Optical coherence tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning-based image reconstruction framework that can generate swept-source OCT (SS-OCT) images using undersampled spectral data, without any spatial aliasing artifacts. This neural network-based image reconstruction does not require any hardware changes to the optical setup and can be easily integrated with existing swept-source or spectral-domain OCT systems to reduce the amount of raw spectral data to be acquired. To show the efficacy of this framework, we trained and blindly tested a deep neural network using mouse embryo samples imaged by an SS-OCT system. Using 2-fold undersampled spectral data (i.e., 640 spectral points per A-line), the trained neural network can blindly reconstruct 512 A-lines in 0.59 ms using multiple graphics-processing units (GPUs), removing spatial aliasing artifacts due to spectral undersampling, also presenting a very good match to the images of the same samples, reconstructed using the full spectral OCT data (i.e., 1280 spectral points per A-line). We also successfully demonstrate that this framework can be further extended to process 3× undersampled spectral data per A-line, with some performance degradation in the reconstructed image quality compared to 2× spectral undersampling. Furthermore, an A-line-optimized undersampling method is presented by jointly optimizing the spectral sampling locations and the corresponding image reconstruction network, which improved the overall imaging performance using less spectral data points per A-line compared to 2× or 3× spectral undersampling results. This deep learning-enabled image reconstruction approach can be broadly used in various forms of spectral-domain OCT systems, helping to increase their imaging speed without sacrificing image resolution and signal-to-noise ratio.

光学相干断层扫描（OCT）是一种广泛使用的非侵入性生物医学成像方式，可以快速提供样本的体积图像。在这里，我们提出了一种基于深度学习的图像重建框架，可以使用欠采样光谱数据生成扫频 OCT (SS-OCT) 图像，而不会产生任何空间混叠伪影。这种基于神经网络的图像重建不需要对光学设置进行任何硬件更改，并且可以轻松与现有的扫频或谱域 OCT 系统集成，以减少要采集的原始光谱数据量。为了展示该框架的有效性，我们使用 SS-OCT 系统成像的小鼠胚胎样本来训练和盲目测试深度神经网络。使用 2 倍欠采样光谱数据（即每条 A 线 640 个光谱点），经过训练的神经网络可以使用多个图形处理单元 (GPU) 在 0.59 毫秒内盲重建 512 条 A 线，从而消除由于光谱造成的空间混叠伪影。欠采样，也与使用全光谱 OCT 数据（即每 A 线 1280 个光谱点）重建的相同样本的图像非常匹配。我们还成功证明了该框架可以进一步扩展到处理每条 A 线 3× 欠采样光谱数据，与 2× 光谱欠采样相比，重建图像质量的性能有所下降。此外，通过联合优化光谱采样位置和相应的图像重建网络，提出了一种A线优化欠采样方法，与2×或3×光谱欠采样相比，每条A线使用更少的光谱数据点，提高了整体成像性能结果。 这种支持深度学习的图像重建方法可广泛应用于各种形式的谱域OCT系统，有助于在不牺牲图像分辨率和信噪比的情况下提高成像速度。