Quantitative phase imaging (QPI) through multi-core fibers (MCFs) has been an emerging in vivo label-free endoscopic imaging modality with minimal invasiveness. However, the computational demands of conventional iterative phase retrieval algorithms have limited their real-time imaging potential. We demonstrate a learning-based MCF phase imaging method, that significantly reduced the phase reconstruction time to 5.5 ms, enabling video-rate imaging at 181 fps. Moreover, we introduce an innovative optical system that automatically generated the first open-source dataset tailored for MCF phase imaging, comprising 50,176 paired speckle and phase images. Our trained deep neural network (DNN) demonstrates robust phase reconstruction performance in experiments with a mean fidelity of up to 99.8\%. Such an efficient fiber phase imaging approach can broaden the applications of QPI in hard-to-reach areas.

翻译：通过多芯光纤实现定量相位成像（QPI）已成为一种新兴的、具有微创性的活体无标记内窥镜成像模式。然而，传统迭代相位恢复算法的计算需求限制了其实时成像潜力。我们提出了一种基于学习的多芯光纤相位成像方法，将相位重建时间显著缩短至5.5毫秒，实现了181帧/秒的视频级成像速率。此外，我们引入了一种创新型光学系统，该系统自动生成了首个专为多芯光纤相位成像设计的开源数据集，包含50,176对散斑和相位图像。经过训练的深度神经网络在实验中展现出稳健的相位重建性能，平均保真度高达99.8%。这种高效的光纤相位成像方法可拓展QPI在难以触及区域的应用范围。