scholarly journals Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yanqiu Zeng ◽  
Baocan Zhang ◽  
Wei Zhao ◽  
Shixiao Xiao ◽  
Guokai Zhang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Electronic Noise ◽  
Mr Images ◽  
Electronic Components ◽  
Mr Image ◽  
Residual Part ◽  
Soft Threshold

Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused by the random thermal motion of electronic components, which reduces the quality and reliability of the images. This paper puts forward a hybrid denoising algorithm for MR images based on two sparsely represented morphological components and one residual part. To begin with, decompose a noisy MR image into the cartoon, texture, and residual parts by MCA, and then each part is denoised by using Wiener filter, wavelet hard threshold, and wavelet soft threshold, respectively. Finally, stack up all the denoised subimages to obtain the denoised MR image. The experimental results show that the proposed method has significantly better performance in terms of mean square error and peak signal-to-noise ratio than each method alone.

scholarly journals Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction

2021 ◽  
Author(s):  
Gaia Amaranta Taberna ◽  
Jessica Samogin ◽  
Dante Mantini
Keyword(s):  
Magnetic Resonance ◽  
Brain Structures ◽  
Magnetic Resonance Images ◽  
Head Model ◽  
Source Reconstruction ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Mr Image ◽  
Eeg Recordings ◽  
Tissue Modelling

AbstractIn the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing – the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping – template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation – information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.

Gaussian Noise Filter Using Variable Step Time Matrix of PCNN

2011 ◽  
Vol 48-49 ◽  
pp. 551-554 ◽  
Author(s):  
Yuan Yuan Cheng ◽  
Hai Yan Li ◽  
Qi Xiao ◽  
Yu Feng Zhang ◽  
Xin Ling Shi
Keyword(s):  
Gaussian Noise ◽  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Wiener Filter ◽  
Noise Filter ◽  
Variable Step ◽  
Mean Filter ◽  
Reduction Methods ◽  
Novel Method

A novel method was brought forward for the purpose of filtering Gaussian noise effectively by using variable step time matrix of the simplified pulse coupled neural network (PCNN). Firstly, the time matrix of PCNN, related to the grayscale and spatial information of an image, is calculated to identify the noise polluted pixels. Subsequently, a variable step, a long step for strong noise and a short step for weak noise, based on the time matrix is applied to modify the grayscale of noised pixels in a sliding window. And then wiener filter is used to the image to further filter the noise. Experiments show that the proposed filter can remove Gaussian noise effectively than other noise reduction methods such as median filter, mean filter, wiener filter etc, and the filtered image is smooth and the details and edges are sharp. Compared with existing PCNN based Gaussian noise filter, the proposed filter gets higher Peak Signal-to-Noise Ratio (PSNR) and better performance.

Three-tesla magnetic resonance imaging of the meniscus of the knee: What about equivocal errors?

2010 ◽  
Vol 51 (3) ◽  
pp. 296-301 ◽  
Author(s):  
Pieter Van Dyck ◽  
Filip M. Vanhoenacker ◽  
Jan L. Gielen ◽  
Lieven Dossche ◽  
Joost Weyler ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Medical Records ◽  
Medial Meniscus ◽  
Signal To Noise Ratio ◽  
Meniscal Tear ◽  
Lateral Meniscus ◽  
Meniscal Tears ◽  
Resonance Imaging ◽  
Mr Images ◽  
Knee Meniscus

