We achieve these gains using an integrated, four-pronged approach: (1) developing compact line scanners that enable sensitive, rapid, diffraction-limited imaging over large areas (2) combining line-scanning with multiview imaging, developing reconstruction algorithms that improve resolution isotropy and recover signal otherwise lost to scattering (3) adapting techniques from structured illumination microscopy, achieving super-resolution imaging in densely labelled, thick samples (4) synergizing deep learning with these advances, further improving imaging speed, resolution and duration. Here we address these problems, enhancing confocal microscopy performance from the sub-micrometre to millimetre spatial scale and the millisecond to hour temporal scale, improving both lateral and axial resolution more than twofold while simultaneously reducing phototoxicity. Nature volume 600, pages 279–284 ( 2021) Cite this articleĬonfocal microscopy 1 remains a major workhorse in biomedical optical microscopy owing to its reliability and flexibility in imaging various samples, but suffers from substantial point spread function anisotropy, diffraction-limited resolution, depth-dependent degradation in scattering samples and volumetric bleaching 2. Multiview confocal super-resolution microscopy
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