Statistical Approach of Measurement of Signal to Noise Ratio in According to Change Pulse Sequence on Brain MRI Meningioma and Cyst Images

2016 ◽  
Vol 39 (3) ◽  
pp. 345-352
Author(s):  
Eul-Kyu Lee ◽  
◽  
Kwan-Woo Choi ◽  
Hoi-Woun Jeong ◽  
Seo-Goo Jang ◽  
...  
Keyword(s):  
Statistical Approach ◽  
Pulse Sequence ◽  
Signal To Noise Ratio ◽  
Brain Mri ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals NOISE STABILITY OF SIGNAL RECEPTION ALGORITHMS WITH MULTIPULSE PULSE-POSITION MODULATION

2018 ◽  
Vol 42 (1) ◽  
pp. 167-174 ◽  
Author(s):  
V. I. Parfenov ◽  
D. Y. Golovanov
Keyword(s):  
Communication Systems ◽  
Pulse Sequence ◽  
Signal To Noise Ratio ◽  
Point Of View ◽  
Estimation Accuracy ◽  
Pulse Position Modulation ◽  
Signal To Noise ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Noise Ratio

An algorithm for estimating time positions and amplitudes of a periodic pulse sequence from a small number of samples was proposed. The number of these samples was determined only by the number of pulses. The performance of this algorithm was considered on the assumption that the spectrum of the original signal is limited with an ideal low-pass filter or the Nyquist filter, and conditions for the conversion from one filter to the other were determined. The efficiency of the proposed algorithm was investigated through analyzing in which way the dispersion of estimates of time positions and amplitudes depends on the signal-to-noise ratio and on the number of pulses in the sequence. It was shown that, from this point of view, the efficiency of the algorithm decreases with increasing number of sequence pulses. Besides, the efficiency of the proposed algorithm decreases with decreasing signal-to-noise ratio.It was found that, unlike the classical maximum likelihood algorithm, the proposed algorithm does not require a search for the maximum of a multivariable function, meanwhile characteristics of the estimates are practically the same for both these methods. Also, it was shown that the estimation accuracy of the proposed algorithm can be increased by an insignificant increase in the number of signal samples.The results obtained may be used in the practical design of laser communication systems, in which the multipulse pulse-position modulation is used for message transmission. 

Noise Removal from Brain MRI Images Using Adaptive Bayesian Shrinkage

2020 ◽  
Vol 17 (4) ◽  
pp. 1818-1825
Author(s):  
S. Josephine ◽  
S. Murugan
Keyword(s):  
Signal To Noise Ratio ◽  
Brain Mri ◽  
Noise Removal ◽  
Wavelet Thresholding ◽  
Signal To Noise ◽  
Pixel Intensity ◽  
Noisy Pixel ◽  
Shrinkage Method ◽  
Bayesian Shrinkage ◽  
Noise Ratio

In MR machine, surface coils, especially phased-arrays are used extensively for acquiring MR images with high spatial resolution. The signal intensities on images acquired using these coils have a non-uniform map due to coil sensitivity profile. Although these smooth intensity variations have little impact on visual diagnosis, they become critical issues when quantitative information is needed from the images. Sometimes, medical images are captured by low signal to noise ratio (SNR). The low SNR makes it difficult to detect anatomical structures because tissue characterization fails on those images. Hence, denoising are essential processes before further processing or analysis will be conducted. They found that the noise in MR image is of Rician distribution. Hence, general filters cannot be used to remove these types of noises. The linear spatial filtering technique blurs the object boundaries and degrades the sharp details. The existing works proved that Wavelet based works eliminates the noise coefficient that called wavelet thresholding. Wavelet thresholding estimates the noise level from high frequency content and estimates the threshold value by comparing the estimated noisy wavelet coefficient with other wavelet coefficients and eliminate the noisy pixel intensity value. Bayesian Shrinkage rule is one of the widely used methods. It uses for Gaussian type of noise, the proposed method introduced some adaptive technique in Bayesian Shrinkage method to remove Rician type of noises from MRI images. The results were verified using quantitative parameters such as Peak Signal to Noise Ratio (PSNR). The proposed Adaptive Bayesian Shrinkage Method (ABSM) outperformed existing methods.

scholarly journals Analisis Pembobotan T2 Turbo Spin Echo (TSE) brain MRI Potongan Axial dengan Penggunaan Sensitivity Encoding (SENSE) dan Tanpa Penggunaan Sense : Evaluasi pada Signal to Noise Ratio (SNR) dan Scan Time

2016 ◽  
Vol 2 (2) ◽  
pp. 148-153
Author(s):  
Fani Susanto ◽  
A. Gunawan Santoso ◽  
Bagus Abimanyu
Keyword(s):  
Signal To Noise Ratio ◽  
Spin Echo ◽  
Brain Mri ◽  
Statistical Test ◽  
Turbo Spin Echo ◽  
Signal To Noise ◽  
Turbo Spin ◽  
Sensitivity Encoding ◽  
Scan Time ◽  
Noise Ratio

