| 光谱学与光谱分析 |
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| Research on Traumatic Brain Real-Time Monitoring by Near-Infrared Technology |
| MAO Wen-lan1,QIAN Zhi-yu1*,YANG Tian-ming2,HE Liang2,GUO Li-na1,WU Qi2 |
1. Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2. Department of Neurosurgery, Zhongda Hospital, Southeast University, Nanjing 210009, China |
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Abstract In order to monitoring the development of traumatic brain edema in vivo, a specifically designed optical parameters of tissue testing system wit h a mini-invasion bifurcated optical fiber probe and a fiber spectrometer was used to monitor the reduced scattering coefficient(μ′s) of t he rat traumatic brain while the counterpart parameter, i.e. brain water content (BWC), was also measured. Acute rat regional brain trauma was applied according to Feeney’s apparatus. The changes of brain edema were monitored by near-infrared spectroscopy (NIRS) technology and by measuring the water content of the brain. Experiment result showed that distinct brain edema in injured areas was found at 6 hours later after trauma, which reached a summit of severity at 24-72 hours later after trauma, then gradually declined. After using the dehydrant, the brain edema situation became better, and then, the edema occurred again whilet he medicamentosus effect of dehydrant was gradually lost. It can be showed that μ′s had similar change profile wit h BWC and the two parameters were well linearly relative to each other. μ′s is a good indicator for monitoring traumatic brain edema and t he medicamentosus effect of dehydrant. As a result near-infrared spectroscopy is a new feasible method of monitoring the development of traumatic brain edema in vivo.
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Received: 2007-10-08
Accepted: 2008-01-12
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Corresponding Authors:
QIAN Zhi-yu
E-mail: zhiyu@nuaa.edu.cn
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[1] Nisson P, Gazelius B, Carlson H, et al. Neurotrauma, 1996, 13(4): 201.
[2] Bergusneider M, Alwan A A, Falkson L, et al. Acta Neuochir Suppl. (Wien), 1998, 71(2):266.
[3] Kampfl A, Pfausler B, Denc hev D, et al. Acta Neuochir Suppl.(Wien) 1997, 70: 112.
[4] Parasad Paras N. Introduction to Biophotonics(生物光子学导论). Translated by HE Sai-ling, et al(何赛灵， 等译). Hangzhou: Zhejiang University Press(杭州：浙江大学出版社), 2006. 136.
[5] QIAN Zhi-yu, CHEN Ren-wen, GU Yue-qing, et al(钱志余, 陈仁文, 顾月清, 等). Journal of Nanjing University of Aeronautics & Astronautics(南京航空航天大学学报), 2004, 36(3): 369.
[6] Qian Zhiyu, Victor S S, Gu Yueqing, et al. Optics Express, 2003,11(16): 1845.
[7] LI Rong, QIAN Zhi-yu, DAI Li-juan(李 荣，钱志余，戴丽娟). Computer Measurement & Control(计算机测量与控制), 2007， 15(2): 154.
[8] Johns Maureen, Giller Cole A, German Dwight C, et al. Optics Express, 2005, 13(13): 4828.
[9] Solonenko M, Cheung R, Busch T M, et al. Phys. Med. Biol., 2002, 47: 857.
[10] Cerussi A E, Berger A J, Bevilacqua F, et al. Academic Radiology, 2001, 8: 211.
[11] Dam J S, Pedersen C B, Dalgaard T, et al. Appl. Opt., 2001, 40: 1155.
[12] Senapati Arun Kumar, Radhakrishnan Harsha, Liu Hanli, et al. Brain Research Protocols, 2005, 14(2): 119.
[13] Farrell T J, Patterson M S, Wilson B. Medical Physics, 1992, 19: 879.
[14] Feeney D M, Boyeson M H, Linn R T, et al. Brain Res., 1981, 211(1): 67.
[15] Samdani A F, Tamarago R J, Long D M. Neurosurg Quarterly, 1999, 9(2): 120. |
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