Opt Lett. 2020 Apr 1;45(7):2111-2114. doi: 10.1364/OL.387496.
Computational cannula microscopy is a minimally invasive imaging technique that can enable high-resolution imaging deep inside tissue. Here, we apply artificial neural networks to enable real-time, power-efficient image reconstructions that are more efficiently scalable to larger fields of view. Specifically, we demonstrate widefield fluorescence microscopy of cultured neurons and fluorescent beads with a field of view of 200 µm (diameter) and a resolution of less than 10 µm using a cannula of diameter of only 220 µm. In addition, we show that this approach can also be extended to macro-photography.
计算式管显微镜是一种微创成像技术，可以实现组织深处的高分辨率成像。在这里，我们应用人工神经网络实现实时、节能的图像重建，这些重建方法在更大的视场中更具可扩展性。具体来说，我们使用直径仅为 220 µm 的管，实现了 200 µm（直径）视场和小于 10 µm 分辨率的培养神经元和荧光珠的广角荧光显微镜。此外，我们还表明，这种方法也可以扩展到宏观摄影。
