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Fluorescence imaging technology can dynamically monitor gene and cell changing in live animals in real-time, with advantages such as high sensitivity, high resolution, and non-invasion. In recent years, it has been widely used in tumor research, gene expression research, drug development research, etc. The imaging wavelength of traditional fluorescence imaging technology falls in the visible and near-infrared-I region. Due to the absorption and scattering effects of light propagation in biological tissues, and the inherent fluorescence of biological tissues, traditional fluorescence imaging techniques still have significant limitations in penetration depth and image signal-to-noise ratio. In this work, a highly integrated near-infrared-II (NIR-II, 900—1880 nm) small animal living fluorescence imaging system is developed by taking the advantages of NIR-II fluorescence imaging technology, such as low absorption, low scattering, and deep penetration depth in biological tissues. And a method of enhancing and correcting fluorescence image is proposed to optimize fluorescence images. In this work, the biological tissue simulation experiments and live animal experiments are conducted to test the performance and imaging effect of the system. The experimental results show that the system has the advantages of deep penetration depth, high signal-to-noise ratio, and high sensitivity. Combined with commercial indocyanine green reagents and aggregation-induced emission dyes, this system can monitor the distribution of blood vessels in real time and continuously monitor deep tissues and organs in mice, and conduct the dynamically monitoring research in living mice in a conscious state. This helps to promote tumor research and drug development research in the field of biomedical imaging to enter a new stage.
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图 1 荧光成像示意图
Figure 1. Schematic diagram of fluorescence imaging.
图 2 生物组织的吸收和散射作用
Figure 2. Absorption and scattering of biological tissues.
图 3 NIR-II荧光成像系统 (a) 系统结构示意图; (b) 系统三维效果图
Figure 3. NIR-II fluorescence imaging system: (a) Schematic diagram of system; (b) 3D design of system.
图 4 荧光图像处理 (a) 流程图; (b) 原始低荧光图像; (c) 原始高荧光图像; (d) 图(b)的增强图像; (e) 图(c)的增强图像
Figure 4. Fluorescence image processing: (a) Flow chart; (b) original low fluorescence image; (c) original high fluorescence image; (d) enhanced image of Fig. (b); (e) enhanced image of Fig. (c).
图 5 系统的穿透深度测试 (a) 示意图; (b) 实物图; (c) 伪彩图像
Figure 5. Penetration depth testing of the system: (a) Schematic diagram; (b) physical diagram; (c) pseudo color image.
图 6 ICG梯度实验 (a) 12组的荧光信号强度分析; (b) 最后4组的荧光信号强度分析
Figure 6. ICG gradient experiment: (a) Analysis of fluorescence signal intensity in 12 groups; (b) analysis of fluorescence signal intensity for the last 4 groups.
图 7 脱毛小鼠
Figure 7. Depilatory mouse.
图 8 荧光成像 (a) 原始荧光图像; (b) 增强荧光图像; (c) ROI选取示意图; (d) 图(a)的ROI信背比分析; (e) 图(b)的ROI信背比分析
Figure 8. Fluorescence imaging: (a) Original fluorescence image; (b) enhanced fluorescence image; (c) schematic diagram of ROI selection; (d) ROI analysis of signal-to-back ratio in Fig. (a); (e) ROI analysis of signal-to-back ratio in Fig. (b).
图 9 组织器官荧光成像持续监测结果
Figure 9. Continuous monitoring results of tissue and organ fluorescence imaging.
图 10 活体动物动态监测结果 (a) 原始荧光图像; (b) 伪彩增强图像
Figure 10. Dynamic monitoring results of living animals: (a) Original fluorescence image; (b) pseudo color enhanced image.
表 1 图像量化评价指标对比
Table 1. Comparison of image quantitative evaluation indicators.
