DeepInMiniscope: Deep learning-powered physics-informed integrated miniscope.

Abstract

Mask-based integrated fluorescence microscopy is a compact imaging technique for biomedical research. It can perform snapshot 3D imaging through a thin optical mask with a scalable field of view (FOV). Integrated microscopy uses computational algorithms for object reconstruction, but efficient reconstruction algorithms for large-scale data have been lacking. Here, we developed DeepInMiniscope, a miniaturized integrated microscope featuring a custom-designed optical mask and an efficient physics-informed deep learning model that markedly reduces computational demand. Parts of the 3D object can be individually reconstructed and combined. Our deep learning algorithm can reconstruct object volumes over 4 millimeters by 6 millimeters by 0.6 millimeters. We demonstrated substantial improvement in both reconstruction quality and speed compared to traditional methods for large-scale data. Notably, we imaged neuronal activity with near-cellular resolution in awake mouse cortex, representing a substantial leap over existing integrated microscopes. DeepInMiniscope holds great promise for scalable, large-FOV, high-speed, 3D imaging applications with compact device footprint.