Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning

Light-field microscopy has emerged as a technique of choice for high-speed volumetric imaging of fast biological processes. However, artifacts, nonuniform resolution and a slow reconstruction speed have limited its full capabilities for in toto extraction of dynamic spatiotemporal patterns in samples. Here, we combined a view-channel-depth (VCD) neural network with light-field microscopy to mitigate these limitations, yielding artifact-free three-dimensional image sequences with uniform spatial resolution and high-video-rate reconstruction throughput. We imaged neuronal activities across moving Caenorhabditis elegans and blood flow in a beating zebrafish heart at single-cell resolution with volumetric imaging rates up to 200 Hz. Reconstruction of light-field microscopy data with a deep-learning network achieves high reconstruction speed and reduces artifacts, as illustrated for moving C. elegans and beating zebrafish hearts.