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Pump-probe-based photoacoustic imaging is an innovative technique for high-specificity molecular imaging in deep tissues. Compared with conventional photoacoustic imaging, this method effectively eliminates the interference from blood signal and other background signal, enabling the detection of subtle target molecules. Additionally, the manipulating of the time delay between the pump laser and probe laser can facilitate non-invasive mapping of oxygen partial pressure distribution within tissues. To quantify the photoacoustic pump-probe imaging, we use methylene blue as the molecular probe to monitor changes in oxygen partial pressure within a hemoglobin solution. Utilizing a Gaussian noise model, we investigate the relationship between the stability of the triplet-state difference signal and the average number, and also evaluate the error associated with measuring oxygen partial pressure. The results demonstrate that the detection accuracy of the system is better than 33 mmHg (1 mmHg = 133 Pa) in the oxygen partial pressure range of about 300 to 550 mmHg after 200 times of averaging. This research will play a significant role in guiding the further advancement and application of pump-probe-based photoacoustic imaging technology.
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Keywords:
- pump-probe-based photoacoustic imaging /
- oxygen partial pressure /
- transient triplet-differential method /
- quantitative analysis
[1] Attia A B E, Balasundaram G, Moothanchery M, Dinish U S, Bi R, Ntziachristos V, Olivo M 2019 Photoacoustics 16 100144
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图 1 光声泵浦成像原理示意图
Figure 1. Illustration of the principle of photoacoustic pump-probe imaging.
图 2 基于泵浦-探测的组织氧含量体外光声定量检测系统 (a) 光声泵浦成像系统图; (b) 溶液循环装置图; (c) 脉冲序列波形示意图
Figure 2. Based on pump-probe technique, in vitro quantitative detection system for tissue oxygen content: (a) Schematic diagram of photoacoustic pump-probe imaging system; (b) diagram of solution circulation device; (c) illustration of pulse sequence waveform.
图 3 亚甲基蓝的基态和激发态吸收光谱 (a) 基态吸收光谱图; (b) 经能量校正后的T1态吸收光谱图
Figure 3. Ground-state and excited-state absorption spectra of methylene blue: (a) The absorption spectrum of the ground state; (b) the energy-corrected absorption spectrum of the T1 state.
图 4 通过光声泵浦成像得到的光声信号 (a) 归一化后的MB的光声信号示意图; (b) 归一化后的TTD幅值分布图(基于平均值归一化)
Figure 4. Photoacoustic signals obtained through photoacoustic pump-probe imaging: (a) Schematic representation of the normalized photoacoustic signal of MB; (b) normalized distribution of TTD amplitude (normalized to the mean).
图 5 TTD信号波动性分 (a) 含不同浓度牛血红蛋白的TTD标准差; (b) 含不同浓度亚甲基蓝溶液的TTD标准差
Figure 5. Analysis of TTD signal variability: (a) TTD standard deviation with different concentrations of bovine hemoglobin; (b) TTD standard deviation with different concentrations of methylene blue solution.
图 6 不同氧分压下的测量误差分析图 (a) TTD衰减曲线示意图; (b) T1态寿命累计分布图; (c) 累积概率积分曲线图; (d) 概率密度分布曲线图; (e) T1态寿命和氧分压的关系示意图; (f) 氮氧比为1∶1时T1态寿命和氧分压波动性示意图
Figure 6. Measurement error analysis under different oxygen partial pressures: (a) Schematic of TTD decay curve; (b) cumulative distribution of T1 state lifetimes; (c) cumulative probability integration curve; (d) probability density distribution curve; (e) schematic of the relationship between T1 state lifetime and oxygen partial pressure; (f) illustration of T1 state lifetime fluctuation with oxygen partial pressure at nitrogen-to-oxygen ratio of 1∶1.
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[1] Attia A B E, Balasundaram G, Moothanchery M, Dinish U S, Bi R, Ntziachristos V, Olivo M 2019 Photoacoustics 16 100144
Google Scholar
[2] Wang S, Lin J, Wang T F, Chen X Y, Huang P 2016 Theranostics 6 2394
Google Scholar
[3] Yao J J, Wang L V 2018 Curr. Opin. Chem. Biol. 45 104
Google Scholar
[4] Li L, Wang L V 2021 BME Front. 2021 9823268
Google Scholar
[5] Weber J, Beard P C, Bohndiek S E 2016 Nat. Methods 13 639
Google Scholar
[6] Tan J W Y, Lee C H, Kopelman R, Wang X D 2018 Sci. Rep. 8 9290
Google Scholar
[7] Sud D, Zhong W, Beer D G, Mycek M A 2006 Opt. Express 14 4412
Google Scholar
[8] Shao Q, Ashkenazi S 2015 J. Biomed. Opt. 20 036004
Google Scholar
[9] Ashkenazi S, Huang S W, Horvath T, Koo Y E, Kopelman R 2018 J. Biomed. Opt. 13 034023
Google Scholar
[10] Jo J, Lee C H, Folz J, Tan J W Y, Wang X D, Kopelman R 2019 ACS Nano 13 14024
Google Scholar
[11] Wang B, Xie Y, He X, Jiang J S, Xiao J Y, Chen Z Y 2022 Opt. Express 30 39129
Google Scholar
[12] Correia J H, Rodrigues J A, Pimenta S, Dong T, Yang Z C 2021 Pharmaceutics 13 1332
Google Scholar
[13] Li L, Zhu L R, Ma C, Lin L, Yao J J, Wang L D, Maslov K, Zhang R Y, Chen W Y, Shi J H, Wang L V 2017 Nat. Biomed. Eng. 1 0071
Google Scholar
[14] Gao L, Zhang C, Li C Y, Wang L V 2013 Appl. Phys. Lett. 102 193705
Google Scholar
[15] Zhao W A, Ali M M, Brook M A, Li Y F 2008 Angew. Chem. Int. Ed. 47 6330
Google Scholar
[16] 高晓怡, 李景虹 2022 中国科学: 化学 52 1609
Google Scholar
Gao X Y, Li J H 2022 Sci. China Chem. 52 1609
Google Scholar
[17] Orth K, Beck G, Genze F, Rück A 2000 J. Photochem. Photobiol. B Biol. 57 186
Google Scholar
[18] Grande M P D, Miyake A M, Nagamine M K, Leite J V P, da Fonseca I I M, Massoco C O, Dagli M L Z 2022 Photodiagn. Photodyn. 37 102635
Google Scholar
[19] Al-Talib M, Al Kadiri M, Al-Masri A Q 2020 Commun. Stat. Theory Methods 49 5627
Google Scholar
[20] Lee D K, In J, Lee S 2015 Korean. J. Anesthesiology 68 220
Google Scholar
[21] Schillaci M A, Schillaci M E 2022 Evol. Hum. Behav. 171 103230.
Google Scholar
[22] Thistleton W J, Marsh J A, Nelson K, Tsallis C 2007 IEEE Trans. Inf. Theory 53 4805
Google Scholar
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