MR Brain Scans Research Articles

Varicella zoster vasculopathy causing recurrent ischaemic strokes in an immunocompetent patient.

A 66-year-old woman reported 10 days of generalised weakness, falls and memory 'glitches'. She had developed left-sided ophthalmic herpes zoster 3 months before but was otherwise well. MR scan of brain showed acute left-sided ischaemic strokes and CT cerebral angiogram identified marked stenoses of the left anterior and middle cerebral arteries. We suspected varicella-zoster virus vasculopathy, confirmed by cerebrospinal fluid analysis. Initially she had further ischaemic strokes despite intravenous acyclovir, prednisone, aspirin and clopidogrel. However, after prolonged acyclovir and prednisone, there were no new infarcts though imaging of left anterior and middle cerebral artery vessel walls showed persistent inflammation. Varicella zoster vasculopathy can cause recurrent ischaemic strokes, even in immunocompetent people with no cardiovascular risk factors, and despite long-term antiviral therapy.

Medical image registration via neural fields

Image registration is an essential step in many medical image analysis tasks. Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images. Recent learning-based methods, trained to directly predict transformations between two images, run much faster, but suffer from performance deficiencies due to domain shift. Here we present a new neural network based image registration framework, called NIR (Neural Image Registration), which is based on optimization but utilizes deep neural networks to model deformations between image pairs. NIR represents the transformation between two images with a continuous function implemented via neural fields, receiving a 3D coordinate as input and outputting the corresponding deformation vector. NIR provides two ways of generating deformation field: directly output a displacement vector field for general deformable registration, or output a velocity vector field and integrate the velocity field to derive the deformation field for diffeomorphic image registration. The optimal registration is discovered by updating the parameters of the neural field via stochastic mini-batch gradient descent. We describe several design choices that facilitate model optimization, including coordinate encoding, sinusoidal activation, coordinate sampling, and intensity sampling. NIR is evaluated on two 3D MR brain scan datasets, demonstrating highly competitive performance in terms of both registration accuracy and regularity. Compared to traditional optimization-based methods, our approach achieves better results in shorter computation times. In addition, our methods exhibit performance on a cross-dataset registration task, compared to the pre-trained learning-based methods.

Microchimerism in multiple sclerosis: The association between sex of offspring and MRI features in women with multiple sclerosis.

During pregnancy, fetal cells can migrate to the mother via blood circulation. A percentage of these cells survive in maternal tissues for decades generating a population of fetal microchimeric cells (fMCs), whose biological role is unclear. The aim of this study was to investigate the association between the sex of offspring, an indirect marker of fMCs, and magnetic resonance imaging (MRI) features in women with multiple sclerosis (MS). We recruited 26 nulliparous MS patients (NPp), 20 patients with at least one male son (XYp), and 8 patients with only daughters (XXp). Each patient underwent brain MR scan to acquire 3D-T2w FLAIR FatSat and 3D-T1w FSPGR/TFE. Lesion Segmentation Tool (LST) and FreeSurfer were used to obtain quantitative data from MRI. Additional data were collected using medical records. Multiple regression models were applied to evaluate the association between sex of offspring and MS data. Comparing NPp and XXp, we found that NPp had larger 4th ventricle volume (2.02 ± 0.59 vs. 1.70 ± 0.41; p = 0.022), smaller left entorhinal volume (0.55 ± 0.17 vs. 0.68 ± 0.25; p = 0.028), and lower thickness in the following cortical areas: left paracentral (2.34 ± 0.16 vs. 2.39 ± 0.17; p = 0.043), left precuneus (2.27 ± 0.11 vs. 2.34 ± 0.16; p = 0.046), right lateral occipital (2.14 ± 0.11 vs. 2.25 ± 0.08; p = 0.006). NPp also had lower thickness in left paracentral cortex (2.34 ± 0.16 vs. 2.46 ± 0.17; p = 0.004), left precalcarine cortex (1.64 ± 0.14 vs. 1.72 ± 0.12; p = 0.041), and right paracentral cortex (2.34 ± 0.17 vs. 2.42 ± 0.14; p = 0.015) when compared to XYp. Comparing XYp and XXp, we found that XYp had higher thickness in left cuneus (1.80 ± 0.14 vs. 1.93 ± 0.10; p = 0.042) and left pericalcarine areas (1.59 ± 0.19 vs. 1.72 ± 0.12; p = 0.032) and lower thickness in right lateral occipital cortex (2.25 ± 0.08 vs. 2.18 ± 0.13; p = 0.027). Our findings suggested an association between the sex of offspring and brain atrophy. Considering the sex of offspring as an indirect marker of fMCs, we speculated that fMCs could accumulate in different brain areas modulating MS neuropathological processes.

Neuropsychiatric symptoms and their neural correlates in individuals with mild cognitive impairment.

