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

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.

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

International Journal of Computer Assisted Radiology and Surgery ◽

10.1007/s11548-021-02433-x ◽

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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A comparison of two methods for measuring the signal to noise ratio on MR images

Physics in Medicine and Biology ◽

10.1088/0031-9155/44/12/403 ◽

1999 ◽

Vol 44 (12) ◽

pp. N261-N264 ◽

Cited By ~ 174

Author(s):

M J Firbank ◽

A Coulthard ◽

R M Harrison ◽

E D Williams

Keyword(s):

Signal To Noise Ratio ◽

Mr Images ◽

Signal To Noise ◽

Noise Ratio

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The Influence of Signal to Noise Ratio on the Pharmacokinetic Analysis in DCE-MRI Studies

Frontiers in Biomedical Technologies ◽

10.18502/fbt.v6i4.2212 ◽

2020 ◽

Author(s):

Azimeh NV Dehkordi ◽

Saeideh Koohestani

Keyword(s):

Model Selection ◽

Pharmacokinetic Model ◽

Signal To Noise Ratio ◽

Series Data ◽

Mr Images ◽

Signal To Noise ◽

Dce Mri ◽

Estimated Parameters ◽

Mri Studies ◽

Noise Ratio

Purpose: Recruiting the Pharmacokinetic (PK) parameters estimated from non-invasive methods such as Dynamic Contrast Enhanced MRI (DCE-MRI) to evaluate or plan treatment procedure is widely investigated in clinical practices. Interpretation of the DCE-MRI data is highly dependent to precision and accuracy of the estimated parameters. One of the most effective factors on the DCE-MR images and on the contrast concentration profile is the Signal to Noise Ratio (SNR). This work focuses on the analytical evaluation of the noise effect on accuracy of the estimated PK parameters in DCE-MRI studies. Materials and Methods: Tofts model as a popular pharmacokinetic model and model selection technique was used to simulate 3470 time curves of contrast concentration. Maximum likelihood estimator as a minimum variance unbiased estimator was recruited to estimate the PK parameters. Eleven levels of signal to noise ratios (SNR= 5, 8, 10, 13, 15, 20, 25, 30, 35, 50, Noiseless) were added to the simulated CA concentration profiles. The PK parameters were estimated for 11 series data and then Mean Percentage Error (MPE) was calculated for estimated parameters. Results: The results indicate that the most sensitive parameter to the SNR of the DCE-MR images is inverse transfer constant. A SNR greater than 25 was found to ensure a reasonable error (MPE <5%) in all models parameters. Conclusion: Clinical decision based on the DCE-MRI data analysis and estimated PK parameters needs a good image quality (SNR>25), an accurate and robust estimator and correct pharmacokinetic model selection

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Determination of the Best Emulsion for the Microscopy of Crystals

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100096886 ◽

1981 ◽

Vol 39 ◽

pp. 32-33

Author(s):

David A. Grano ◽

Kenneth H. Downing

Keyword(s):

Spatial Frequency ◽

Information Transfer ◽

Signal To Noise Ratio ◽

Experimental Situation ◽

Photographic Emulsion ◽

Modulation Transfer ◽

Signal To Noise ◽

Frequency Space ◽

Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

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Experiments in Bright-Field Imaging with Hollow-Cone Illumination

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100097703 ◽

1981 ◽

Vol 39 ◽

pp. 226-227

Author(s):

W. Kunath ◽

K. Weiss ◽

E. Zeitler

Keyword(s):

Signal To Noise Ratio ◽

Carbon Support ◽

Electronic System ◽

Optic Axis ◽

Hollow Cone ◽

Signal To Noise ◽

Bright Field ◽

Noise Ratio ◽

Single Field ◽

Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

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Information capacity and bandwidth limitations in the SEM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100152392 ◽

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 :

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Speech Reception in the Presence of Classroom Noise

Language Speech and Hearing Services in Schools ◽

10.1044/0161-1461.1004.221 ◽

1979 ◽

Vol 10 (4) ◽

pp. 221-230 ◽

Cited By ~ 2

Author(s):

Veronica Smyth

Keyword(s):

Signal To Noise Ratio ◽

Learning Processes ◽

Speech Discrimination ◽

Signal To Noise ◽

Speech Reception ◽

Monosyllabic Words ◽

Noise Ratio

Three hundred children from five to 12 years of age were required to discriminate simple, familiar, monosyllabic words under two conditions: 1) quiet, and 2) in the presence of background classroom noise. Of the sample, 45.3% made errors in speech discrimination in the presence of background classroom noise. The effect was most marked in children younger than seven years six months. The results are discussed considering the signal-to-noise ratio and the possible effects of unwanted classroom noise on learning processes.

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Quantifying the Effects of Background Speech Babble on Preschool Children's Novel Word Learning in a Multisession Paradigm: A Preliminary Study

Journal of Speech Language and Hearing Research ◽

10.1044/2019_jslhr-h-19-0083 ◽

2020 ◽

Vol 63 (1) ◽

pp. 345-356

Author(s):

Meital Avivi-Reich ◽

Megan Y. Roberts ◽

Tina M. Grieco-Calub

Keyword(s):

Word Learning ◽

Signal To Noise Ratio ◽

Preschool Age ◽

Exposure Condition ◽

Signal To Noise ◽

Verbal Recall ◽

Referent Selection ◽

Main Effect ◽

Noise Ratio ◽

Novel Word Learning

Purpose This study tested the effects of background speech babble on novel word learning in preschool children with a multisession paradigm. Method Eight 3-year-old children were exposed to a total of 8 novel word–object pairs across 2 story books presented digitally. Each story contained 4 novel consonant–vowel–consonant nonwords. Children were exposed to both stories, one in quiet and one in the presence of 4-talker babble presented at 0-dB signal-to-noise ratio. After each story, children's learning was tested with a referent selection task and a verbal recall (naming) task. Children were exposed to and tested on the novel word–object pairs on 5 separate days within a 2-week span. Results A significant main effect of session was found for both referent selection and verbal recall. There was also a significant main effect of exposure condition on referent selection performance, with more referents correctly selected for word–object pairs that were presented in quiet compared to pairs presented in speech babble. Finally, children's verbal recall of novel words was statistically better than baseline performance (i.e., 0%) on Sessions 3–5 for words exposed in quiet, but only on Session 5 for words exposed in speech babble. Conclusions These findings suggest that background speech babble at 0-dB signal-to-noise ratio disrupts novel word learning in preschool-age children. As a result, children may need more time and more exposures of a novel word before they can recognize or verbally recall it.

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