评价指标 清晰度 标准差 原始低/高荧光图像 352.7 517.4 2399.9 10294.3 处理后低/高荧光图像 2520.6 723.0 8440.6 17042.3 
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[1] Hong G S, Antaris A L, Dai H J 2017 Nat. Biomed. Eng. 1 0010
Google Scholar
[2] Li C Y, Wang Q B 2018 ACS Nano 12 9654
Google Scholar
[3] Cao J, Zhu B L, Zheng K F, He S G, Meng L, Song J B, Yang H H 2020 Front. Bioeng. Biotech. 7 487
Google Scholar
[4] Miao Q Q, Pu K Y 2018 Adv. Mater. 30 1801778
Google Scholar
[5] Welsher K, Liu Z, Sherlock S P, Robinson J T, Chen Z, Daranciang D, Dai H J 2009 Nat. Nanotechnol. 4 773
Google Scholar
[6] Smith A M, Mancini M C, Nie S M 2009 Nat. Nanotechnol. 4 710
Google Scholar
[7] Chang B S, Li D F, Ren Y, Qu C R, Shi X J, Liu R Q, Liu H G, Tian J, Hu Z H, Sun T L, Cheng Z 2022 Nat. Biomed. Eng. 6 629
Google Scholar
[8] Wang T, Wang S F, Liu Z Y, He Z Y, Yu P, Zhao M Y, Zhang H X, Lu L F, Wang Z X, Wang Z Y, Zhang W A, Fan Y, Sun C X, Zhao D Y, Liu W M, Bunzli J C G, Zhang F 2021 Nat. Mater. 20 1571
Google Scholar
[9] Diao S, Hong G S, Antaris A L, Blackburn J L, Cheng K, Cheng Z, Dai H J 2015 Nano Res 8 3027
Google Scholar
[10] Zhang X N, Li S S, Ma H Z, Wang H, Zhang R P, Zhang X D 2022 Theranostics 12 3345
Google Scholar
[11] Tian R, Ma H L, Zhu S J, Lau J, Ma R, Liu Y J, Lin L S, Chandra S, Wang S, Zhu X F, Deng H Z, Niu G, Zhang M X, Antaris A L, Hettie K S, Yang B, Liang Y Y, Chen X Y 2020 Adv. Mater. 32 1907365
Google Scholar
[12] Alifu N, Ma R, Zhu L J, Du Z, Chen S, Yan T, Alimu G, Zhang L X, Zhang X L 2022 J. Mater. Chem. B 10 506
Google Scholar
[13] Soref R A 1993 P. IEEE 81 1687
Google Scholar
[14] Youngblood N, Chen C, Koester S J, Li M 2015 Nat. Photonics 9 247
Google Scholar
[15] Hu F, Dai X Y, Zhou Z Q, Kong X Y, Sun S L, Zhang R J, Wang S Y, Lu M, Sun J 2019 Opt. Express 27 3161
Google Scholar
[16] Yang Y, Zhao J H, Li C, Chen Q D, Chen Z G, Sun H B 2021 Opt. Lett. 46 3300
Google Scholar
[17] Vallone M, Goano M, Bertazzi F, Ghione G, Schirmacher W, Hanna S, Figgemeier H 2016 J. Electron. Mater. 45 4524
Google Scholar
[18] Martyniuk P, Rogalski A 2013 B. Pol. Acad. Sci-Tech. 61 211
Google Scholar
[19] Li X, Gong H M, Fang J X, Shao X M, Tang H J, Huang S L, Li T, Huang Z C 2017 Infrared Phys. Techn. 80 112
Google Scholar
[20] Singh A, Pal R 2017 Appl. Phys. A 123 701
Google Scholar
[21] Feng Z, Tang T, Wu T X, Yu X M, Zhang Y H, Wang M, Zheng J Y, Ying Y Y, Chen S Y, Zhou J, Fan X X, Zhang D, Li S L, Zhang M X, Qian J 2021 Light 10 197
Google Scholar
[22] Benayas A, Hemmer E, Hong G S, et al. 2020 Near Infrared-emitting Nanoparticles for Biomedical Applications (Cham: Springer) pp11–15
[23] He Y, Wang S F, Yu P, Yan K, Ming J, Yao C Z, He Z Y, El-Toni A M, Khan A, Zhu X Y, Sun C X, Lei Z H, Zhang F 2021 Chem. Sci. 12 10474
Google Scholar
[24] Zhang Z Y, He K S, Chi C W, Hu Z H, Tian J 2022 EJNMMI 49 2531
Google Scholar
[25] Luo J D, Xie Z L, Lam J W Y, Cheng L, Chen H Y, Qiu C F, Kwok H S, Zhan X W, Liu Y Q, Zhu D B, Tang B Z 2001 Chem. Commun. 18 1740
Google Scholar
[26] Zhu W, Kang M M, Wu Q, Zhang Z J, Wu Y, Li C B, Li K, Wang L, Wang D, Tang B Z 2021 Adv. Funct. Mater. 31 2007026
Google Scholar
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