Background: On examination brain MRI often finds non-cooperative patients, requiring rapid acquisition techniques. The parallel imaging sensitivity encoding (SENSE) technique utilizes spatial RF coated phased array information to reduce acquisition time by reducing the K space sampling line to produce good quality and spatial resolution, but has a limitation of signal-to-noise ratio (SNR) reduction. SENSE is used with MRI sequence pulses one of them turbo spin echo (TSE). The purpose of this study was to determine the difference of SNR and scan time on TSE T2 weighting brain MRI axial slices between use SENSE and without SENSE.Methods: This research is quantitative study with experimental approach. The data were collected from May to June 2016 at the Radiology Installation of Premier Bintaro Hospital by calculating the SNR through the software for the region of interest (ROI) and calculating the scan time through the scan timer on the workstation monitor. Data analysis was done by statistical test with SPPS 16 application using paired T-test and descriptiveResults: From the result of statistical test, it is known that SNR at TSE T2 weighting between with and without SENSE is obtained p-value 0,000 (p 0, 05). This is because the encoding of the both image are different, On TSE T2 weighting image without SENSE there is the use 1800 pulses approaching the effective TE so the shallow gradient produces maximum echo, while on TSE T2 weighting with SENSE there is a reduction of phase encoding row in K space and the presence of g-factor causes the SNR to decrease. From descriptive analysis result, is known that scan time on TSE T2 weighting between with and without SENSE usage is obtained by reduction of scan time for 1 minute 24 seconds (49, 01%). This is because the acquisition technique between the both image are different, on the TSE T2 weighting  without SENSE there is ETL in charging K space, whereas on the TSE T2 Weighting  with  SENSE there is R-factor causing the sampling not to fill all K space so that scanning time is reduced.Conclusion: There are SNR and scan time differences on TSE T2 weighting brain MRI of the axial slices with SENSE and without SENSE usage.

scholarly journals Fast and High-Resolution Neonatal Brain MRI Through Super-Resolution Reconstruction From Acquisitions With Variable Slice Selection Direction

2021 ◽  
Vol 15 ◽  
Author(s):  
Yao Sui ◽  
Onur Afacan ◽  
Ali Gholipour ◽  
Simon K. Warfield
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Brain Mri ◽  
Super Resolution ◽  
Neonatal Brain ◽  
Signal To Noise ◽  
High Quality ◽  
Noise Ratio

The brain of neonates is small in comparison to adults. Imaging at typical resolutions such as one cubic mm incurs more partial voluming artifacts in a neonate than in an adult. The interpretation and analysis of MRI of the neonatal brain benefit from a reduction in partial volume averaging that can be achieved with high spatial resolution. Unfortunately, direct acquisition of high spatial resolution MRI is slow, which increases the potential for motion artifact, and suffers from reduced signal-to-noise ratio. The purpose of this study is thus that using super-resolution reconstruction in conjunction with fast imaging protocols to construct neonatal brain MRI images at a suitable signal-to-noise ratio and with higher spatial resolution than can be practically obtained by direct Fourier encoding. We achieved high quality brain MRI at a spatial resolution of isotropic 0.4 mm with 6 min of imaging time, using super-resolution reconstruction from three short duration scans with variable directions of slice selection. Motion compensation was achieved by aligning the three short duration scans together. We applied this technique to 20 newborns and assessed the quality of the images we reconstructed. Experiments show that our approach to super-resolution reconstruction achieved considerable improvement in spatial resolution and signal-to-noise ratio, while, in parallel, substantially reduced scan times, as compared to direct high-resolution acquisitions. The experimental results demonstrate that our approach allowed for fast and high-quality neonatal brain MRI for both scientific research and clinical studies.

Fast Padé transform for increasing the signal to noise ratio of spectra provided by STEAM pulse sequence

2019 ◽  
Vol 27 (2) ◽  
pp. 167-172 ◽  
Author(s):  
Mahdi Saeedi-Moghadam ◽  
Majid Pouladian ◽  
Reza Faghihi ◽  
Mehrzad Lotfi
Keyword(s):  
Pulse Sequence ◽  
Signal To Noise Ratio ◽  
Fast Padé Transform ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Analisis TSE Factor Terhadap Signal to Noise Ratio dan Contrast to Noise Ratio pada Pembobotan T2 Turbo Spin Echo Potongan Axial MRI Brain

2015 ◽  
Vol 3 (2) ◽  
pp. 271-276 ◽  
Author(s):  
Novelsa Chintya Prabawati ◽  
Siti Masrochah ◽  
Sri Mulyati
Keyword(s):  
White Matter ◽  
Linear Regression ◽  
Grey Matter ◽  
Signal To Noise Ratio ◽  
Brain Mri ◽  
Signal To Noise ◽  
Mri Examination ◽  
Regression Test ◽  
Coefficient Corrélation ◽  
Noise Ratio

