Open AccessReal-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning
Author(s) -
Zhaoqiang Wang,
Lanxin Zhu,
Hao Zhang,
Guo Li,
Chengqiang Yi,
Yang Li,
Yucheng Yang,
Yichen Ding,
Mei Zhen,
Shangbang Gao,
Tzung K. Hsiai,
Fei Peng
Publication year - 2021
Publication title -
nature methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 19.469
H-Index - 318
eISSN - 1548-7105
pISSN - 1548-7091
DOI - 10.1038/s41592-021-01058-x
Subject(s) - microscopy , light sheet fluorescence microscopy , artificial intelligence , computer vision , light field , computer science , temporal resolution , artifact (error) , zebrafish , iterative reconstruction , video microscopy , image resolution , biological system , optics , biology , physics , scanning confocal electron microscopy , biochemistry , gene , microbiology and biotechnology
Light-field microscopy has emerged as a technique of choice for high-speed volumetric imaging of fast biological processes. However, artifacts, nonuniform resolution and a slow reconstruction speed have limited its full capabilities for in toto extraction of dynamic spatiotemporal patterns in samples. Here, we combined a view-channel-depth (VCD) neural network with light-field microscopy to mitigate these limitations, yielding artifact-free three-dimensional image sequences with uniform spatial resolution and high-video-rate reconstruction throughput. We imaged neuronal activities across moving Caenorhabditis elegans and blood flow in a beating zebrafish heart at single-cell resolution with volumetric imaging rates up to 200 Hz.