Background: The significance of borderline magnetic resonance (MR) findings that are equivocal for a tear of the knee meniscus remains uncertain. Given their higher signal-to-noise ratio (SNR) and greater spatial resolution, these equivocal meniscal tears could be expected to be less frequent using a 3.0T MR system. Purpose: To investigate the prevalence of equivocal meniscal tears using 3.0T MR, and to study their impact on MR accuracy compared with arthroscopy in the detection of meniscal tears. Material and Methods: The medical records of 100 patients who underwent 3.0T MR imaging and subsequent arthroscopy of the knee were retrospectively reviewed. Two observers interpreted MR images in consensus, and menisci were diagnosed as torn (abnormality on two or more images), equivocal for a tear (abnormality on one image), or intact, using arthroscopy as the standard of reference. The prevalence of equivocal meniscal tears was assessed, and MR accuracy was calculated as follows: first, considering both torn menisci and equivocal diagnoses as positive for a tear; and second, considering only torn menisci as positive for a tear. Results: Evidence of meniscal tears on MR images was equivocal in 12 medial (12%) and three lateral (3%) menisci. Of these equivocal MR diagnoses, tears were found at arthroscopy in eight medial and one lateral meniscus. In our study, the specificity and positive predictive value increased for both the medial and lateral meniscus when only menisci with two or more abnormal images were considered to be torn: from 80% and 89% to 91% and 94% for the medial meniscus, and from 91% and 73% to 93% and 78% for the lateral meniscus, respectively. Conclusion: Subtle findings that are equivocal for a tear of the knee meniscus still make MR diagnosis difficult, even at 3.0T. We recommend that radiologists should rather be descriptive in reporting subtle or equivocal MR findings, alerting the clinician of possible meniscal tear.

scholarly journals Echocardiography image denoising using fractal wavelet transform

2018 ◽  
Vol 5 ◽  
pp. 23-33
Author(s):  
Reena Manandhar ◽  
Sanjeeb Prashad Pandey
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Gaussian Noise ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Hard Thresholding ◽  
Echocardiography Image ◽  
Different Types

One of the most important areas in image processing is medical image processing where the quality of the images has become an important issue. Most of the medical images are corrupted with the visual noise, and one of the such images is echocardiography image where this effect is more. So, this research aims to denoise the echocardiography image with fractal wavelet transform and to compare its performance with other wavelet based algorithm like hard thresholding, soft thresholding and wiener filter. Initially, the image is corrupted by the Gaussian noise with varying noise variances and is denoised using above mentioned different wavelet based denoising techniques. On comparison of the obtained results, it is observed that the fractal wavelet transform is well suited for highly degraded echocardiography images in terms of Mean Square Error (MSE) and Peak Signal To Noise Ratio (PSNR) than other wavelet based denoising methods. Further, the work could be enhanced to denoise the echocardiography image corrupted by other different types of noise. This research is limited to denoise the echocardiography image corrupted with Gaussian noise only.

scholarly journals Comparison of radiographer interobserver image registration variability using cone beam CT and MR for cervix radiotherapy

2020 ◽  
Vol 93 (1112) ◽  
pp. 20200169
Author(s):  
John Rodgers ◽  
Rosie Hales ◽  
Lee Whiteside ◽  
Jacqui Parker ◽  
Louise McHugh ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Image Registration ◽  
Image Guidance ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Mr Images ◽  
Mr Image ◽  
Image Guided Radiotherapy ◽  
Multimodality Image ◽  
Planning Ct

Objectives: The aim of this study was to assess the consistency of therapy radiographers performing image registration using cone beam computed tomography (CBCT)-CT, magnetic resonance (MR)-CT, and MR-MR image guidance for cervix cancer radiotherapy and to assess that MR-based image guidance is not inferior to CBCT standard practice. Methods: 10 patients receiving cervix radiation therapy underwent daily CBCT guidance and magnetic resonance (MR) imaging weekly during treatment. Offline registration of each MR image, and corresponding CBCT, to planning CT was performed by five radiographers. MR images were also registered to the earliest MR interobserver variation was assessed using modified Bland–Altman analysis with clinically acceptable 95% limits of agreement (LoA) defined as ±5.0 mm. Results: 30 CBCT-CT, 30 MR-CT and 20 MR–MR registrations were performed by each observer. Registration variations between CBCT-CT and MR-CT were minor and both strategies resulted in 95% LoA over the clinical threshold in the anteroposterior direction (CBCT-CT ±5.8 mm, MR-CT ±5.4 mm). MR–MR registrations achieved a significantly improved 95% LoA in the anteroposterior direction (±4.3 mm). All strategies demonstrated similar results in lateral and longitudinal directions. Conclusion: The magnitude of interobserver variations between CBCT-CT and MR-CT were similar, confirming that MR-CT radiotherapy workflows are comparable to CBCT-CT image-guided radiotherapy. Our results suggest MR–MR radiotherapy workflows may be a superior registration strategy. Advances in knowledge: This is the first publication quantifying interobserver registration of multimodality image registration strategies for cervix radical radiotherapy patients.

