The microscopic visualization of large-scale three-dimensional (3D) samples by optical microscopy requires overcoming challenges in imaging quality and speed and in big data acquisition and management. We report a line-illumination modulation (LiMo) technique for imaging thick tissues with high throughput and low background. Combining LiMo with thin tissue sectioning, we further develop a high-definition fluorescent micro-optical sectioning tomography (HD-fMOST) method that features an average signal-to-noise ratio of 110, leading to substantial improvement in neuronal morphology reconstruction. We achieve a >30-fold lossless data compression at a voxel resolution of 0.32 × 0.32 × 1.00 μm³, enabling online data storage to a USB drive or in the cloud, and high-precision (95% accuracy) brain-wide 3D cell counting in real time. These results highlight the potential of HD-fMOST to facilitate large-scale acquisition and analysis of whole-brain high-resolution datasets.

通过光学显微镜对大型三维 (3D) 样品进行显微可视化需要克服成像质量和速度以及大数据采集和管理方面的挑战。我们报告了一种线照明调制 (LiMo) 技术，用于以高通量和低背景对厚组织进行成像。将 LiMo 与薄组织切片相结合，我们进一步开发了一种高清荧光显微光学切片断层扫描 (HD-fMOST) 方法，该方法的平均信噪比为 110，从而显着改善了神经元形态重建。^{我们以 0.32 × 0.32 × 1.00 μm 3}的体素分辨率实现了 >30 倍的无损数据压缩，实现在线数据存储到 USB 驱动器或云端，以及实时高精度（95% 准确度）全脑 3D 细胞计数。这些结果突出了 HD-fMOST 在促进全脑高分辨率数据集的大规模采集和分析方面的潜力。