3d Computational Cannula Fluorescence Microscopy Enabled By Artificial Neural Networks

Computational cannula microscopy (CCM) is a high-resolution widefield fluorescence imaging approach deep inside tissue, which is minimally invasive. Rather than using conventional lenses, a surgical cannula acts as a lightpipe for both excitation and fluorescence emission, where computational methods are used for image visualization. Here, we enhance CCM with artificial neural networks to enable 3D imaging of cultured neurons and fluorescent beads, the latter inside a volumetric phantom. We experimentally demonstrate transverse resolution of similar to 6 mu m, field of view similar to 200 mu m and axial sectioning of similar to 50 mu m for depths down to similar to 700 mu m, all achieved with computation time of similar to 3ms/frame on a desktop computer. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement