Laser & Optoelectronics Progress, Volume. 60, Issue 6, 0617003(2023)
Rapid Parathyroid Recognition System Based on Near-Infrared Autofluorescence
Based on near-infrared autofluorescence, a rapid identification system for parathyroid glands during surgery is designed, the system is of great value for rapid identification of parathyroid glands during operation. In this research, a ring-shaped adjustable excitation light source and a high-precision adjustable LED constant current source are designed. The near-infrared light source is used to excite tissue fluorescence, and the tissue autofluorescence information is collected by a high-sensitivity CMOS camera. The obtained fluorescence images are processed, accurately identifying parathyroid glands. Simulating tissue fluorescence through gradient concentration of indocyanine green (ICG) solution, the experimentally measured fluorescence intensity is positively correlated with the concentration of ICG, and both the signal-to-noise ratio and signal-to-background ratio meet the requirements for intraoperative discrimination, which verifie the sensitivity and accuracy of the proposed system for different fluorescence intensities. Using this system to test tissue phantoms, the fluorescent phantom can be clearly distinguished from the background. The parathyroid gland and surrounding tissues were tested, and the parathyroid gland is green and clearly distinguished from the surrounding tissues, which preliminarily verifies that the proposed system can be used for the identification and detection of parathyroid glands.
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Chunhui Yao, Yang Zhang, Bin Liu, Chijian Zhang, Jiayun Zheng, Xia Wang, Xu Kang, Quanfu Wang, Zhongsheng Li, Yong Liu, Meili Dong, Yikun Wang. Rapid Parathyroid Recognition System Based on Near-Infrared Autofluorescence[J]. Laser & Optoelectronics Progress, 2023, 60(6): 0617003
Category: Medical Optics and Biotechnology
Received: Jan. 2, 2022
Accepted: Jan. 11, 2022
Published Online: Mar. 16, 2023
The Author Email: Dong Meili (dongmeili@aiofm.ac.cn), Wang Yikun (wyk@aiofm.ac.cn)