Reliable spectroscopic process monitoring using an optimal acquisition time procedure determined by signal-to-noise ratio

Measurement ◽  
2018 ◽  
Vol 122 ◽  
pp. 100-105
Author(s):  
J. Meyer-Kirschner ◽  
A. Mitsos ◽  
J. Viell
Keyword(s):  
Process Monitoring ◽  
Signal To Noise Ratio ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Noise Ratio

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

Image Acquisition Using Non-Pixeled Amorphous Silicon Based Sensors

2001 ◽  
Vol 685 ◽  
Author(s):  
M. Fernandes ◽  
Yu. Vygranenko ◽  
J. Martins ◽  
M. Vieira
Keyword(s):  
Amorphous Silicon ◽  
Signal To Noise Ratio ◽  
Image Acquisition ◽  
Short Circuit ◽  
Acquisition Time ◽  
Large Area ◽  
Signal To Noise ◽  
Scanning Beam ◽  
Noise Ratio ◽  
Silicon Based

AbstractWe suggest to enhance the performance of image acquisition systems based on large area amorphous silicon based sensors by optimizing the readout parameters such as the intensity and cross-section of scanner beam, acquisition time and bias conditions. The main output device characteristics as image responsivity, signal to noise ratio and spatial resolution were analyzed in open circuit, short circuit and photodiode modes. The result show that the highest signal to noise ratio and best dark to bright ratio can be achieved in short circuit mode.It was shown that the sensor resolution is related to the basic device parameters and, in practice, limited by the acquisition time and scanning beam properties. The scanning beam spot size limits the resolution due to the overlapping of dark and illuminated zones leading to a blurring effect on the final image and a consequent degradation in the resolution.

Super-resolution methods in MRI: Can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time?

2012 ◽  
Vol 68 (6) ◽  
pp. 1983-1993 ◽  
Author(s):  
Esben Plenge ◽  
Dirk H. J. Poot ◽  
Monique Bernsen ◽  
Gyula Kotek ◽  
Gavin Houston ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Trade Off ◽  
Noise Ratio

Low Flip Angle Gradient Echo Magnetic Resonance Imaging of the Cervical Spine at 0.3 Tesla

1989 ◽  
Vol 30 (4) ◽  
pp. 343-348 ◽  
Author(s):  
S. Holtås ◽  
F. Ståhlberg ◽  
H. Nilsson ◽  
E.-M. Larsson ◽  
A. Ericsson
Keyword(s):  
Cervical Spine ◽  
Signal To Noise Ratio ◽  
Spin Echo ◽  
Flip Angle ◽  
Acquisition Time ◽  
Gradient Echo ◽  
Signal To Noise ◽  
Spine Imaging ◽  
Spin Echo Sequence ◽  
Noise Ratio

The influence of flip angle and TR on signal to noise ratio and contrast between cerebrospinal fluid (CSF) and cord was evaluated in cervical spine imaging in 5 volunteers, using gradient echo technique. All experiments were performed on a 0.3 tesla Fonar β-3000 M scanner using solenoidal surface coils. The most useful sequence was considered to be TR/TE=300/12 ms and 10° flip angle. This sequence provided images with a ‘myelographic appearance’ with good delineation of cord, CSF and epidural space. The grey and white matter was also regularly visualized. The acquisition time was considerably shorter than would have been necessary if a long TR/TE spin echo sequence had been used to obtain the same contrast pattern and the sequence was not as sensitive to motion as was the spin echo sequence. The sequence was also evaluated in 10 patients with degenerative disease and in 5 with lesions in the cord. The gradient echo sequence was found to be equal to or better than short and long TR/TE spin echo sequences in demonstrating narrowing of the spinal canal and cord lesions. The drawback is the limited signal to noise ratio.

Vibrational Stark Spectroscopy of Fluorobenzene Using Quantum Cascade Laser Dual Frequency Combs

2019 ◽  
Vol 74 (3) ◽  
pp. 347-356 ◽  
Author(s):  
Urszula Szczepaniak ◽  
Samuel Hayes Schneider ◽  
Raphael Horvath ◽  
Jacek Kozuch ◽  
Markus Geiser
Keyword(s):  
Quantum Cascade Laser ◽  
Signal To Noise Ratio ◽  
Spectral Response ◽  
Acquisition Time ◽  
Dual Frequency ◽  
Stark Spectroscopy ◽  
Signal To Noise ◽  
Quantum Cascade ◽  
Ft Ir ◽  
Noise Ratio

We demonstrate the performance of a dual frequency comb quantum cascade laser (QCL) spectrometer for the application of vibrational Stark spectroscopy. Measurements performed on fluorobenzene with the dual-comb spectrometer (DCS) were compared to results obtained using a conventional Fourier transform infrared (FT-IR) instrument in terms of spectral response, parameter estimation, and signal-to-noise ratio (S/N). The dual-comb spectrometer provided similar qualitative and quantitative data as the FT-IR setup in 250 times shorter acquisition time. For fluorobenzene, the DCS measurement resulted in a more precise estimation of the fluorobenzene Stark tuning rate ((0.81 ± 0.09) cm−1/(MV/cm)) than with the FT-IR system ((0.89 ± 0.15) cm−1/(MV/cm)). Both values are in accordance with the previously reported value of 0.84 cm−1/(MV/cm). We also point to an improvement of signal-to-noise ratio in the DCS configuration. Additional characteristics of the dual-comb spectrometer applicable to vibrational Stark spectroscopy and their scaling properties for future applications are discussed.

scholarly journals MR-contrast-aware image-to-image translations with generative adversarial networks

2021 ◽  
Author(s):  
Jonas Denck ◽  
Jens Guehring ◽  
Andreas Maier ◽  
Eva Rothgang
Keyword(s):  
Signal To Noise Ratio ◽  
Image Contrast ◽  
Clinical Indication ◽  
Generative Adversarial Networks ◽  
Acquisition Time ◽  
Large Set ◽  
Mr Images ◽  
Signal To Noise ◽  
Style Transfer ◽  
Noise Ratio

Abstract Purpose A magnetic resonance imaging (MRI) exam typically consists of several sequences that yield different image contrasts. Each sequence is parameterized through multiple acquisition parameters that influence image contrast, signal-to-noise ratio, acquisition time, and/or resolution. Depending on the clinical indication, different contrasts are required by the radiologist to make a diagnosis. As MR sequence acquisition is time consuming and acquired images may be corrupted due to motion, a method to synthesize MR images with adjustable contrast properties is required. Methods Therefore, we trained an image-to-image generative adversarial network conditioned on the MR acquisition parameters repetition time and echo time. Our approach is motivated by style transfer networks, whereas the “style” for an image is explicitly given in our case, as it is determined by the MR acquisition parameters our network is conditioned on. Results This enables us to synthesize MR images with adjustable image contrast. We evaluated our approach on the fastMRI dataset, a large set of publicly available MR knee images, and show that our method outperforms a benchmark pix2pix approach in the translation of non-fat-saturated MR images to fat-saturated images. Our approach yields a peak signal-to-noise ratio and structural similarity of 24.48 and 0.66, surpassing the pix2pix benchmark model significantly. Conclusion Our model is the first that enables fine-tuned contrast synthesis, which can be used to synthesize missing MR-contrasts or as a data augmentation technique for AI training in MRI. It can also be used as basis for other image-to-image translation tasks within medical imaging, e.g., to enhance intermodality translation (MRI → CT) or 7 T image synthesis from 3 T MR images.

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