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Journal of Biomedical Optics • new

Development and application of the near-infrared and white-light thoracoscope system for minimally invasive lung cancer surgery
Author(s): Yamin Mao; Kun Wang; Kunshan He; Jinzuo Ye; Fan Yang; Jian Zhou; Hao Li; Xiuyuan Chen; Jun Wang; Chongwei Chi; Jie Tian
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Paper Abstract

In minimally invasive surgery, the white-light thoracoscope as a standard imaging tool is facing challenges of the low contrast between important anatomical or pathological regions and surrounding tissues. Recently, the near-infrared (NIR) fluorescence imaging shows superior advantages over the conventional white-light observation, which inspires researchers to develop imaging systems to improve overall outcomes of endoscopic imaging. We developed an NIR and white-light dual-channel thoracoscope system, which achieved high-fluorescent signal acquisition efficiency and the simultaneously optimal visualization of the NIR and color dual-channel signals. The system was designed to have fast and accurate image registration and high signal-to-background ratio by optimizing both software algorithms and optical hardware components for better performance in the NIR spectrum band. The system evaluation demonstrated that the minimally detectable concentration of indocyanine green (ICG) was 0.01    μ M , and the spatial resolution was 35    μ m . The in vivo feasibility of our system was verified by the preclinical experiments using six porcine models with the intravenous injection of ICG. Furthermore, the system was successfully applied for guiding the minimally invasive segmentectomy in three lung cancer patients, which revealed that our system held great promise for the clinical translation in lung cancer surgeries.

Paper Details

Date Published: 5 June 2017
PDF: 9 pages
J. Biomed. Opt. 22(6) 066002 doi: 10.1117/1.JBO.22.6.066002
Published in: Journal of Biomedical Optics Volume 22, Issue 6
Show Author Affiliations
Yamin Mao, Institute of Automation (China)
Beijing Key Lab. of Molecular Imaging (China)
Univ. of Chinese Academy of Sciences (China)
Kun Wang, Institute of Automation (China)
Beijing Key Lab. of Molecular Imaging (China)
Kunshan He, Institute of Automation (China)
Beijing Key Lab. of Molecular Imaging (China)
Univ. of Chinese Academy of Sciences (China)
Jinzuo Ye, Institute of Automation (China)
Beijing Key Lab. of Molecular Imaging (China)
Univ. of Chinese Academy of Sciences (China)
Fan Yang, Peking Univ. People's Hospital (China)
Jian Zhou, Peking Univ. People's Hospital (China)
Hao Li, Peking Univ. People's Hospital (China)
Xiuyuan Chen, Peking Univ. People's Hospital (China)
Jun Wang, Peking Univ. People's Hospital (China)
Chongwei Chi, Institute of Automation (China)
Beijing Key Lab. of Molecular Imaging (China)
Jie Tian, Institute of Automation (China)
Beijing Key Lab. of Molecular Imaging (China)


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