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.

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.

A wavelet-based method for improving signal-to-noise ratio and contrast in MR images

2000 ◽  
Vol 18 (2) ◽  
pp. 169-180 ◽  
Author(s):  
M.E. Alexander ◽  
R. Baumgartner ◽  
A.R. Summers ◽  
C. Windischberger ◽  
M. Klarhoefer ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Mr Images ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Evaluation of sted super-resolution image quality by image correlation spectroscopy (QuICS)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elena Cerutti ◽  
Morgana D’Amico ◽  
Isotta Cainero ◽  
Gaetano Ivan Dellino ◽  
Mario Faretta ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Image Contrast ◽  
Correlation Spectroscopy ◽  
Image Correlation ◽  
Signal To Noise ◽  
Image Correlation Spectroscopy ◽  
Noise Ratio

AbstractQuantifying the imaging performances in an unbiased way is of outmost importance in super-resolution microscopy. Here, we describe an algorithm based on image correlation spectroscopy (ICS) that can be used to assess the quality of super-resolution images. The algorithm is based on the calculation of an autocorrelation function and provides three different parameters: the width of the autocorrelation function, related to the spatial resolution; the brightness, related to the image contrast; the relative noise variance, related to the signal-to-noise ratio of the image. We use this algorithm to evaluate the quality of stimulated emission depletion (STED) images of DNA replication foci in U937 cells acquired under different imaging conditions. Increasing the STED depletion power improves the resolution but may reduce the image contrast. Increasing the number of line averages improves the signal-to-noise ratio but facilitates the onset of photobleaching and subsequent reduction of the image contrast. Finally, we evaluate the performances of two different separation of photons by lifetime tuning (SPLIT) approaches: the method of tunable STED depletion power and the commercially available Leica Tau-STED. We find that SPLIT provides an efficient way to improve the resolution and contrast in STED microscopy.

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

scholarly journals Improving Signal to Noise Ratio in Ultra High Field Magnetic Resonance Imaging

2021 ◽  
Author(s):  
Nader Tavaf
Keyword(s):  
Human Brain ◽  
High Field ◽  
Signal To Noise Ratio ◽  
Noise Correlation ◽  
Resonance Imaging ◽  
Mr Images ◽  
Signal To Noise ◽  
Ultra High Field ◽  
Noise Ratio ◽  
Receive Array

Ultra-High Field (UHF) Magnetic Resonance Imaging (MRI) advantages, including higher image resolution, reduced acquisition time via parallel imaging, and better signal-to-noise ratio (SNR) have opened new opportunities for various clinical and research projects, including functional MRI, brain connectivity mapping, and anatomical imaging. The advancement of these UHF MRI performance metrics, especially SNR, was the primary motivation of this thesis. Unaccelerated SNR depends on receive array sensitivity profile, receiver noise correlation and static magnetic field strength. Various receive array decoupling technologies, including overlap/inductive and preamplifier decoupling, were previously utilized to mitigate noise correlation. In this dissertation, I developed a novel self-decoupling principle to isolate elements of a loop-based receive array and demonstrated, via full-wave electromagnetic/circuit co-simulations validated by bench measurements, that the self-decoupling technique provides inter-element isolation on par with overlap decoupling while self-decoupling improves SNR. I then designed and constructed the first self-decoupled 32 and 64 channel receiver arrays for human brain MR imaging at 10.5T / 447MHz. Experimental comparisons of these receive arrays with the industry’s gold-standard 7T 32 channel receiver resulted in 1.81 times and 3.53 times more average SNR using the 10.5T 32 and 64 channel receivers I built, respectively. To further improve the SNR of accelerated MR images, I developed a novel data-driven model using a customized conditional generative adversarial network (GAN) architecture for parallel MR image reconstruction and demonstrated that, when applied to human brain images subsampled with rate of 4, the GAN model results in a peak signal-to-noise ratio (PSNR) of 37.65 compared to GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA)’s PSNR of 33.88.In summary, the works presented in this dissertation improved the SNR available for human brain imaging and provided the experimental realization of the advantages anticipated at 10.5T MRI. The insights from this thesis inform future efforts to build self-decoupled transmit arrays and high density, 128 channel loop-based receive arrays for human brain MRI especially at ultra-high field as well as future studies to utilize deep learning techniques for reconstruction and post-processing of parallel MR images.

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 Enhancing the signal-to-noise ratio and generating contrast for cryo-EM images with convolutional neural networks

IUCrJ ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. 1142-1150
Author(s):  
Eugene Palovcak ◽  
Daniel Asarnow ◽  
Melody G. Campbell ◽  
Zanlin Yu ◽  
Yifan Cheng
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Convolutional Neural Networks ◽  
Signal To Noise Ratio ◽  
Image Contrast ◽  
Signal To Noise ◽  
Pass Filter ◽  
Processing Pipeline ◽  
Low Pass ◽  
Noise Ratio

In cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image-processing pipeline. Classic methods improve low-frequency image contrast experimentally, by imaging with high defocus, or computationally, by applying various types of low-pass filter. These contrast improvements typically come at the expense of the high-frequency SNR, which is suppressed by high-defocus imaging and removed by low-pass filtration. Recently, convolutional neural networks (CNNs) trained to denoise cryo-EM images have produced impressive gains in image contrast, but it is not clear how these algorithms affect the information content of the image. Here, a denoising CNN for cryo-EM images was implemented and a quantitative evaluation of SNR enhancement, induced bias and the effects of denoising on image processing and three-dimensional reconstructions was performed. The study suggests that besides improving the visual contrast of cryo-EM images, the enhanced SNR of denoised images may be used in other parts of the image-processing pipeline, such as classification and 3D alignment. These results lay the groundwork for the use of denoising CNNs in the cryo-EM image-processing pipeline beyond particle picking.

Entropy based Range Optimized Brightness Preserved Histogram-Equalization for Image Contrast Enhancement

2016 ◽  
Vol 6 (1) ◽  
pp. 59-72 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Sankhadip Sen ◽  
Kaustav Sarkar ◽  
Sanjoy Das
Keyword(s):  
Contrast Enhancement ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Image Contrast ◽  
Experimental Results ◽  
Signal To Noise ◽  
Image Histogram ◽  
Image Contrast Enhancement ◽  
Noise Ratio

In this study the over-enhancement problem of traditional Histogram-Equalization (HE) has been removed to some extent by a variant of HE called Range Optimized Entropy based Bi-Histogram Equalization (ROEBHE). In ROEBHE image histogram has been thresholded into two sub-histograms i.e. histograms corresponding to background and foreground. The threshold is calculated by maximizing the sum of the entropy of these two sub-histograms. The range for equalization has been optimized by maximizing the Peak-Signal to Noise ratio (PSNR). The experimental results prove that ROEBHE has prevailed over existing methods and PSNR is a better range optimizer than Absolute Mean Brightness Error (AMBE).

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