Compressive confocal microscopy imaging at the single-photon level with ultra-low sampling ratios

• Published in: Communications engineering Vol. 3; no. 1; pp. 88 - 9
• Main Authors: Liu, Shuai, Chen, Bin, Zou, Wenzhen, Sha, Hao, Feng, Xiaochen, Han, Sanyang, Li, Xiu, Yao, Xuri, Zhang, Jian, Zhang, Yongbing
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25.06.2024; Springer Nature B.V; Nature Portfolio
• Subjects: 631/1647/245/2225; 639/624/1107/510; 96/63; Algorithms; Coding; Deep learning; Digital imaging; Engineering; Fluorescence; Image reconstruction; Lasers; Light; Machine learning; Microscopy; Microspheres; Photons; Pinholes; Pixels; Power; Sampling; Sensors
• ISSN: 2731-3395; 2731-3395
• DOI: 10.1038/s44172-024-00236-x

Laser-scanning confocal microscopy serves as a critical instrument for microscopic research in biology. However, it suffers from low imaging speed and high phototoxicity. Here we build a novel deep compressive confocal microscope, which employs a digital micromirror device as a coding mask for single-pixel imaging and a pinhole for confocal microscopic imaging respectively. Combined with a deep learning reconstruction algorithm, our system is able to achieve high-quality confocal microscopic imaging with low phototoxicity. Our imaging experiments with fluorescent microspheres demonstrate its capability of achieving single-pixel confocal imaging with a sampling ratio of only approximately 0.03% in specific sparse scenarios. Moreover, the deep compressive confocal microscope allows single-pixel imaging at the single-photon level, thus reducing the excitation light power requirement for confocal imaging and suppressing the phototoxicity. We believe that our system has great potential for long-duration and high-speed microscopic imaging of living cells. Shuai Liu, Bin Chen and colleagues improve imaging speed and reduced phototoxicity in confocal microimaging by building a deep compressive confocal microscope. Digital micromirror acts as a coding mask for deep learning-based reconstruction algorithms.