Computational cannula microscopy of neurons using neural networks.

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 mu m (diameter) and a resolution of less than 10 mu m using a cannula of diameter of only 220 mu m. In addition, we show that this approach can also be extended to macro-photography. (C) 2020 Optical Society of America