scholarly journals Super Resolution of Magnetic Resonance Images

2021 ◽  
Vol 7 (6) ◽  
pp. 101
Author(s):  
Prabhjot Kaur ◽  
Anil Kumar Sao ◽  
Chirag Kamal Ahuja
Keyword(s):  
Magnetic Resonance ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Magnetic Resonance Images ◽  
Low Rank ◽  
Mr Images ◽  
Unified Framework ◽  
Gradient Profile ◽  
Structural Deformity ◽  
Image Details

In this work, novel denoising and super resolution (SR) approaches for magnetic resonance (MR) images are addressed, and are integrated in a unified framework, which do not require example low resolution (LR)/high resolution (HR)/cross-modality/noise-free images and prior information of noise–noise variance. The proposed method categorizes the patches as either smooth or textured and then denoises them by deploying different denoising strategies for efficient denoising. The denoising algorithm is integrated into the SR approach, which uses a gradient profile-based constraint in a sparse representation-based framework to improve the resolution of MR images with reduced smearing of image details. This constraint regularizes the estimation of HR images such that the estimated HR image has gradient profiles similar to the gradient profiles of the original HR image. For this, the gradient profile sharpness (GPS) values of an unknown HR image are estimated using an approximated piece-wise linear relation among GPS values of LR and upsampled LR images. The experiments are performed on three different publicly available datasets. The proposed SR approach outperforms the existing unsupervised SR approach addressed for real MR images that exploits low rank and total variation (LRTV) regularization, by an average peak signal to noise ratio (PSNR) of 0.73 dB and 0.38 dB for upsampling factors 2 and 3, respectively. For the super resolution of noisy real MR images (degraded with 2% noise), the proposed approach outperforms the LRTV approach by an average PSNR of 0.54 dB and 0.46 dB for upsampling factors 2 and 3, respectively. The qualitative analysis is shown for real MR images from healthy subjects and subjects with Alzheimer’s disease and structural deformity, i.e., cavernoma.

scholarly journals Optimized Resolution-Oriented Many-to-One Intensity Standardization Method for Magnetic Resonance Images

2019 ◽  
Vol 9 (24) ◽  
pp. 5531
Author(s):  
Yuan Gao ◽  
Yuanyuan Wang ◽  
Jinhua Yu
Keyword(s):  
Magnetic Resonance ◽  
Visual Information ◽  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Computer Aided Diagnosis ◽  
Magnetic Resonance Images ◽  
Image Resolution ◽  
Mr Images ◽  
Computer Aided ◽  
Aided Diagnosis

With the development of big data, Radiomics and deep-learning methods based on magnetic resonance (MR) images, it is necessary to conduct large databases containing MR images from multiple centers. Having huge intensity distribution differences among images reduced or even eliminated, robust computer-aided diagnosis models could be established. Therefore, an optimized intensity standardization model is proposed. The network structure, loss function, and data input strategy were optimized to better avoid the image resolution loss during transformation. The experimental dataset was obtained from five MR scanners located in four hospitals and was divided into nine groups based on the imaging parameters, during which 9152 MR images from 499 participants were collected. Experiments show the superiority of the proposed method to the previously proposed unified model in resolution metrics including the peak signal-to-noise ratio, structural similarity, visual information fidelity, universal quality index, and image fidelity criterion. Another experiment further shows the advantage of the proposed method in increasing the effectiveness of following computer-aided diagnosis models by better preservation of MR image details. Moreover, the advantage over conventional standardization methods are also shown. Thus, MR images from different centers can be standardized using the proposed method, which will facilitate numerous data-driven medical imaging studies.

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.

scholarly journals Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yunjie Chen ◽  
Tianming Zhan ◽  
Ji Zhang ◽  
Hongyuan Wang
Keyword(s):  
Magnetic Resonance ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Magnetic Resonance Images ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Proposed Model ◽  
Brain Mr Images ◽  
The Impact

We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

Reducing susceptibility artefacts in magnetic resonance images of the canine stifle following surgery for cranial cruciate ligament deficiency

2012 ◽  
Vol 25 (06) ◽  
pp. 488-497 ◽  
Author(s):  
J. Grierson ◽  
C. R. Lamb ◽  
F. H. David
Keyword(s):  
Magnetic Resonance ◽  
Tibial Plateau ◽  
Cruciate Ligament ◽  
Magnetic Resonance Images ◽  
Proton Density ◽  
Mr Images ◽  
Cranial Cruciate Ligament ◽  
Metallic Implants ◽  
Image Ranking ◽  
Canine Stifle

