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Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning

Light-field microscopy has emerged as a technique of choice for high-speed volumetric imaging of fast biological processes. However, artefacts, non-uniform resolution, and a slow reconstruction speed have limited its full capabilities for in toto extraction of the dynamic spatiotemporal patterns in...

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Detalles Bibliográficos
Autores principales: Wang, Zhaoqiang, Zhu, Lanxin, Zhang, Hao, Li, Guo, Yi, Chengqiang, Li, Yi, Yang, Yicong, Ding, Yichen, Zhen, Mei, Gao, Shangbang, Hsiai, Tzung K., Fei, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8107123/
https://www.ncbi.nlm.nih.gov/pubmed/33574612
http://dx.doi.org/10.1038/s41592-021-01058-x
Descripción
Sumario:Light-field microscopy has emerged as a technique of choice for high-speed volumetric imaging of fast biological processes. However, artefacts, non-uniform resolution, and a slow reconstruction speed have limited its full capabilities for in toto extraction of the dynamic spatiotemporal patterns in samples. Here, we combined a view-channel-depth (VCD) neural network with light-field microscopy to mitigate these limitations, yielding artefact-free three-dimensional image sequences with uniform spatial resolution and high video-rate reconstruction throughput. We imaged neuronal activities across moving C. elegans and blood flow in a beating zebrafish heart at single-cell resolution with volumetric imaging rates up to 200 Hz.