Detection of degradation of magnetic resonance (MR) images: Comparison of an automated MR image-quality analysis system with trained human observers

1995 ◽  
Vol 2 (4) ◽  
pp. 277-281 ◽  
Author(s):  
Edward A. Gardner ◽  
James H. Ellis ◽  
Rosemary J. Hyde ◽  
Alex M. Aisen ◽  
Douglas J. Quint ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Image Quality ◽  
Quality Analysis ◽  
Mr Images ◽  
Mr Image ◽  
Image Quality Analysis ◽  
Analysis System

scholarly journals Signal Enhancement of Low Magnetic Field Magnetic Resonance Image Using a Conventional- and Cyclic-Generative Adversarial Network Models With Unpaired Image Sets

2021 ◽  
Vol 11 ◽  
Author(s):  
Denis Yoo ◽  
Yuni Annette Choi ◽  
C. J. Rah ◽  
Eric Lee ◽  
Jing Cai ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Magnetic Resonance ◽  
Correlation Factor ◽  
Signal Enhancement ◽  
Enhancement Ratio ◽  
Mr Images ◽  
Mr Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Low Magnetic Field

In this study, the signal enhancement ratio of low-field magnetic resonance (MR) images was investigated using a deep learning-based algorithm. Unpaired image sets (0.06 Tesla and 1.5 Tesla MR images for different patients) were used in this study following three steps workflow. In the first step, the deformable registration of a 1.5 Tesla MR image into a 0.06 Tesla MR image was performed to ensure that the shapes of the unpaired set matched. In the second step, a cyclic-generative adversarial network (GAN) was used to generate a synthetic MR image of the original 0.06 Tesla MR image based on the deformed or original 1.5 Tesla MR image. Finally, an enhanced 0.06 Tesla MR image could be generated using the conventional-GAN with the deformed or synthetic MR image. The results from the optimized flow and enhanced MR images showed significant signal enhancement of the anatomical view, especially in the nasal septum, inferior nasal choncha, nasopharyngeal fossa, and eye lens. The signal enhancement ratio, signal-to-noise ratio (SNR) and correlation factor between the original and enhanced MR images were analyzed for the evaluation of the image quality. A combined method using conventional- and cyclic-GANs is a promising approach for generating enhanced MR images from low-magnetic-field MR.

A Bayesian Multiresolution Approach for Noise Removal in Medical Magnetic Resonance Images

2018 ◽  
Vol 29 (1) ◽  
pp. 189-201 ◽  
Author(s):  
Sima Sahu ◽  
Harsh Vikram Singh ◽  
Basant Kumar ◽  
Amit Kumar Singh
Keyword(s):  
Magnetic Resonance ◽  
Bayesian Approach ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
White Gaussian Noise ◽  
Error Estimator ◽  
Wavelet Coefficients ◽  
Signal To Noise ◽  
Noise Ratio

