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基于光声泵浦成像的氧分压测量定量分析

何霄 肖小舟 何滨 薛平 肖嘉莹

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基于光声泵浦成像的氧分压测量定量分析

何霄, 肖小舟, 何滨, 薛平, 肖嘉莹

Quantitative analysis of oxygen partial pressure measurements based on photoacoustic pump-probe imaging

He Xiao, Xiao Xiao-Zhou, He Bin, Xue Ping, Xiao Jia-Ying
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  • 光声泵浦成像是一种新型的高特异性光声分子影像技术. 它可以避免常规光声成像中来自血液等强背景信号的干扰, 实现组织中微弱目标分子的探测, 并通过对泵浦-探测激光间的延时扫描, 获得组织中的氧分压分布. 本文采用亚甲基蓝作为分子探针, 通过对血红蛋白溶液中氧分压变化的监测, 开展了对光声泵浦成像的定量分析研究. 本文采用高斯噪声模型, 获得了三重态差分信号稳定性随着平均次数变化的规律, 并在此基础上对氧分压测量的误差进行了分析. 结果表明, 在平均次数为200次条件下, 氧分压在300—550 mmHg (1 mmHg = 133 Pa)区间内, 所搭建系统的检测精度优于33 mmHg. 本研究将对光声泵浦成像技术的进一步发展和应用起到重要的指导作用.
    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.
      通信作者: 肖嘉莹, jiayingxiao@csu.edu.cn
    • 基金项目: 低维量子物理国家重点实验室开放科研基金项目(批准号: KF202209)、湖南省自然科学基金 (批准号: 2022JJ30756)、中南大学创新驱动项目(批准号: 2020CX004)和深圳市科技创新委员会自由探索基础研究计划(批准号: 2021Szvup168)资助的课题.
      Corresponding author: Xiao Jia-Ying, jiayingxiao@csu.edu.cn
    • Funds: Project supported by the Open Research Fund Program of the State Key Laboratory of Low-Dimensional Quantum Physics, China (Grant No. KF202209), the Natural Science Foundation of Hunan Province, China (Grant No. 2022JJ30756), the Innovation-Driven Project of Central South University, China (Grant No. 2020CX004), and the Department of Science and Technology Innovation Committee of Shenzhen, Free Exploration of Fundamental Research Program, China (Grant No. 2021Szvup168).
    [1]

    Attia A B E, Balasundaram G, Moothanchery M, Dinish U S, Bi R, Ntziachristos V, Olivo M 2019 Photoacoustics 16 100144Google Scholar

    [2]

    Wang S, Lin J, Wang T F, Chen X Y, Huang P 2016 Theranostics 6 2394Google Scholar

    [3]

    Yao J J, Wang L V 2018 Curr. Opin. Chem. Biol. 45 104Google Scholar

    [4]

    Li L, Wang L V 2021 BME Front. 2021 9823268Google Scholar

    [5]

    Weber J, Beard P C, Bohndiek S E 2016 Nat. Methods 13 639Google Scholar

    [6]

    Tan J W Y, Lee C H, Kopelman R, Wang X D 2018 Sci. Rep. 8 9290Google Scholar

    [7]

    Sud D, Zhong W, Beer D G, Mycek M A 2006 Opt. Express 14 4412Google Scholar

    [8]

    Shao Q, Ashkenazi S 2015 J. Biomed. Opt. 20 036004Google Scholar

    [9]

    Ashkenazi S, Huang S W, Horvath T, Koo Y E, Kopelman R 2018 J. Biomed. Opt. 13 034023Google Scholar

    [10]

    Jo J, Lee C H, Folz J, Tan J W Y, Wang X D, Kopelman R 2019 ACS Nano 13 14024Google Scholar

    [11]

    Wang B, Xie Y, He X, Jiang J S, Xiao J Y, Chen Z Y 2022 Opt. Express 30 39129Google Scholar

    [12]

    Correia J H, Rodrigues J A, Pimenta S, Dong T, Yang Z C 2021 Pharmaceutics 13 1332Google 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 0071Google Scholar

