scholarly journals DIFFERENCES IMAGE QUALITY OF SIGNAL TO NOISE RATIO (SNR) IN SENSITIVITY ENCODING (SENSE) APPLIED ON MAGNETIC RESONANCE CHOLANGIOPANCREATOGRAPHY (MRCP) EXAMINATION SEQUENCES TSE T2 CORONAL

2018 ◽  
Vol 4 (2) ◽  
pp. 85
Author(s):  
Hengky Saefulloh ◽  
Siti Masrochah ◽  
Fatimah Fatimah
Keyword(s):  
Magnetic Resonance ◽  
Image Quality ◽  
Signal To Noise Ratio ◽  
Magnetic Resonance Cholangiopancreatography ◽  
Signal To Noise ◽  
Sensitivity Encoding ◽  
Noise Ratio

scholarly journals Evaluation Comparison Image Quality of Breath Hold (SSTSE) and Respiratory Triggering (TSE) Technique to the Examination of Magnetic Cholangiopancreatography (MRCP)

2017 ◽  
Vol 1 (2) ◽  
pp. 39
Author(s):  
Nikky Anis Suroiyah ◽  
Risalatul Latifah ◽  
Sri Andreani Utomo
Keyword(s):  
Image Quality ◽  
Pancreatic Duct ◽  
Signal To Noise Ratio ◽  
Magnetic Resonance Cholangiopancreatography ◽  
Breath Hold ◽  
Signal To Noise ◽  
Human Bile ◽  
Noise Ratio ◽  
Respiratory Triggering

Background : Magnetic resonance cholangiopancreatography (MRCP) is MRI examination to visualize to visualize a disorder in part biliary or of the human bile. The artefact in the image MRCP is often happened due to the movement of respiratory system. Purpose :  This study to compare the better image quality between Breath Hold (SSTSE) and Repiratory Triggering (TSE)techinques. Methods : This study used observational analytic study with prospective approach. 16 samples were examined with two techniques SSTSE and TSE respectively in order to get 3D MRCP image. The Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR) is measured to evaluate the image quality. Result: The SSTSE technique only has a higher SNR in gallbladder meanwhile the TSE technique has a higher SNR in pancreatic duct. The higher CNR is got using TSE technique. Conclusion :  The TSE showed the higher SNR in pancreatic duct and the higher CNR is got with SSTSE technique. The recommendation technique for evaluating pancreatic duct is respiratory triggering.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

The Influence of Alteration of kVp and mAs towards the Image Quality of Acrylic Using CBCT

2019 ◽  
Vol 829 ◽  
pp. 252-257
Author(s):  
Azhari ◽  
Yohanes Hutasoit ◽  
Freddy Haryanto
Keyword(s):  
Image Quality ◽  
Signal To Noise Ratio ◽  
Region Of Interest ◽  
Signal To Noise ◽  
Bite Block ◽  
Significant Difference ◽  
Noise Ratio ◽  
Post Hoc ◽  
Radiograph Image

CBCT is a modernized technology in producing radiograph image on dentistry. The image quality excellence is very important for clinicians to interpret the image, so the result of diagnosis produced becoming more accurate, appropriate, thus minimizing the working time. This research was aimed to assess the image quality using the blank acrylic phantom polymethylmethacrylate (PMMA) (C­5H8O2)n in the density of 1.185 g/cm3 for evaluating the homogeneity and uniformity of the image produced. Acrylic phantom was supported with a tripod and laid down on the chin rest of the CBCT device, then the phantom was fixed, and the edge of the phantom was touched by the bite block. Furthermore, the exposure of the X-ray was executed toward the acrylic phantom with various kVp and mAs, from 80 until 90, with the range of 5 kV and the variation of mA was 3, 5, and 7 mA respectively. The time exposure was kept constant for 25 seconds. The samples were taken from CBCT acrylic images, then as much as 5 ROIs (Region of Interest) was chosen to be analyzed. The ROIs determination was analyzed by using the ImageJ® software for recognizing the influence of kVp and mAs towards the image uniformity, noise and SNR. The lowest kVp and mAs had the result of uniformity value, homogeneity and signal to noise ratio of 11.22; 40.35; and 5.96 respectively. Meanwhile, the highest kVp and mAs had uniformity value, homogeneity and signal to noise ratio of 16.96; 26.20; and 5.95 respectively. There were significant differences between the image uniformity and homogeneity on the lowest kVp and mAs compared to the highest kVp and mAs, as analyzed with the ANOVA statistics analysis continued with the t-student post-hoc test with α = 0.05. However, there was no significant difference in SNR as analyzed with the ANOVA statistic analysis. The usage of the higher kVp and mAs caused the improvement of the image homogeneity and uniformity compared to the lower kVp and mAs.

