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| Noninvasive Cerebral Blood Flow Measurement Based on NIRS-ICG |
| ZHA Yu-tong1, LIU Guang-da1, WANG Yong-xiang1, WANG Te2, CAI Jing1, ZHOU Ge1, SHANG Xiao-hu1* |
1. College of Instrument Science and Electrical Engineering, Jilin University, Changchun 130061, China
2. Department of Orthodontics, School and Hospital of Stomatology, Jilin University, Changchun 130021, China |
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Abstract Clinicians take cerebral blood flow (CBF) and other cerebral vascular hemodynamic parameters as diagnostic basis for cerebral blood oxygen and cerebrovascular reserve capability. But current detection methods have disadvantages such as technically complexity and poor universality of reagents or equipments to all diagnostic groups. To solve the problems above, a new noninvasive, rapid and repeated technique for measurement of CBF by combination of near-infrared spectroscopy (NIRS) and indocyanine green (ICG) pulse-dye densitometry is proposed by the name of NIRS-ICG. After ICG intravenous bolus injection, concentration curves of three main chromophores, which are oxygenated hemoglobin, reduced hemoglobin and ICG, in both brain tissue and cerebral artery, are estimated. And then ICG accumulation rate and introduced quantity models are established in order to obtain CBF and other cerebral vascular hemodynamic parameters. To verify the feasibility of this method, CBF and other parameters of normocapnia piglet and hypercapnia piglets were detected in NIRS-ICG. 3, 6 and 9 percent of CO2 mixed with air was mechanical ventilated into four group Chinese experimental miniature pigs. NIRS-ICG was used to measure CBF, cerebral arterial oxygen saturation (SaO2), and mean transit time (MTT) after rapidly intravenous infusion of ICG. The test value showed that CBF increased with the increase of CO2 ratio, SaO2 decreased with the increase of CO2 ratio, and MTT had no significant change. It is proved that the measurements reflected changes in cerebral blood flow reliably. NIRS-ICG is applicable both in cerebral blood oxygen and cerebrovascular reserve capability detection.
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Received: 2016-07-21
Accepted: 2016-11-09
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Corresponding Authors:
SHANG Xiao-hu
E-mail: 243341292@qq.com
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