Speckle imposes obvious limitations on resolving capabilities of optical coherence tomography (OCT), while speckle-modulating OCT can efficiently reduce speckle arbitrarily. However, speckle-modulating OCT seriously reduces the imaging sensitivity and temporal resolution of the OCT system when reducing speckle. Here, we proposed a deep-learning-based speckle-modulating OCT, termed Sm-Net OCT, by deeply integrating conventional OCT setup and generative adversarial network trained with a customized large speckle-modulating OCT dataset containing massive speckle patterns. The customized large speckle-modulating OCT dataset was obtained from the aforementioned conventional OCT setup rebuilt into a speckle-modulating OCT and performed imaging using different scanning parameters. Experimental results demonstrated that the proposed Sm-Net OCT can effectively obtain high-quality OCT images without the electronic noise and speckle, and conquer the limitations of reducing the imaging sensitivity and temporal resolution which conventional speckle-modulating OCT has. The proposed Sm-Net OCT can significantly improve the adaptability and practicality capabilities of OCT imaging, and expand its application fields.

中文翻译：

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Sm-Net OCT：基于深度学习的散斑调制光学相干断层扫描

散斑对光学相干断层扫描（OCT）的分辨能力有明显的限制，而散斑调制的 OCT 可以有效地任意减少散斑。然而，散斑调制OCT在减少散斑时严重降低了OCT系统的成像灵敏度和时间分辨率。在这里，我们提出了一种基于深度学习的散斑调制 OCT，称为 Sm-Net OCT，通过深度集成传统 OCT 设置和生成对抗网络，该网络使用包含大量散斑图案的定制大型散斑调制 OCT 数据集训练。定制的大型散斑调制 OCT 数据集是从上述传统 OCT 设置中获得的，重建为散斑调制 OCT，并使用不同的扫描参数进行成像。实验结果表明，所提出的 Sm-Net OCT 可以有效地获得没有电子噪声和散斑的高质量 OCT 图像，并克服了传统散斑调制 OCT 降低成像灵敏度和时间分辨率的局限性。所提出的 Sm-Net OCT 可以显着提高 OCT 成像的适应性和实用性能力，并扩展其应用领域。