Exceeding the limits of 3D fluorescence microscopy using a dual-stage-processing network

Although three-dimensional (3D) fluorescence microscopy is an essential tool for life science research, the fundamentally limited optical throughput, as reflected in the compromise between speed and resolution, so far prevents further movement towards faster, clearer, and higher-throughput applications. We herein report a dual-stage mutual-feedback deep-learning approach that allows gradual reversion of microscopy degradation from high-resolution targets to low-resolution images. Using a single blurred-and-pixelated 3D image as input, our trained network infers a 3D output with notably higher resolution and improved contrast. The performance is better than conventional one-stage network approaches. It pushes the throughput limit of current 3D fluorescence microscopy in three ways: notably reducing the acquisition time for accurate mapping of large organs, breaking the diffraction limit for imaging subcellular events with faster lower-toxicity measurement, and improving temporal resolution for capturing instantaneous biological processes. Combining our network approach with light-sheet fluorescence microscopy, we demonstrate the imaging of vessels and neurons in the mouse brain at single-cell resolution and with a throughput of 6 min for a whole brain. We also image cell organelles beyond the diffraction limit at a 2 Hz volume rate and map neuronal activities of freely moving C. elegans at single-cell resolution and 30 Hz volume rate.