Virtual-scanning light-field microscopy for robust snapshot high-resolution volumetric imaging

Abstract

High-speed three-dimensional (3D) intravital imaging in animals is useful for studying transient subcellular interactions and functions in health and disease. Light-field microscopy (LFM) provides a computational solution for snapshot 3D imaging with low phototoxicity but is restricted by low resolution and reconstruction artifacts induced by optical aberrations, motion and noise. Here, we propose virtual-scanning LFM (VsLFM), a physics-based deep learning framework to increase the resolution of LFM up to the diffraction limit within a snapshot. By constructing a 40 GB high-resolution scanning LFM dataset across different species, we exploit physical priors between phase-correlated angular views to address the frequency aliasing problem. This enables us to bypass hardware scanning and associated motion artifacts. Here, we show that VsLFM achieves ultrafast 3D imaging of diverse processes such as the beating heart in embryonic zebrafish, voltage activity in Drosophila brains and neutrophil migration in the mouse liver at up to 500 volumes per second.