Block face imaging is widely used in three-dimensional large biological samples imaging. However, high throughput and excellent optical sectioning cannot be achieved at the same time for imaging such large area. Here, we propose a line-scanning virtual structured modulation method to combine the advantages of optical sectioning and imaging speed in structured modulation microscopy and line-scanning microscopy. We significantly improve signal to noise ratio and throughput compared to wild-filed structured illumination microscopy. Our results also have no residual modulation artifacts. It indicates our method enables to achieve imaging 3D large biological sample with excellent optical sectioning and high throughput.
Obtaining fine structures in the whole brain is necessary for understanding brain function. Simple and effective methods for large-scale 3D imaging at optical resolution are still lacking. Here, we proposed a deep-learning-based fluorescence micro-optical sectioning tomography (DL-fMOST) method for fast, high-resolution whole-brain imaging. We utilized a wide-field microscope and a convolutional neural network for optical sectioning imaging, replacing traditional optical method. A 3D dataset of a mouse brain with a voxel size of 0.32 × 0.32 × 2 µm was acquired in 1.5 days. We demonstrated the robustness of DL-fMOST for mouse brains with labeling of different types of neurons.
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