    [14]

    Gao L, Zhang C, Li C Y, Wang L V 2013 Appl. Phys. Lett. 102 193705Google Scholar

    [15]

    Zhao W A, Ali M M, Brook M A, Li Y F 2008 Angew. Chem. Int. Ed. 47 6330Google Scholar

    [16]

    高晓怡, 李景虹 2022 中国科学: 化学 52 1609Google Scholar

    Gao X Y, Li J H 2022 Sci. China Chem. 52 1609Google Scholar

    [17]

    Orth K, Beck G, Genze F, Rück A 2000 J. Photochem. Photobiol. B Biol. 57 186Google 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 102635Google Scholar

    [19]

    Al-Talib M, Al Kadiri M, Al-Masri A Q 2020 Commun. Stat. Theory Methods 49 5627Google Scholar

    [20]

    Lee D K, In J, Lee S 2015 Korean. J. Anesthesiology 68 220Google 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 4805Google Scholar

  • 图 1  光声泵浦成像原理示意图

    Fig. 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

    Fig. 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

    Fig. 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)

    Fig. 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

    Fig. 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

    Fig. 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.

  • [1]

    Attia A B E, Balasundaram G, Moothanchery M, Dinish U S, Bi R, Ntziachristos V, Olivo M 2019 Photoacoustics 16 100144Google Scholar

    [2]

    Wang S, Lin J, Wang T F, Chen X Y, Huang P 2016 Theranostics 6 2394Google Scholar

    [3]

    Yao J J, Wang L V 2018 Curr. Opin. Chem. Biol. 45 104Google Scholar

    [4]

    Li L, Wang L V 2021 BME Front. 2021 9823268Google Scholar

    [5]

    Weber J, Beard P C, Bohndiek S E 2016 Nat. Methods 13 639Google Scholar

    [6]

    Tan J W Y, Lee C H, Kopelman R, Wang X D 2018 Sci. Rep. 8 9290Google Scholar

    [7]

    Sud D, Zhong W, Beer D G, Mycek M A 2006 Opt. Express 14 4412Google Scholar

    [8]

    Shao Q, Ashkenazi S 2015 J. Biomed. Opt. 20 036004Google Scholar

    [9]

    Ashkenazi S, Huang S W, Horvath T, Koo Y E, Kopelman R 2018 J. Biomed. Opt. 13 034023Google Scholar

    [10]

    Jo J, Lee C H, Folz J, Tan J W Y, Wang X D, Kopelman R 2019 ACS Nano 13 14024Google Scholar

    [11]

    Wang B, Xie Y, He X, Jiang J S, Xiao J Y, Chen Z Y 2022 Opt. Express 30 39129Google Scholar

    [12]

    Correia J H, Rodrigues J A, Pimenta S, Dong T, Yang Z C 2021 Pharmaceutics 13 1332Google 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 0071Google Scholar

    [14]

    Gao L, Zhang C, Li C Y, Wang L V 2013 Appl. Phys. Lett. 102 193705Google Scholar

    [15]

    Zhao W A, Ali M M, Brook M A, Li Y F 2008 Angew. Chem. Int. Ed. 47 6330Google Scholar

    [16]

    高晓怡, 李景虹 2022 中国科学: 化学 52 1609Google Scholar

    Gao X Y, Li J H 2022 Sci. China Chem. 52 1609Google Scholar

    [17]

    Orth K, Beck G, Genze F, Rück A 2000 J. Photochem. Photobiol. B Biol. 57 186Google 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 102635Google Scholar

    [19]

    Al-Talib M, Al Kadiri M, Al-Masri A Q 2020 Commun. Stat. Theory Methods 49 5627Google Scholar

    [20]

    Lee D K, In J, Lee S 2015 Korean. J. Anesthesiology 68 220Google 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 4805Google Scholar

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出版历程
  • 收稿日期:  2023-06-26
  • 修回日期:  2023-07-21
  • 上网日期:  2023-08-19
  • 刊出日期:  2023-11-05

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