optimize its spatial resolution only based on hardware. Digital image processing methods, especially deep learning, provide great potential in super-resolving images. In this paper, the matched axial low resolution (LR) and high resolution OCT image pairs from actual OCT imaging are collected to generate the dataset by our home-made spectral domain OCT (SD-OCT) system. Several methods are selected to super-resolve LR OCT images. It is shown from the experimental results that the residual-in-residual dense block network (RRDBNet) trained with different loss functions performs the best super-resolution for OCT images, and it is demonstrated from the preliminary results that deep learning methods have good generalization and robustness between OCT systems. We believe deep learning methods have broad prospects in improving the quality of OCT images.

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Yanmei Liang

Papers

Axial Super-Resolution Study for Optical Coherence Tomography Images Via Deep Learning

More realistic low-resolution OCT image generation approach for training deep neural networks