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 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.