Light-field microscopy has emerged as a technique of choice for high-speed volumetric imaging of fast biological processes. However, artefacts, non-uniform resolution, and a slow reconstruction speed have limited its full capabilities for
in toto
extraction of the dynamic spatiotemporal patterns in samples. Here, we combined a view-channel-depth (VCD) neural network with light-field microscopy to mitigate these limitations, yielding artefact-free three-dimensional image sequences with uniform spatial resolution and high video-rate reconstruction throughput. We imaged neuronal activities across moving
C. elegans
and blood flow in a beating zebrafish heart at single-cell resolution with volumetric imaging rates up to 200 Hz.
Isotropic 3D histological imaging of large biological specimens is highly desired but remains highly challenging to current fluorescence microscopy technique. Here we present a new method, termed deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM), to enable fast, isotropic light-sheet fluorescence imaging on a conventional wide-field microscope. After integrating a minimized add-on device that transforms an inverted microscope into a 3D light-sheet microscope, we further integrate a deep neural network (DNN) procedure to quickly restore the ambiguous z-reconstructed planes that suffer from still insufficient axial resolution of light-sheet illumination, thereby achieving isotropic 3D imaging of thick biological specimens at single-cell resolution. We apply this easy and cost-effective Deep-SLAM approach to the anatomical imaging of single neurons in a meso-scale mouse brain, demonstrating its potential for readily converting commonly-used commercialized 2D microscopes to high-throughput 3D imaging, which is previously exclusive for high-end microscopy implementations.
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