Network-enabled efficient image restoration for 3D microscopy of turbid biological specimens

Abstract

Though three-dimensional (3D) fluorescence microscopy has been an essential tool for modern life science research, the light scattering by biological specimens fundamentally prevents its more widespread applications in live imaging. We hereby report a deep-learning approach, termed ScatNet, that enables reversion of 3D fluorescence microscopy from high-resolution targets to low-quality, light-scattered measurements, thereby allowing restoration for a single blurred and light-scattered 3D image of deep tissue, with achieving improved resolution and signal-to-noise ratio. Our approach can computationally extend the imaging depth for current 3D fluorescence microscopes, without the addition of complicated optics. Combining ScatNet approach with cutting-edge light-sheet fluorescence microscopy, we demonstrate that the image restoration of cell nuclei in the deep layer of live Drosophila melanogaster embryos at single-cell resolution. Applying our approach to two-photon excitation microscopy, we could improve the signal and resolution of neurons in mouse brain beyond the photon ballistic region.