A resolution enhancement technique for optical coherence tomography (OCT), based on Generative Adversarial Networks (GANs), was developed and investigated. GANs have been previously used for resolution enhancement of photography and optical microscopy images. We have adapted and improved this technique for OCT image generation. Conditional GANs (cGANs) were trained on a novel set of ultrahigh resolution spectral domain OCT volumes, termed micro-OCT, as the high-resolution ground truth (∼1 μm isotropic resolution). The ground truth was paired with a low-resolution image obtained by synthetically degrading resolution 4x in one of (1-D) or both axial and lateral axes (2-D). Cross-sectional image (B-scan) volumes obtained from in vivo imaging of human labial (lip) tissue and mouse skin were used in separate feasibility experiments. Accuracy of resolution enhancement compared to ground truth was quantified with human perceptual accuracy tests performed by an OCT expert. The GAN loss in the optimization objective, noise injection in both the generator and discriminator models, and multi-scale discrimination were found to be important for achieving realistic speckle appearance in the generated OCT images. The utility of high-resolution speckle recovery was illustrated by an example of micro-OCT imaging of blood vessels in lip tissue. Qualitative examples applying the models to image data from outside of the training data distribution, namely human retina and mouse bladder, were also demonstrated, suggesting potential for cross-domain transferability. This preliminary study suggests that deep learning generative models trained on OCT images from high-performance prototype systems may have potential in enhancing lower resolution data from mainstream/commercial systems, thereby bringing cutting-edge technology to the masses at low cost.

中文翻译：

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通过微光学相干断层扫描的生成对抗模型实现分辨率增强和真实散斑恢复


开发并研究了一种基于生成对抗网络 (GAN) 的光学相干断层扫描 (OCT) 分辨率增强技术。 GAN 之前曾用于增强摄影和光学显微镜图像的分辨率。我们已经采用并改进了这种用于 OCT 图像生成的技术。条件 GAN (cGAN) 在一组新颖的超高分辨率谱域 OCT 体积（称为微 OCT）上进行训练，作为高分辨率地面实况（∼1 μm各向同性分辨率）。地面实况与通过在（一维）之一或两个轴和横轴（二维）上综合降低分辨率 4 倍而获得的低分辨率图像配对。从人类唇（唇）组织和小鼠皮肤的体内成像获得的横截面图像（B 扫描）体积用于单独的可行性实验。与地面实况相比，分辨率增强的准确性通过 OCT 专家进行的人类感知准确性测试进行了量化。研究发现，优化目标中的 GAN 损失、生成器和鉴别器模型中的噪声注入以及多尺度鉴别对于在生成的 OCT 图像中实现真实的散斑外观非常重要。通过唇组织血管的微 OCT 成像示例说明了高分辨率散斑恢复的实用性。还演示了将模型应用于来自训练数据分布之外的图像数据（即人类视网膜和小鼠膀胱）的定性示例，这表明了跨域可转移性的潜力。 这项初步研究表明，在高性能原型系统的 OCT 图像上训练的深度学习生成模型可能有潜力增强主流/商业系统的低分辨率数据，从而以低成本为大众带来尖端技术。