An ultrathin fiber-based fluorescent imaging probe based on hyperspectral imaging

被引:2
|
作者
Zhang, Shuyan [1 ]
Chua, Jian Jun [1 ,2 ]
Tang, Wilbert Yao Hwee [1 ,2 ]
Cheng, Joseph Yong Xin [1 ,2 ]
Li, Xiuting [1 ]
Olivo, Malini [1 ]
机构
[1] ASTAR, Inst Bioengn & Bioimaging, Translat Biophoton Lab, Singapore, Singapore
[2] Natl Univ Singapore, Dept Biomed Engn, Singapore, Singapore
来源
FRONTIERS IN PHYSICS | 2023年 / 10卷
关键词
fiber sensor; hyperspectral imaging; fluorescent imaging; spectral unmixing; bioimaging; quantum dots; bacteria; spectral angle mapper; SYSTEM;
D O I
10.3389/fphy.2022.1096290
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Most fluorescent imaging systems use pre-defined filter sets with limited flexibility in the choice of excitation and emission bands and suffer from crosstalk between different fluorophores for multiplexed imaging. When implemented in the context of optical fibers for biological imaging in a remote setting with space constraints, the size of the fiber probe is crucial. To overcome these challenges, we combined the merits of hyperspectral imaging and an ultrathin optical imaging fiber where fluorescent images at 311 spectral bands in the visible range were captured using a liquid crystal tunable filter with a fiber probe of 500 mu m in diameter. Fluorescent experiments were performed on quantum dots mixtures and genetically modified E. coli bacteria mixtures to demonstrate the system's capabilities for multiplexed imaging. Images were first processed using a Fourier transform filtering technique to remove the fiber core pattern artifact. Subsequently, the classification of different types of bacteria samples was calculated using two methods, namely, spectral unmixing with maximum abundance and spectral matching with minimum spectral angle distance. Finally, the spatial distributions of individual bacteria types were overlaid with the mixture image, and the two classification results matched well. A simple-to-use graphic user interface (GUI) platform for the hyperspectral imaging fiber probe system was developed which performs image processing and displays the classification results. The methods and results presented will appeal to the optical fiber-based imaging and hyperspectral imaging communities in general and have great potential for biological imaging applications.
引用
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页数:9
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