Quality of Computed Tomography Pulmonary Embolism and Aortic Phase Images Acquired with Power Injection via an Arm Port

2017 ◽  
Vol 22 (2) ◽  
pp. 88-92
Author(s):  
Ryan Verity ◽  
David Leswick ◽  
Brent Burbridge ◽  
Rhonda Bryce ◽  
Hyun Lim
Keyword(s):  
Computed Tomography ◽  
Pulmonary Embolism ◽  
Image Quality ◽  
Thin Section ◽  
Signal To Noise Ratio ◽  
Contrast Injection ◽  
Signal To Noise ◽  
Streak Artifact ◽  
Noise Ratio

Abstract Background: The safety of power-injectable implanted arm ports is well established, but there is insufficient data to conclude that image quality of computed tomography resulting from contrast introduced via the port is of equal quality to images derived from contrast introduced via traditional peripheral access. The objective of this study was to determine whether the image quality of computed tomography pulmonary embolism and computed tomography aorta studies would differ when injecting contrast via an implanted arm port vs a peripheral intravenous site. We hypothesized that injecting via an implanted arm port would produce better-quality images, the result of more appropriate timing and less streak artifact. Methods: Scans from a provincial database search for patients who underwent a computed tomography pulmonary embolism or aorta study with contrast injection via the implanted arm port and thin section images available, were reviewed (pulmonary embolism studies n = 3, aorta studies n = 3). Only a limited number of patients were available for review because there are currently few patients with these ports in place and we limited evaluation to thin section images. Comparison was made with 6 control patients who did not have a port and had received a peripheral arm intravenous contrast injection for these study types. Objective measurements included signal-to-noise ratio and contrast-to-noise ratio of the pulmonary arteries (4 sites) and aorta (2 sites) as appropriate for scan type. Subjective analysis of image quality was performed by 2 radiologists. Results: Although sample size was limited, the implanted arm port group had similar or higher mean signal-to-noise ratio and contrast-to-noise ratio values at all sites. Subjective assessments showed the implanted arm port group to have similar or better opacification and diagnostic confidence; similar or less streak artifact was also observed at each of the sites. Conclusions: These exploratory results suggest that studies with implanted arm port injection can generate high-quality images on both objective and subjective assessment, similar to, or possibly better than, images generated from usual peripheral intravenous access for contrast injection.

Determining the Quality of Compression in High Resolution Satellite Images Using Different Compression Methods

2015 ◽  
Vol 20 ◽  
pp. 202-217
Author(s):  
S. Sanjith ◽  
R. Ganesan
Keyword(s):  
High Resolution ◽  
Image Quality ◽  
Satellite Images ◽  
Signal To Noise Ratio ◽  
Joint Photographic Expert Group ◽  
Expert Group ◽  
Signal To Noise ◽  
High Resolution Satellite Images ◽  
Noise Ratio

Measuring the quality of image is very complex and hard process since the opinion of the humans are affected by physical and psychological parameters. So many techniques are invented and proposed for image quality analysis but none of the methods suits best for it. Assessment of image quality plays an important role in image processing. In this paper we present the experimental results by comparing the quality of different satellite images (ALOS, RapidEye, SPOT4, SPOT5, SPOT6, SPOTMap) after compression using four different compression methods namely Joint Photographic Expert Group (JPEG), Embedded Zero tree Wavelet (EZW), Set Partitioning in Hierarchical Tree (SPIHT), Joint Photographic Expert Group – 2000 (JPEG 2000). The Mean Square Error (MSE), Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR) values are calculated to determine the quality of the high resolution satellite images after compression.

