Deep learning-enhanced snapshot hyperspectral confocal microscopy imaging system

Author:

Liu Shuai1,Zou Wenzhen,Sha Hao,Feng Xiaochen,Chen Bin2ORCID,Zhang Jian2,Han Sanyang1,Li Xiu1ORCID,Zhang Yongbing

Affiliation:

1. Tsinghua University

2. Peking University Shenzhen Graduate School

Abstract

Laser-scanning confocal hyperspectral microscopy is a powerful technique to identify the different sample constituents and their spatial distribution in three-dimensional (3D). However, it suffers from low imaging speed because of the mechanical scanning methods. To overcome this challenge, we propose a snapshot hyperspectral confocal microscopy imaging system (SHCMS). It combined coded illumination microscopy based on a digital micromirror device (DMD) with a snapshot hyperspectral confocal neural network (SHCNet) to realize single-shot confocal hyperspectral imaging. With SHCMS, high-contrast 160-bands confocal hyperspectral images of potato tuber autofluorescence can be collected by only single-shot, which is almost 5 times improvement in the number of spectral channels than previously reported methods. Moreover, our approach can efficiently record hyperspectral volumetric imaging due to the optical sectioning capability. This fast high-resolution hyperspectral imaging method may pave the way for real-time highly multiplexed biological imaging.

Funder

Shenzhen Science and Technology research and development funds

Fundamental Research Funds for the Central Universities

Shenzhen Science and Technology Project

National Natural Science Foundation of China

Publisher

Optica Publishing Group

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