Analysis on the influences of range-gated underwater laser imaging system parameters on the signal to noise ratio

2013 ◽  
Author(s):  
Wei-long Ge ◽  
Liang-hong Hua ◽  
Hong-wei Han
Keyword(s):  
Imaging System ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Laser Imaging ◽  
System Parameters ◽  
Noise Ratio ◽  
Underwater Laser

Design of imaging system of cloud and aerosol polarization imager with high signal-to-noise ratio

2018 ◽  
Vol 47 (11) ◽  
pp. 1111006
Author(s):  
李 帅 Li Shuai ◽  
徐抒岩 Xu Shuyan ◽  
刘栋斌 Liu Dongbin ◽  
张 航 Zhang Hang
Keyword(s):  
Imaging System ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Signal to Noise Ratio as a Cross-Platform Metric for Intraoperative Fluorescence Imaging

2020 ◽  
Vol 19 ◽  
pp. 153601212091369
Author(s):  
Asmaysinh Gharia ◽  
Efthymios P. Papageorgiou ◽  
Simeon Giverts ◽  
Catherine Park ◽  
Mekhail Anwar
Keyword(s):  
Tumor Cells ◽  
Imaging System ◽  
Signal To Noise Ratio ◽  
Size Variation ◽  
Pixel Size ◽  
Signal To Noise ◽  
Noise Sources ◽  
Noise Measurements ◽  
Noise Ratio ◽  
The Impact

Real-time molecular imaging to guide curative cancer surgeries is critical to ensure removal of all tumor cells; however, visualization of microscopic tumor foci remains challenging. Wide variation in both imager instrumentation and molecular labeling agents demands a common metric conveying the ability of a system to identify tumor cells. Microscopic disease, comprised of a small number of tumor cells, has a signal on par with the background, making the use of signal (or tumor) to background ratio inapplicable in this critical regime. Therefore, a metric that incorporates the ability to subtract out background, evaluating the signal itself relative to the sources of uncertainty, or noise is required. Here we introduce the signal to noise ratio (SNR) to characterize the ultimate sensitivity of an imaging system and optimize factors such as pixel size. Variation in the background (noise) is due to electronic sources, optical sources, and spatial sources (heterogeneity in tumor marker expression, fluorophore binding, and diffusion). Here, we investigate the impact of these noise sources and ways to limit its effect on SNR. We use empirical tumor and noise measurements to procedurally generate tumor images and run a Monte Carlo simulation of microscopic disease imaging to optimize parameters such as pixel size.

scholarly journals Signal to Noise Ratio as a Cross-Platform Metric for Intraoperative Fluorescence Imaging

2019 ◽  
Author(s):  
Asmaysinh Gharia ◽  
Efthymios P. Papageorgiou ◽  
Simeon Giverts ◽  
Catherine Park ◽  
Mekhail Anwar
Keyword(s):  
Tumor Cells ◽  
Imaging System ◽  
Signal To Noise Ratio ◽  
Size Variation ◽  
Pixel Size ◽  
Signal To Noise ◽  
Noise Sources ◽  
Noise Measurements ◽  
Noise Ratio ◽  
The Impact

AbstractReal-time molecular imaging to guide curative cancer surgeries is critical to ensure removal of all tumor cells, however visualization of microscopic tumor foci remains challenging. Wide variation in both imager instrumentation and molecular labeling agents demands a common metric conveying the ability of a system to identify tumor cells. Microscopic disease, comprised of a small number of tumor cells, has a signal on par with the background, making the use of signal (or tumor) to background ratio inapplicable in this critical regime. Therefore, a metric that incorporates the ability to subtract out background, evaluating the signal itself relative to the sources of uncertainty, or noise is required. Here we introduce the signal-to-noise ratio (SNR) to characterize the ultimate sensitivity of an imaging system, and optimize factors such as pixel size. Variation in the background (noise) are due to electronic sources, optical sources, and spatial sources (heterogeneity in tumor marker expression, fluorophore binding, diffusion). Here we investigate the impact of these noise sources and ways to limit its effect on SNR. We use empirical tumor and noise measurements to procedurally generate tumor images and run a monte carlo simulation of microscopic disease imaging to optimize parameters such as pixel size.

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