scholarly journals Applicability of deep learning-based reconstruction trained by brain and knee 3T MRI to lumbar 1.5T MRI

2021 ◽  
Vol 10 (6) ◽  
pp. 205846012110239
Author(s):  
Nobuo Kashiwagi ◽  
Hisashi Tanaka ◽  
Yuichi Yamashita ◽  
Hiroto Takahashi ◽  
Yoshimori Kassai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Image Quality ◽  
Signal To Noise Ratio ◽  
Magnetic Resonance Images ◽  
Reconstruction Method ◽  
Signal To Noise ◽  
Scanning Time ◽  
Noise Ratio ◽  
Weighted Sequences

Background Several deep learning-based methods have been proposed for addressing the long scanning time of magnetic resonance imaging. Most are trained using brain 3T magnetic resonance images, but is unclear whether performance is affected when applying these methods to different anatomical sites and at different field strengths. Purpose To validate the denoising performance of deep learning-based reconstruction method trained by brain and knee 3T magnetic resonance images when applied to lumbar 1.5T magnetic resonance images. Material and Methods Using a 1.5T scanner, we obtained lumber T2-weighted sequences in 10 volunteers using three different scanning times: 228 s (standard), 119 s (double-fast), and 68 s (triple-fast). We compared the images obtained by the standard sequence with those obtained by the deep learning-based reconstruction-applied faster sequences. Results Signal-to-noise ratio values were significantly higher for deep learning-based reconstruction-double-fast than for standard and did not differ significantly between deep learning-based reconstruction-triple-fast and standard. Contrast-to-noise ratio values also did not differ significantly between deep learning-based reconstruction-triple-fast and standard. Qualitative scores for perceived signal-to-noise ratio and overall image quality were significantly higher for deep learning-based reconstruction-double fast and deep learning-based reconstruction-triple-fast than for standard. Average scores for sharpness, contrast, and structure visibility were equal to or higher for deep learning-based reconstruction-double-fast and deep learning-based reconstruction-triple-fast than for standard, but the differences were not statistically significant. The average scores for artifact were lower for deep learning-based reconstruction-double-fast and deep learning-based reconstruction-triple-fast than for standard, but the differences were not statistically significant. Conclusion The deep learning-based reconstruction method trained by 3T brain and knee images may reduce the scanning time of 1.5T lumbar magnetic resonance images by one-third without sacrificing image quality.

scholarly journals Faktor Eksposi terhadap Kualitas Citra Radiografi dan Dosis Pasien Menggunakan Parameter Penilaian Signal to Noise Ratio (SNR) pada Pemeriksaan Thorax Posteroanterior dengan Menggunakan Pesawat Computed Radiografi

2021 ◽  
Vol 7 (1) ◽  
pp. 15-18
Author(s):  
Surdiyah Asriningrum ◽  
Khaerul Ansory ◽  
Putra Tri Hasan
Keyword(s):  
Image Quality ◽  
Digital Image ◽  
Computed Radiography ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Measurement Results ◽  
Noise Ratio ◽  
Dose Radiation ◽  
Average Body

Background: The research was analyzing digital image quality and estimation dose patient by using  Signal to Noise Ratio (SNR) on Computed Radiography. SNR can be used for analyzing digital image spatial resolution and estimation dose accurately. The aims of this study to determine the influence of exposure factors on image quality and estimation dose patient.Methods: This type of research is a quantitative method with an experimental study. Direct experiments in August 2020 assessment with a sample of 9 adults posteroanterior chest photo with the average age of 20-50 years old with an average body weight of 50-69 kilograms. Results: The measurement results showed that the digital images will be analyzed by SNR, so it can be determined the optimum exposed factor of the highest SNR value and dose radiation. From the analysis, the highest SNR value at 121 kV, current 1 mAs, the lower dose radiation at 121 kV, current 0,9 mAs.Conclusions: There was an influence variation of an exposed factor on the quality of the image and dose to the patient.

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

2019 ◽  
Vol 12 (2) ◽  
pp. 138-151
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Perceptual Evaluation ◽  
Lifting Wavelet ◽  
Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

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