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Light-sheet microscopy uses a thin plane of light to illuminate a sample orthogonally to the detection objective such that the axial resolution of the microscope is determined wholly or partly by the thickness of the light sheet. It allows fast volumetric imaging with reduced sample irradiation compared to conventional light microscopy methods.
Multi-sheet RESOLFT combines the speed and optical sectioning of light-sheet fluorescence microscopy with reversibly photoswitchable fluorescent proteins to enable fast, volumetric super-resolution imaging in live cells.
InfraRed-mediated Image Restoration (IR2) uses deep learning to combine the benefits of deep-tissue imaging with NIR probes and the convenience of imaging with GFP for improved time-lapse imaging of embryogenesis.
A combination of light-sheet fluorescence microscopy (LSFM) with structured illumination doubles resolving power over LSFM alone. We show a practical implementation using a single objective for illumination and fluorescence detection and demonstrate its use for rapid volumetric imaging.
We integrated the pre-characterized physical model of super-resolution (SR) microscopy into a deep neural network to guide the denoising of raw images for high-quality SR image reconstruction. This approach enabled us to investigate a wide variety of fragile and rapidly evolving bioprocesses at ultrahigh spatiotemporal resolution over extended imaging times.
A flexible open-top light-sheet microscope has been developed that can perform deep three-dimensional imaging on all clearing protocols with low and high optical resolution.
A new single-objective light-sheet microscope has been developed that uses novel optics and imaging protocols to increase resolution without compromising imaging speed and volume.