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

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.

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

BioMed Research International ◽

10.1155/2016/6727290 ◽

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.

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Neuroanatomy and Volumes of Brain Structures of a Live California Sea Lion (Zalophus californianus) From Magnetic Resonance Images

The Anatomical Record ◽

10.1002/ar.20937 ◽

2009 ◽

Vol 292 (10) ◽

pp. 1523-1547 ◽

Cited By ~ 23

Author(s):

Eric W. Montie ◽

Nicola Pussini ◽

Gerald E. Schneider ◽

Thomas W.K. Battey ◽

Sophie Dennison ◽

...

Keyword(s):

Magnetic Resonance ◽

Brain Structures ◽

Magnetic Resonance Images ◽

Zalophus Californianus ◽

Sea Lion ◽

California Sea Lion

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

Veterinary and Comparative Orthopaedics and Traumatology ◽

10.3415/vcot-12-03-0031 ◽

2012 ◽

Vol 25 (06) ◽

pp. 488-497 ◽

Cited By ~ 5

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.

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

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/1405647 ◽

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.

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Interobserver variability in quality assessment of magnetic resonance images

BMC Medical Imaging ◽

10.1186/s12880-020-00505-z ◽

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.

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Multicomponent MR Image Denoising

International Journal of Biomedical Imaging ◽

10.1155/2009/756897 ◽

2009 ◽

Vol 2009 ◽

pp. 1-10 ◽

Cited By ~ 28

Author(s):

José V. Manjón ◽

Neil A. Thacker ◽

Juan J. Lull ◽

Gracian Garcia-Martí ◽

Luís Martí-Bonmatí ◽

...

Keyword(s):

Random Noise ◽

Principal Component ◽

Magnetic Resonance Images ◽

Image Resolution ◽

Single Image ◽

Mr Images ◽

Mr Image ◽

Automatic Feature Extraction ◽

Improved Performance ◽

Multicomponent Nature

Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. It is because of this reason that denoising methods have been traditionally applied to improve MR image quality. Many of these methods use the information of a single image without taking into consideration the intrinsic multicomponent nature of MR images. In this paper we propose a new filter to reduce random noise in multicomponent MR images by spatially averaging similar pixels using information from all available image components to perform the denoising process. The proposed algorithm also uses a local Principal Component Analysis decomposition as a postprocessing step to remove more noise by using information not only in the spatial domain but also in the intercomponent domain dealing in a higher noise reduction without significantly affecting the original image resolution. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical multicomponent MR images showing an improved performance in all cases analyzed.

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

Clinical Rheumatology ◽

10.1007/s10067-020-05341-8 ◽

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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BISON: Brain tissue segmentation pipeline using T 1 ‐weighted magnetic resonance images and a random forest classifier

Magnetic Resonance in Medicine ◽

10.1002/mrm.28547 ◽

2020 ◽

Vol 85 (4) ◽

pp. 1881-1894 ◽

Cited By ~ 1

Author(s):

Mahsa Dadar ◽

D. Louis Collins

Keyword(s):

Magnetic Resonance ◽

Random Forest ◽

Brain Tissue ◽

Magnetic Resonance Images ◽

Random Forest Classifier ◽

Tissue Segmentation ◽

Brain Tissue Segmentation

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Computer Aided Interpretation of Fibrous Texture in Hepatic Magnetic Resonance Images

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.647.325 ◽

2013 ◽

Vol 647 ◽

pp. 325-330 ◽

Cited By ~ 3

Author(s):

Yu Fan Zeng ◽

Xue Jun Zhang ◽

Wen Yan ◽

Li Ling Long ◽

Yu Kun Huang ◽

...

Keyword(s):

Magnetic Resonance ◽

Hepatic Fibrosis ◽

Region Of Interest ◽

Texture Features ◽

Magnetic Resonance Images ◽

Optimal Number ◽

Support Vector ◽

Mr Images ◽

Accuracy Rate ◽

Occurrence Matrix

The fibrous texture in liver is one of important signs for interpreting the chronic liver diseases in radiologists’ routines. In order to investigate the usefulness of various texture features calculated by computer algorithm on hepatic magnetic resonance (MR) images, 15 texture features were calculated from the gray level co-occurrence matrix (GLCM) within a region of interest (ROI) which was selected from the MR images with 6 stages of hepatic fibrosis. By different combination of 15 features as input vectors, the classifier had different performance in staging the hepatic fibrosis. Each combination of texture features was tested by Support Vector Machine (SVM) with leave one case out method. 173 patients’ MR images including 6 stages of hepatic fibrosis were scanned within recent two years. The result showed that optimal number of features was confirmed from 3 to 7 by investigating the classified accuracy rate between each stage/group. It is evident that angular second moment, entropy, sum average and sum entropy played the most significant role in classification.

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