LED-based photoacoustic imaging system: why it achieves the same signal to noise ratio as solid-state-laser-based system: a review

The solid-state nanopore sensor offers a versatile platform for the rapid, label-free electrical detection and analysis of single molecules, especially on DNA sequencing. However, the overall signal-to-noise ratio (SNA) is a major challenge in sequencing applications. In our work, two different fluid systems made by metal and plexiglass have been designed to improve the signal to noise ratio of the solid-state nanopore sensor. From the measurements on the noise power spectra with a variety of conditions, it is found that plexiglass fluid system coupled with shielding box produces a good quality of electric signals on nanopore sensors.

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Comparison of regional myocardial velocities assessed by quantitative 2-dimensional and M-mode color doppler tissue imaging: Influence of the signal-to-noise ratio of color doppler myocardial images on velocity estimators of the doppler tissue imaging system

Journal of the American Society of Echocardiography ◽

10.1016/s0894-7317(98)80004-7 ◽

1998 ◽

Vol 11 (12) ◽

pp. 1093-1105 ◽

Cited By ~ 15

Author(s):

Jérôme Garot ◽

Benoît Diebold ◽

Geneviève Anne Derumeaux ◽

Jean-Luc Monin ◽

Philippe Bosio ◽

...

Keyword(s):

Imaging System ◽

Signal To Noise Ratio ◽

Color Doppler ◽

Tissue Imaging ◽

Doppler Tissue Imaging ◽

Signal To Noise ◽

Noise Ratio

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Measurement and analysis of perceivable signal-to-noise ratio for infrared imaging system with human vision

10.1117/12.999894 ◽

2012 ◽

Cited By ~ 2

Author(s):

Xin Liu ◽

Jing Zhao ◽

Honghua Chang ◽

Lin Ma

Keyword(s):

Infrared Imaging ◽

Imaging System ◽

Signal To Noise Ratio ◽

Human Vision ◽

Signal To Noise ◽

Infrared Imaging System ◽

Measurement And Analysis ◽

Noise Ratio

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Comparing current noise in biological and solid-state nanopores

10.1101/866384 ◽

2019 ◽

Cited By ~ 1

Author(s):

A. Fragasso ◽

S. Schmid ◽

C. Dekker

Keyword(s):

Solid State ◽

Single Molecule ◽

Signal To Noise Ratio ◽

Low Cost ◽

Ionic Current ◽

Low Noise ◽

Signal To Noise ◽

Experimental Conditions ◽

High Frequencies ◽

Noise Ratio

AbstractNanopores bear great potential as single-molecule tools for bioanalytical sensing and sequencing, due to their exceptional sensing capabilities, high-throughput, and low cost. The detection principle relies on detecting small differences in the ionic current as biomolecules traverse the nanopore. A major bottleneck for the further progress of this technology is the noise that is present in the ionic current recordings, because it limits the signal-to-noise ratio and thereby the effective time resolution of the experiment. Here, we review the main types of noise at low and high frequencies and discuss the underlying physics. Moreover, we compare biological and solid-state nanopores in terms of the signal-to-noise ratio (SNR), the important figure of merit, by measuring free translocations of a short ssDNA through a selected set of nanopores under typical experimental conditions. We find that SiNx solid-state nanopores provide the highest SNR, due to the large currents at which they can be operated and the relatively low noise at high frequencies. However, the real game-changer for many applications is a controlled slowdown of the translocation speed, which for MspA was shown to increase the SNR >160-fold. Finally, we discuss practical approaches for lowering the noise for optimal experimental performance and further development of the nanopore technology.

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

Molecular Imaging ◽

10.1177/1536012120913693 ◽

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.

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