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.

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 The Geometry of Noise in Color and Spectral Image Sensors

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4487
Author(s):  
Axel Clouet ◽  
Jérôme Vaillant ◽  
David Alleysson
Keyword(s):  
Signal To Noise Ratio ◽  
Spectral Estimation ◽  
Spectral Characteristics ◽  
Color Space ◽  
Color Correction ◽  
Signal To Noise ◽  
Noise Evaluation ◽  
Noise Ratio ◽  
Correction Step ◽  
The Impact

Digital images are always affected by noise and the reduction of its impact is an active field of research. Noise due to random photon fall onto the sensor is unavoidable but could be amplified by the camera image processing such as in the color correction step. Color correction is expressed as the combination of a spectral estimation and a computation of color coordinates in a display color space. Then we use geometry to depict raw, spectral and color signals and noise. Geometry is calibrated on the physics of image acquisition and spectral characteristics of the sensor to study the impact of the sensor space metric on noise amplification. Since spectral channels are non-orthogonal, we introduce the contravariant signal to noise ratio for noise evaluation at spectral reconstruction level. Having definitions of signal to noise ratio for each steps of spectral or color reconstruction, we compare performances of different types of sensors (RGB, RGBW, RGBWir, CMY, RYB, RGBC).

Performance Analysis of Hybrid Fusion in Cognitive Radio Networks

2013 ◽  
Vol 479-480 ◽  
pp. 1027-1031
Author(s):  
Man Man Guo ◽  
Yun Xue Liu ◽  
Wen Qiang Fan
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Signal To Noise Ratio ◽  
Radio Networks ◽  
Base Station ◽  
Good Choice ◽  
Signal To Noise ◽  
Noise Ratio ◽  
The Impact ◽  
Hybrid Fusion

Spectrum sensing is a crucial issue in cognitive radio networks for primary user detection. Cooperative sensing based on energy detection in the cognitive radio network with multiple antennas base-station is considered in this letter. To improve the sensing performance, we investigate hybrid fusion of the observed energies from the base-station and decisions (1bit, hard information) from different cognitive radio (CR) users around the base-station. Further, we present an optimized scheme where the global detection probability can be maximized according to the Neyman-Pearson criterion. Finally the impact of the change of parameters (Signal to Noise Ratio and number of CR users) in the optimized scheme is analyzed. Numerical simulations and extensive analysis confirm that hybrid fusion base on the optimized scheme is a good choice, also, Signal to Noise Ratio (SNR) and number of CR users does not have influence on the optimized scheme

scholarly journals Evaluation of Nonlinear Interference Effects in a Dispersion Managed (DM) Optical Fiber: Performance and Transmission Analysis of 16QAM Modulation using Split Step Fourier Method

2020 ◽  
Author(s):  
Reinhardt Rading
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Optical Fiber ◽  
Signal To Noise Ratio ◽  
Fourier Method ◽  
Interference Effects ◽  
Signal To Noise ◽  
Optical Links ◽  
Noise Ratio ◽  
The Impact

<div>This paper investigates the impact on the optical</div><div>signal-to-noise ratio (OSNR) of the residual per span (RDPS) in a N × 100km dispersion managed system with zero total accumulated dispersion from input to output using split step Fourier method (SSFM) -Monte Carlo simulation. </div><div><br></div><div>This paper shows that the nonlinear interference NLI does in-fact impact the performance yielding different best working power depending on the value of Nx100 km span and the type of dispersion managed link. The paper shows that dispersion uncompensated optical links are preferable to dispersion managed fibers in equalizing NLI effects in long haul optical links.</div>

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