Background: TSE factor is parameters that affect Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR). TSE factor for brain MRI examination is a long TSE factor. There are differences when using TSE factor. At the theory, the brain MRI examination is using TSE factor ≥16 while at Siloam  Surabaya  Hospital was using TSE factor 14. The writer ever seen some noises at brain MRI image therefore the radiographer doing modification of TSE factor. The purpose of this research are to determine the influence of modification in the TSE factor value against SNR and CNR and to define the SNR and CNR optimum from that.Methods: This research is a quantitative study with an experimental approach. This research was done by MRI Philips Achieva 1,5 T with 10 modification TSE factor (8, 10, 12, 14, 16, 18, 20, 22, 24 and 26). SNR and CNR obtained by measurement of ROI in the grey matter, white matter and CSF with the result an average signal and compared with the average standard deviation of the background image. Data was analyzed by linear regression test to know the influence of TSE factor against SNR and CNR and data was analyzed by descriptive test mean rank to obtain the optimum TSE factor value.Result: The result showed that there was the inluence of TSE factor to SNR and CNR at T2W TSE axial brain. There was a significant correlation between TSE factor with all of area SNR and CNR with coefficient correlation of SNR grey matter r=0,591, with coefficient correlation of SNR white matter r=0,604, with coefficient correlation of SNR CSF r=0,687, with coefficient correlation of CNR CSF–grey matter r=0,690, with coefficient correlation of CNR CSF-white matter r=0,658. The significant value of linear regression test is (0,000*) p value (0,05). TSE factor optimum value at T2W TSE axial brain was TSE factor value 10 for SNR with mean rank SNR 45,05 and TSE factor value 8 for CNR with mean rank CNR 35,43.Conclusion: There was the influence of TSE factor to SNR and CNR at T2W TSE axial brain. TSE factor optimum value in brain MRI T2W TSE axial is 10 to SNR and TSE factor 8 to CNR.

scholarly journals Impact of pulse sequence, analysis method, and signal to noise ratio on the accuracy of intervertebral disc T 2 measurement

JOR Spine ◽  
2020 ◽  
Vol 3 (3) ◽  
Author(s):  
Kyle D. Meadows ◽  
Curtis L. Johnson ◽  
John M. Peloquin ◽  
Richard G. Spencer ◽  
Edward J. Vresilovic ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
Intervertebral Disc ◽  
Pulse Sequence ◽  
Signal To Noise Ratio ◽  
Analysis Method ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Signal-to-noise ratio uniformity and stability of agar gel phantom with iron (III) oxide as relaxation modifier

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
W. X. Er ◽  
W. J. Lim ◽  
Y. Dwihapsari ◽  
M. N. A. Awang ◽  
A. N. Yusoff
Keyword(s):  
Pulse Sequence ◽  
Signal To Noise Ratio ◽  
Spin Echo ◽  
T2 Relaxation ◽  
Signal To Noise ◽  
Agar Gel ◽  
T1 And T2 ◽  
Noise Ratio ◽  
Imagej Software ◽  
Echo Pulse

Abstract Background Agar has been commonly used as one of the materials to fabricate magnetic resonance imaging phantoms in the past few decades. In this study, eleven agar gel phantoms with different iron (III) oxide (Fe2O3) masses were prepared. This study was aimed to evaluate the signal-to-noise ratio (SNR) uniformity and stability of agar gel phantoms with and without the addition of Fe2O3 at two different time points (TPs). Fe2O3 powder was used as a relaxation modifier to manipulate and produce various SNR, T1 and T2 values. These phantoms were scanned using turbo spin echo pulse sequence to produce T1- and T2-measurement images. The SNR was then computed by plotting 1, 3 and 25 regions of interest on the images using ImageJ software. The T1 and T2 relaxation equations were then fitted to the experimental results of SNR versus TR and SNR versus TE curves for the determination of saturation (SNRo), T1 and T2 values. Results The results demonstrated that the agar gel phantoms were able to maintain SNR uniformity but not SNR stability after 4 weeks of phantom preparation. The change in the water content and microstructure of the phantoms have no significant effect on T2 relaxation but on T1 relaxation. The T1 and T2 of the agar gel phantoms were minimally affected although there was a systemic increase in the content of the Fe2O3 powder. Conclusions It can be concluded that the agar gel phantoms exhibited the characteristics of SNR uniformity, but they showed instability of SNR at TP2. The Fe2O3 in powder form is not an effective relaxation modifier to reduce the T1 and T2 when it is introduced into the agar gel phantoms. Dissolved nanosized particles should be the focus of future studies.

Sign in / Sign up

Export Citation Format

Share Document