Peng Fei 1,2,*†Jun Nie 1Juhyun Lee 3,4Yichen Ding 3,5[ ... ]Tzung K. Hsiai 3,5,*
Author Affiliations
Abstract
1 Huazhong University of Science and Technology, School of Optical and Electronic Information, Wuhan, China
2 Huazhong University of Science and Technology, Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Wuhan, China
3 University of California, Los Angeles, Department of Bioengineering, Los Angeles, California, United States
4 University of Texas at Arlington, Joint Department of Bioengineering of UT Arlington/UT Southwestern, Arlington, Texas, United States
5 University of California, Los Angeles, School of Medicine, Los Angeles, California, United States
6 University of California, Los Angeles, Chemical and Biomolecular Engineering Department, Los Angeles, California, United States
7 Osaka Prefecture University, Nanoscience and Nanotechnology Research Center, Research Organization for the 21st Century, Osaka, Japan
8 University of California, Los Angeles, Mechanical and Aerospace Engineering Department, Los Angeles, California, United States
A key challenge when imaging whole biomedical specimens is how to quickly obtain massive cellular information over a large field of view (FOV). We report a subvoxel light-sheet microscopy (SLSM) method enabling high-throughput volumetric imaging of mesoscale specimens at cellular resolution. A nonaxial, continuous scanning strategy is developed to rapidly acquire a stack of large-FOV images with three-dimensional (3-D) nanoscale shifts encoded. Then, by adopting a subvoxel-resolving procedure, the SLSM method models these low-resolution, cross-correlated images in the spatial domain and can iteratively recover a 3-D image with improved resolution throughout the sample. This technique can surpass the optical limit of a conventional light-sheet microscope by more than three times, with high acquisition speeds of gigavoxels per minute. By fast reconstruction of 3-D cultured cells, intact organs, and live embryos, SLSM method presents a convenient way to circumvent the trade-off between mapping large-scale tissue (>100 mm3) and observing single cell (∼1-μm resolution). It also eliminates the need of complicated mechanical stitching or modulated illumination, using a simple light-sheet setup and fast graphics processing unit-based computation to achieve high-throughput, high-resolution 3-D microscopy, which could be tailored for a wide range of biomedical applications in pathology, histology, neuroscience, etc.
light-sheet microscopy subvoxel-resolving reconstruction large tissue imaging high-throughput volumetric imaging 
Advanced Photonics
2019, 1(1): 016002
Author Affiliations
Abstract
1 School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
2 Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
We present a three-dimensional (3D) isotropic imaging of mouse brain using light-sheet fluorescent microscopy (LSFM) in conjunction with a multi-view imaging computation. Unlike common single view LSFM is used for mouse brain imaging, the brain tissue is 3D imaged under eight views in our study, by a home-built selective plane illumination microscopy (SPIM). An output image containing complete structural information as well as significantly improved resolution (~4 times) are then computed based on these eight views of data, using a bead-guided multi-view registration and deconvolution. With superior imaging quality, the astrocyte and pyramidal neurons together with their subcellular nerve fibers can be clearly visualized and segmented. With further including other computational methods, this study can be potentially scaled up to map the connectome of whole mouse brain with a simple light-sheet microscope.
Light sheet fluorescent microscopy multi-view deconvolution mouse brain imaging isotropic. 
Journal of Innovative Optical Health Sciences
2017, 10(5): 1743006

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