SummaryBackground: Magnetic resonance (MR) images of the postoperative canine stifle are adversely affected by susceptibility artefacts associated with metallic implants.Objectives: To determine empirically to what extent susceptibility artefacts could be reduced by modifications to MR technique.Methods: Three cadaveric limbs with a tibial plateau levelling osteotomy (TPLO), tibial tuberosity advancement (TTA), or extra-capsular stabilization (ECS) implant, respectively, were imaged at 1.5T. Series of proton density and T2-weighted images were acquired with different combinations of frequency-encoding gradient (FEG) direction and polarity, stifle flexion or extension, echo spacing (ES), and readout bandwidth (ROBW), and ranked. The highest rank (a rank of 1) corresponded to the smallest artefact.Results: Image ranking was affected by FEG polarity (p = 0.005), stifle flexion (p = 0.01), and ROBW (p = 0.0001). For TPLO and TTA implants, the highest ranked images were obtained with the stifle flexed, lateromedial FEG, and medial polarity for dorsal images, and craniocaudal FEG and caudal polarity for sagittal images. For the ECS implant, the highest ranked images were obtained with the stifle extended, a proximodistal FEG and proximal polarity for dorsal images, and craniocaudal FEG and cranial polarity for sagittal images.Clinical significance: Susceptibility artefacts in MR images of postoperative canine stifles do not preclude clinical evaluation of joints with ECS or TTA implants.Part of this study was presented at the Annual Meeting of the American College of Veterinary Radiology, Albuquerque, NM, October 2011.

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 Interobserver variability in quality assessment of magnetic resonance images

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Rafal Obuchowicz ◽  
Mariusz Oszust ◽  
Adam Piorkowski
Keyword(s):  
Magnetic Resonance ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Interobserver Variability ◽  
Poor Performance ◽  
Magnetic Resonance Images ◽  
Perceptual Quality ◽  
Mr Images

Abstract Background The perceptual quality of magnetic resonance (MR) images influences diagnosis and may compromise the treatment. The purpose of this study was to evaluate how the image quality changes influence the interobserver variability of their assessment. Methods For the variability evaluation, a dataset containing distorted MRI images was prepared and then assessed by 31 experienced medical professionals (radiologists). Differences between observers were analyzed using the Fleiss’ kappa. However, since the kappa evaluates the agreement among radiologists taking into account aggregated decisions, a typically employed criterion of the image quality assessment (IQA) performance was used to provide a more thorough analysis. The IQA performance of radiologists was evaluated by comparing the Spearman correlation coefficients, ρ, between individual scores with the mean opinion scores (MOS) composed of the subjective opinions of the remaining professionals. Results The experiments show that there is a significant agreement among radiologists (κ=0.12; 95% confidence interval [CI]: 0.118, 0.121; P<0.001) on the quality of the assessed images. The resulted κ is strongly affected by the subjectivity of the assigned scores, separately presenting close scores. Therefore, the ρ was used to identify poor performance cases and to confirm the consistency of the majority of collected scores (ρmean = 0.5706). The results for interns (ρmean = 0.6868) supports the finding that the quality assessment of MR images can be successfully taught. Conclusions The agreement observed among radiologists from different imaging centers confirms the subjectivity of the perception of MR images. It was shown that the image content and severity of distortions affect the IQA. Furthermore, the study highlights the importance of the psychosomatic condition of the observers and their attitude.

scholarly journals Effusion attenuates the effect of synovitis on radiographic progression in patients with hand osteoarthritis: a longitudinal magnetic resonance imaging study

2020 ◽  
Vol 40 (1) ◽  
pp. 315-319
Author(s):  
W. Damman ◽  
R. Liu ◽  
M. Reijnierse ◽  
F. R. Rosendaal ◽  
J. L. Bloem ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Radiographic Progression ◽  
Imaging Study ◽  
Magnetic Resonance Images ◽  
High Signal ◽  
Hand Osteoarthritis ◽  
Mr Images ◽  
Contrast Enhanced ◽  
Key Points

AbstractAn exploratory study to determine the role of effusion, i.e., fluid in the joint, in pain, and radiographic progression in patients with hand osteoarthritis. Distal and proximal interphalangeal joints (87 patients, 82% women, mean age 59 years) were assessed for pain. T2-weighted and Gd-chelate contrast-enhanced T1-weighted magnetic resonance images were scored for enhanced synovial thickening (EST, i.e., synovitis), effusion (EST and T2-high signal intensity [hsi]) and bone marrow lesions (BMLs). Effusion was defined as follows: (1) T2-hsi > 0 and EST = 0; or 2) T2-hsi = EST but in different joint locations. Baseline and 2-year follow-up radiographs were scored following Kellgren-Lawrence, increase ≥ 1 defined progression. Associations between the presence of effusion and pain and radiographic progression, taking into account EST and BML presence, were explored on the joint level. Effusion was present in 17% (120/691) of joints, with (63/120) and without (57/120) EST. Effusion on itself was not associated with pain or progression. The association with pain and progression, taking in account other known risk factors, was stronger in the absence of effusion (OR [95% CI] 1.7 [1.0–2.9] and 3.2 [1.7–5.8]) than in its presence (1.6 [0.8–3.0] and 1.3 [0.5–3.1]). Effusion can be assessed on MR images and seems not to be associated with pain or radiographic progression but attenuates the association between synovitis and progression. Key Points• Effusion is present apart from synovitis in interphalangeal joints in patients with hand OA.• Effusion in finger joints can be assessed as a separate feature on MR images.• Effusion seems to be of importance for its attenuating effect on the association between synovitis and radiographic progression.

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