Widefield light sheet microscopy using an Airy beam combined with deep-learning super-resolution

Abstract

Imaging across length scales and in depth has been an important pursuit of widefield optical imaging, promising to reveal fine cellular detail within a widefield snapshot of a tissue sample. Current advances often sacrifice resolution through selective sub-sampling to provide a wide field of view in a reasonable time scale. We demonstrate a new avenue for recovering high-resolution images from sub-sampled data in light-sheet microscopy using deep-learning super-resolution. We combine this with the use of a widefield Airy beam to achieve high-resolution imaging over extended fields of view and depths. We characterise our method on fluorescent beads as test targets, and demonstrate improvements in imaging amyloid plaques in a cleared brain from a mouse model of Alzheimer's disease, and in excised healthy and cancerous colon and breast tissues. This development can be widely applied in all forms of light sheet microscopy to provide a two-fold increase in the dynamic range of the imaged length scale. It has the potential to provide further insight into neuroscience, developmental biology and histopathology.