Opt Express. 2022 May 23;30(11):18919-18938. doi: 10.1364/OE.454504.
Optical coherence tomography (OCT), a promising noninvasive bioimaging technique, can resolve sample three-dimensional microstructures. However, speckle noise imposes obvious limitations on OCT resolving capabilities. Here we proposed a deep-learning-based speckle-modulating OCT based on a hybrid-structure network, residual-dense-block U-Net generative adversarial network (RDBU-Net GAN), and further conducted a comprehensively comparative study to explore multi-type deep-learning architectures' abilities to extract speckle pattern characteristics and remove speckle, and resolve microstructures. This is the first time that network comparative study has been performed on a customized dataset containing mass more-general speckle patterns obtained from a custom-built speckle-modulating OCT, but not on retinal OCT datasets with limited speckle patterns. Results demonstrated that the proposed RDBU-Net GAN has a more excellent ability to extract speckle pattern characteristics and remove speckle, and resolve microstructures. This work will be useful for future studies on OCT speckle removing and deep-learning-based speckle-modulating OCT.
光学相干断层扫描（OCT）是一种很有前途的非侵入式生物成像技术，可以解析样本的三维微观结构。然而，散斑噪声对 OCT 的解析能力有明显的限制。在这里，我们提出了一种基于混合结构网络、残差密集块 U-Net 生成对抗网络（RDBU-Net GAN）的基于深度学习的散斑调制 OCT，并进一步进行了全面的比较研究，以探索多种类型的深度学习架构提取散斑模式特征和去除散斑以及解析微观结构的能力。这是首次在一个定制的数据集上进行网络比较研究，该数据集包含了从定制的散斑调制 OCT 获得的更多一般散斑模式，而不是在视网膜 OCT 数据集上进行的，视网膜 OCT 数据集的散斑模式有限。结果表明，所提出的 RDBU-Net GAN 具有更好的提取散斑模式特征和去除散斑以及解析微观结构的能力。这项工作将有助于未来 OCT 散斑去除和基于深度学习的散斑调制 OCT 的研究。
