3D Computational Cannula Fluorescence Microscopy enabled by Artificial Neural Networks

TL;DR

This paper introduces an advanced 3D fluorescence microscopy technique using a surgical cannula combined with artificial neural networks, enabling rapid, high-resolution imaging deep inside tissue with minimal invasiveness.

Contribution

It presents a novel integration of neural networks with computational cannula microscopy for real-time 3D imaging of biological samples.

Findings

Transverse resolution of ~6um achieved

Field of view ~200um demonstrated

Axial sectioning of ~50um at depths ~700um

Abstract

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 ~6um, field of view ~200um and axial sectioning of ~50um for depths down to ~700um, all achieved with computation time of ~3ms/frame on a laptop computer.