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 blurred and light-scattered 3D image of deep tissue. 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 (LSFM), we demonstrate 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-to-noise ratio (SNR) and resolution of neurons in mouse brain beyond the photon ballistic region.

中文翻译：

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用于混浊生物标本的 3D 显微术的深度学习有效图像恢复

尽管三维 (3D) 荧光显微镜已成为现代生命科学研究的重要工具，但生物标本的光散射从根本上阻止了其在实时成像中的更广泛应用。我们在此报告了一种称为 ScatNet 的深度学习方法，该方法可以将 3D 荧光显微镜从高分辨率目标还原为低质量的光散射测量，从而可以恢复深部组织的模糊和光散射 3D 图像。我们的方法可以通过计算扩展当前 3D 荧光显微镜的成像深度，而无需添加复杂的光学元件。将 ScatNet 方法与尖端的光片荧光显微镜 (LSFM) 相结合，我们展示了活果蝇深层细胞核的图像恢复 单细胞分辨率的黑腹胚胎。将我们的方法应用于双光子激发显微镜，我们可以提高光子弹道区域以外的小鼠大脑神经元的信噪比 (SNR) 和分辨率。