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.

Optimalization Image Of Turbo Spin Echo (TSE) With Pre Saturation And Gradient Moment Nulling (GMN) To Reduce Flow Artifact On MRI Cervical

2019 ◽  
Vol 21 (2) ◽  
pp. 57
Author(s):  
Kiki Rohmatul Ula
Keyword(s):  
Spinal Cord ◽  
Signal To Noise Ratio ◽  
Spin Echo ◽  
Region Of Interest ◽  
Spinal Cord Tissue ◽  
Turbo Spin Echo ◽  
Turbo Spin ◽  
Flow Compensation ◽  
Flow Artifact ◽  
Noise Ratio

ABSTRACTThe  research  of  Optimization  Image  of  Turbo  Spin  Echo  (TSE)  with  Pre saturation and Gradient Moment Nulling (GMN) to reduce flow artifact on MRI Cervical has been done. The purpose of this research is to know the effect of pre saturation and gradient moment nulling (GMN) on cervical MRI and determine optimal image to reduce flow artifact. This research used four treatment variations that were without pre saturation and without flow compensation (GMN), with pre saturation, with flow compensation (GMN), and with both pre saturation and flow compensation (GMN) on sequence T2-weighting TSE sagital on cervical MRI. The quantitative analysis done by using Region of Interest (ROI) on MRI image then analyzed signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR). The best effect and image quality obtained by pre saturation and flow compensation (GMN) treatment with SNR value on tissue was 328,7 at vertebra cervical, 278,6 at spinal cord, 366,6 at discus, 596,3 at CSF. While CNR tissue value was 78,6 at vertebra cervical, 257,6 at spinal cord, 274,8 at discus, and336,3 at CSF followed by the decrease of flow artifact in the amount of 160,4. Conclusion shows that the image with both pre saturation and flow compensation (GMN) treatment on T2 TSE sagital can reduce flow artifact signal on the spinal cord tissue. Keywords : Pre saturation, flow compensation, gradient moment nulling (GMN), MRI Cervical.

Optimization of signal-to-noise ratio in calculatedT1 images derived from two spin-echo images

1986 ◽  
Vol 3 (1) ◽  
pp. 63-75 ◽  
Author(s):  
F. S. Prato ◽  
D. J. Drost ◽  
T. Keys ◽  
P. Laxon ◽  
B. Comissiong ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Spin Echo ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals High-Resolution Simultaneous Multi-Slice Accelerated Turbo Spin-Echo Musculoskeletal Imaging: A Head-to-Head Comparison With Routine Turbo Spin-Echo Imaging

2021 ◽  
Vol 12 ◽  
Author(s):  
Feifei Gao ◽  
Zejun Wen ◽  
Shewei Dou ◽  
Xiaojing Kan ◽  
Shufang Wei ◽  
...  
Keyword(s):  
Image Quality ◽  
Knee Joint ◽  
Ankle Joint ◽  
Spin Echo ◽  
Motion Artifacts ◽  
Knee Joints ◽  
Turbo Spin Echo ◽  
Turbo Spin ◽  
Scanning Time ◽  
Noise Ratio

Background/Aim: The turbo spin-echo (TSE) sequence is widely used for musculoskeletal (MSK) imaging; however, its acquisition speed is limited and can be easily affected by motion artifacts. We aimed to evaluate whether the use of a simultaneous multi-slice TSE (SMS-TSE) sequence can accelerate MSK imaging while maintaining image quality when compared with the routine TSE sequence.Methods: We prospectively enrolled 71 patients [mean age, 37.43 ± 12.56 (range, 20–67) years], including 37 men and 34 women, to undergo TSE and SMS sequences. The total scanning times for the wrist, ankle and knee joint with routine sequence were 14.92, 13.97, and 13.48 min, respectively. For the SMS-TSE sequence, they were 7.52, 7.20, and 6.87 min. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), were measured. Three experienced MSK imaging radiologists qualitatively evaluated the image quality of bone texture, cartilage, tendons, ligament, meniscus, and artifact using a 5-point evaluation system, and the diagnostic performance of the SMS-TSE sequences was evaluated.Results: Compared with the routine TSE sequences, the scanning time was lower by 49.60, 48.46, and 49.04% using SMS-TSE sequences for the wrist, ankle, and knee joints, respectively. For the SNR comparison, the SMS-TSE sequences were significantly higher than the routine TSE sequence for wrist (except for Axial-T2WI-FS), ankle, and knee joint MR imaging (all p < 0.05), but no statistical significance was obtained for the CNR measurement (all p > 0.05, except for Sag-PDWI-FS in ankle joint). For the wrist joint, the diagnostic sensitivity, specificity, and accuracy were 88.24, 100, and 92%. For the ankle joint, they were 100, 75, and 93.33%. For the knee joint, they were 87.50, 85.71, and 87.10%.Conclusion: The use of the SMS-TSE sequence in the wrist, ankle, and knee joints can significantly reduce the scanning time and show similar image quality when compared with the routine TSE sequence.

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.

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