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.

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 Automated segmentation of thick confocal microscopy 3D images for the measurement of white matter volumes in zebrafish brains

2020 ◽  
Vol 4 (1) ◽  
pp. 31-45 ◽  
Author(s):  
Sylvain Lempereur ◽  
Arnim Jenett ◽  
Elodie Machado ◽  
Ignacio Arganda-Carreras ◽  
Matthieu Simion ◽  
...  
Keyword(s):  
White Matter ◽  
Signal To Noise Ratio ◽  
Light Attenuation ◽  
Automated Image Analysis ◽  
High Signal ◽  
Signal To Noise ◽  
Tissue Clearing ◽  
Brain White Matter ◽  
Noise Ratio ◽  
The Brain

AbstractTissue clearing methods have boosted the microscopic observations of thick samples such as whole-mount mouse or zebrafish. Even with the best tissue clearing methods, specimens are not completely transparent and light attenuation increases with depth, reducing signal output and signal-to-noise ratio. In addition, since tissue clearing and microscopic acquisition techniques have become faster, automated image analysis is now an issue. In this context, mounting specimens at large scale often leads to imperfectly aligned or oriented samples, which makes relying on predefined, sample-independent parameters to correct signal attenuation impossible.Here, we propose a sample-dependent method for contrast correction. It relies on segmenting the sample, and estimating sample depth isosurfaces that serve as reference for the correction. We segment the brain white matter of zebrafish larvae. We show that this correction allows a better stitching of opposite sides of each larva, in order to image the entire larva with a high signal-to-noise ratio throughout. We also show that our proposed contrast correction method makes it possible to better recognize the deep structures of the brain by comparing manual vs. automated segmentations. This is expected to improve image observations and analyses in high-content methods where signal loss in the samples is significant.

scholarly journals Analisis Variasi Time Repetition (TR) terhadap Signal to Noise Ratio dan Contrast to Noise Ratio pada Pemeriksaan MRI Cervical T2 Weighted Fast Spin Echo (FSE) Potongan Sagital

2016 ◽  
Vol 2 (1) ◽  
pp. 119-123
Author(s):  
Rini Indrati ◽  
Heriansyah Heriansyah ◽  
Wakhrudin Wakhrudin
Keyword(s):  
Signal To Noise Ratio ◽  
Spin Echo ◽  
Fast Spin Echo ◽  
Mean Deviation ◽  
P Value ◽  
Signal To Noise ◽  
Mri Examination ◽  
Spin Echo Sequence ◽  
Noise Ratio ◽  
Fast Spin

Background: Time Repetition (TR) is one parameter that can affect the value of Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR). The purpose of this research is to know the effect of variation of TR value on SNR and CNR on cervical MRI examination with Sagital T2 Weighted Fast Spin Echo sequence and to know the most optimal TR value from the variation of TR value to SNR and CNR on cervical MRI examination with Sagital T2 Weighted Fast Spin Echo.Methods: The type of this study was experimental study. The study was conducted using MRI 1.5 Tesla at Kasih Ibu Denpasar Hospital. Data were 40 MRI cervical images of sagital Fast Spin Echo from 10 volunteers with four variations of TR (2500 ms, 3000 ms, 3500 ms, and 4000 ms). The SNR and CNR values are measured by identifying the Region of Interest (ROI) in the corpus, discus, cerebro spinal fluid (CSF), and medula spinalis regions to obtain the average signals and compared with the mean deviation of the background. Data was analyzed by regression test to know the influence and by Anova test.Results: The result of the research showed that there was the influence of TR value to SNR and CNR of MRI Cervical Sagital T2 FSE. There was a strong correlation between the variation of TR values with SNR and CNR Cervical with p-value 0.05, the optimal TR value obtained in Cervical Sagital T2 FSE anatomical image on MRI 1,5 Tesla modality was 3500 ms.Conclusion: Time Repetition affected the signal to noise ratio and contrast to noise ratio. TR 3500 ms produced the most optimal cervical MRI image quality.

Correcting Fluctuating Baselines and Spectral Overlap with Genetic Regression

1997 ◽  
Vol 51 (1) ◽  
pp. 92-100 ◽  
Author(s):  
Rajesh P. Paradkar ◽  
Ronald R. Williams
Keyword(s):  
Linear Regression ◽  
Signal To Noise Ratio ◽  
Optimization Technique ◽  
Synthetic Data ◽  
Background Correction ◽  
Effective Wavelength ◽  
Signal To Noise ◽  
Spectral Overlap ◽  
Noise Ratio ◽  
Genetic Regression

The application of a new algorithm, known as genetic regression (GR), to calibration problems with spectra containing complex fluctuating baselines is illustrated with the use of synthetic data. The ability of the algorithm to automatically compensate for the presence of linear and polynomial (quadratic and cubic) baselines in the presence of complex spectral overlap is investigated along with the effect of noise. GR is unique in that it provides an effective wavelength optimization technique by sorting through the spectr al data and selecting and appropriately combining wavelengths that compensate for structured baseline and spectral overlap. The results obtained with GR are compared with those obtained with background-corrected linear regression. GR is shown to give much better results and, in constrast to traditional background correction, is much faster and can compensate for the presence of both structured baseline and complex spectral overlap simultaneously. The results of a noise study show that the method works at low signal-to-noise ratio (SNR) and that the error in the final result is a function of the noise.

scholarly journals Pengaruh Variasi Tegangan Tabung Sinar-X terhadap Signal to Noise Ratio (SNR) dengan Penerapan Anode Heel Effect menggunakan Stepwedge

2020 ◽  
Vol 22 (1) ◽  
pp. 20
Author(s):  
Puja Satwika Luh Gede ◽  
Ni Nyoman Ratini ◽  
Maghfirotul Iffah
Keyword(s):  
Signal To Noise Ratio ◽  
Test Results ◽  
Signal To Noise ◽  
Tube Voltage ◽  
X Ray ◽  
Voltage Variation ◽  
Regression Test ◽  
Noise Ratio ◽  
Heel Effect ◽  
Simple Regression

It has been conducted research to determine the effect of X-ray tube voltage variation (kV) on Signal to Noise Ratio (SNR) values by applying the Anode Heel Effect using stepwedge with the addition of 1.5 mm thickness each step. The stepwedge used was 1.5-31.5 mm. The X-ray tube voltage variations used were 40, 50, 60, 70, 80, and 90 kV. Analysis of the effect of X-ray tube voltage variation on SNR values is determined using IMB SPSS Statistics 26 with a simple regression test. The test results showed that variations in X-ray tube voltage affect SNR values, where the greater the variation in X-ray tube voltage then the SNR value would get smaller. The optimal SNR value of 72.685 was obtained at a tube tension of 40 kV and a stepwedge thickness of 27.0 mm.

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