Significance: Reducing the bit depth is an effective approach to lower the cost of an optical coherence tomography (OCT) imaging device and increase the transmission efficiency in data acquisition and telemedicine. However, a low bit depth will lead to the degradation of the detection sensitivity, thus reducing the signal-to-noise ratio (SNR) of OCT images. Aim: We propose using deep learning to reconstruct high SNR OCT images from low bit-depth acquisition. Approach: The feasibility of our approach is evaluated by applying this approach to the quantized 3- to 8-bit data from native 12-bit interference fringes. We employ a pixel-to-pixel generative adversarial network (pix2pixGAN) architecture in the low-to-high bit-depth OCT image transition. Results: Extensively, qualitative and quantitative results show our method could significantly improve the SNR of the low bit-depth OCT images. The adopted pix2pixGAN is superior to other possible deep learning and compressed sensing solutions. Conclusions: Our work demonstrates that the proper integration of OCT and deep learning could benefit the development of healthcare in low-resource settings.

中文翻译：

---

使用深度学习的低位深光学相干断层扫描的高信噪比重建

意义：降低位深是降低光学相干断层扫描（OCT）成像设备成本，提高数据采集和远程医疗传输效率的有效途径。然而，低位深度会导致检测灵敏度下降，从而降低 OCT 图像的信噪比 (SNR)。目的：我们建议使用深度学习从低位深度采集重建高 SNR OCT 图像。方法：通过将这种方法应用于来自原生 12 位干涉条纹的量化 3 到 8 位数据来评估我们方法的可行性。我们在从低到高位深度的 OCT 图像转换中采用了像素到像素的生成对抗网络 (pix2pixGAN) 架构。结果：广泛地，定性和定量结果表明，我们的方法可以显着提高低位深 OCT 图像的 SNR。采用的 pix2pixGAN 优于其他可能的深度学习和压缩感知解决方案。结论：我们的工作表明，OCT 和深度学习的适当结合可以有利于资源匮乏地区的医疗保健发展。