Abstract A Bayesian approach using wavelet coefficient modeling is proposed for de-noising additive white Gaussian noise in medical magnetic resonance imaging (MRI). In a parallel acquisition process, the magnetic resonance image is affected by white Gaussian noise, which is additive in nature. A normal inverse Gaussian probability distribution function is taken for modeling the wavelet coefficients. A Bayesian approach is implemented for filtering the noisy wavelet coefficients. The maximum likelihood estimator and median absolute deviation estimator are used to find the signal parameters, signal variances, and noise variances of the distribution. The minimum mean square error estimator is used for estimating the true wavelet coefficients. The proposed method is simulated on MRI. Performance and image quality parameters show that the proposed method has the capability to reduce the noise more effectively than other state-of-the-art methods. The proposed method provides 8.83%, 2.02%, 6.61%, and 30.74% improvement in peak signal-to-noise ratio, structure similarity index, Pratt’s figure of merit, and Bhattacharyya coefficient, respectively, over existing well-accepted methods. The effectiveness of the proposed method is evaluated by using the mean squared difference (MSD) parameter. MSD shows the degree of dissimilarity and is 0.000324 for the proposed method, which is less than that of the other existing methods and proves the effectiveness of the proposed method. Experimental results show that the proposed method is capable of achieving better signal-to-noise ratio performance than other tested de-noising methods.

scholarly journals Optimized Hybrid Model for Gaussian Noise Reduction images

2021 ◽  
Vol 19 (1) ◽  
pp. 9-17
Author(s):  
Lubna Farhi ◽  
Agha Yasir ◽  
Farhan Ur Rehman ◽  
Baqar A. Zardari ◽  
Ramsha Shakeel
Keyword(s):  
Noise Reduction ◽  
Hybrid Model ◽  
Gaussian Noise ◽  
Impulsive Noise ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Structural Similarity ◽  
Mean Value ◽  
The Matrix ◽  
High Level

In this paper, image noise is removed by using a hybrid model of wiener and fuzzy filters. It is a challenging task to remove Gaussian noise (GN) from an image and to protect the image’s edges. The Fuzzy-Wiener filter (FWF) hybrid model is used for optimizing the image smoothness and efficiency at a high level of GN. The efficiency is measured by using Structural Similarity (SSIM), Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). The proposed algorithm substitutes a mean value of the matrix for a non-overlapping block and replaces the total pixel number with each direction. In the proposed model, overall results proved that the optimized hybrid model FWF has an enormous computational speed and impulsive noise reduction, which enables efficient filtering as compared to the existing techniques.

Biological applications of microscopic magnetic resonance imaging

1993 ◽  
Vol 51 ◽  
pp. 90-91
Author(s):  
Russell E. Jacobs ◽  
S. Earl Fraser
Keyword(s):  
Magnetic Resonance ◽  
Spatial Resolution ◽  
Biological Samples ◽  
Spatial Information ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Water Concentration ◽  
Clinical Tool ◽  
Mr Image ◽  
Magnetic Field Gradients

The ability of MRI to provide three dimensional images of thick opaque samples in a noninvasive manner has made it an extremely important clinical tool. In addition, the large number of types of contrast mechanisms in a MR experiment offer the clinician and research scientist the possibility of adapting the image contrast to fit the problem of interest. While typical resolutions employed clinically are on the order of a millimeter, the notion of using MRI at microscopic resolutions arose early in the development of this technique. Spatial information is encoded in both the frequency and phase of the nuclear magnetic resonance signal by selective application of magnetic field gradients. Spatial resolution in biological samples is typically limited by a number of physical effects as well as signal-to-noise ratio (S/N) considerations. Estimate of the theoretical limits of resolution in the MR image arising from these phenomena range from 2 to 0.5μm. The practical spatial resolution is currently determined by the S/N which is often limited by the amount of time available to actually acquire the image (i.e. the temporal resolution). For example, a reasonable S/N clinical MR image can be obtained in about 5 minutes with a voxel (volume element) size of (1mm). We are interested in voxels down to ∼1μm on a side. Because most of the proton MR signal arises from water in biological samples and water concentration is roughly constant, the S/N change in the image will be proportional to the volume change: a factor of 10−9. Of course, this is true only if all experimental parameters are the same.

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