Neuropsychiatric symptoms are common in subjects with MCI and associated with higher risk of progression to AD. The cognitive and neuroanatomical correlates of neuropsychiatric symptoms in MCI have not been fully elucidated. In this study, we sought to evaluate the association between neuropsychiatric symptoms, cognitive function, regional tau deposition, and brain volumes in MCI subjects. A total of 233 MCI and 305 healthy comparisons were selected from the ADNI-3 cohort. All the subjects underwent a comprehensive neuropsychological assessment, volumetric MR brain scan, and Flortaucipir PET for in vivo assessment of regional tau deposition. Prevalence of neuropsychiatric symptoms was evaluated by means of the NPI questionnaire. Multivariate analyses of variance were used to detect differences in cognitive and imaging markers in MCI subjects with and without neuropsychiatric symptoms. 61.4% MCI subjects showed at least one neuropsychiatric symptom, with the most prevalent ones being depression (26.1%), irritability (23.6%), and sleep disturbances (23.6%). There was a significant effect of neuropsychiatric symptoms on cognitive tests of frontal and executive functions. MCI subjects with neuropsychiatric symptoms showed reduced brain volumes in the orbitofrontal and posterior cingulate cortices, while no effects were detected on regional tau deposition. Posterior cingulate cortex volume was the only predictor of global neuropsychiatric burden in this MCI population. Neuropsychiatric symptoms occur early in the AD trajectory and are mainly related to defects of control executive abilities and to the reduction of gray matter volume in the orbitofrontal and posterior cingulate cortices. A better understanding of the cognitive and neuroanatomical mechanisms of neuropsychiatric symptoms in MCI could help develop more targeted and efficacious treatment alternatives.

Multi-scale multi-hierarchy attention convolutional neural network for fetal brain extraction

Fetal brain extraction from in utero magnetic resonance imaging (MRI) scans is a key step for fetal brain development analysis. As the unpredicted fetal motion and maternal breathing generally result in blurring and ghosting in the slices of phase encoding direction, using the conventional 3D convolutional neural networks for fetal brain extraction with pseudo 3D fetal brain MR scans will lead to sub-optimal extraction performance. To address this issue, in this paper, we propose a novel multi-scale multi-hierarchy attention convolutional neural network (MSMHA-CNN) for fetal brain extraction in MR images. Specifically, to effectively utilize the 3D contextual information of the in utero MR image for fetal brain extraction, we employ multiple convolutional operations with different local receptive fields (i.e., with different kernel sizes) in each layer to learn the multi-scale feature representation for fetal brain extraction. To effectively use the learned multi-scale feature maps, we introduce a channel-wise spatial attention architecture to adaptively fuse those multi-scale feature maps derived from convolutional operations with different kernel sizes. In this way, the learned multi-scale features can be explicitly used to fetal brain extraction process. Besides, to take advantage of high-level feature maps at all spatial resolutions, we adopt the feature pyramid architecture to learn multi-hierarchy features for boosting the performance. We compare our proposed method with several state-of-the-art methods on two in utero MRI scan datasets (a total of 180 scans) for fetal brain extraction. The experimental results suggest the superior performance of the proposed MSMHA-CNN in comparison with its competitors.

Adaptive neighbor constrained deviation sparse variant fuzzy c-means clustering for brain MRI of AD subject

Progression of Alzheimer’s disease (AD) bears close proximity with the tissue loss in the medial temporal lobe (MTL) and enlargement of lateral ventricle (LV). The early stage of AD, mild cognitive impairment (MCI), can be traced by diagnosing brain MRI scans with advanced fuzzy c-means clustering algorithm that helps to take an appropriate intervention. In this paper, firstly the sparsity is initiated in clustering method that too rician noise is also incorporated for brain MR scans of AD subject. Secondly, a novel neighbor pixel constrained fuzzy c-means clustering algorithm is designed where topoloty-based selection of parsimonious neighbor pixels is automated. The adaptability in choice of neighbor pixel class outliers more justified object edge boundary which outperforms a dynamic cluster output. The proposed adaptive neighbor constrained deviation sparse variant fuzzy c-means clustering (AN_DsFCM) can withhold imposed sparsity and withstands rician noise at imposed sparse environment. This novel algorithm is applied for MRI of AD subjects and normative data is acquired to analyse clustering accuracy. The data processing pipeline of theoretically plausible proposition is elaborated in detail. The experimental results are compared with state-of-the-art fuzzy clustering methods for test MRI scans. Visual evaluation and statistical measures are studied to meet both image processing and clinical neurophysiology standards. Overall the performance of proposed AN_DsFCM is significantly better than other methods.

Paraneoplastic opsoclonus-myoclonus-ataxia syndrome secondary to ovarian cancer

A 63-year-old woman presented as a thrombolysis call with dysarthria, diplopia, vertigo and generalised weakness. She had a history of high-grade serous ovarian cancer, treated 3 years previously with surgery...

Bilateral trigeminal motor nucleus syndrome

A 48-year-old man was brought to our unit with deteriorating consciousness but no available history. His Glasgow Coma Scale score was 11/15 (E3, V4, M4). A limited neurological examination identified...

Bilateral spontaneous carotid-cavernous fistulae

A man in his late fifties had right-sided retro-orbital pain and diplopia for 1 month. There was no preceding head trauma. On examination, he had a right abducens palsy with...

Brain Tumour Segmentation in FLAIR MRI Using Sliding Window Texture Feature Extraction Followed by Fuzzy C-Means Clustering

In this paper, a hybrid approach using sliding window mechanism followed by fuzzy c means clustering is proposed for the automated brain tumour extraction. The proposed method consists three phases. The first phase is used for detecting the tumorous brain MR scans by implementing pre-processing techniques followed by texture features extraction and classification. Further, this phase also compares the performance of different classifiers. The second phase consists of the localization of the tumorous region using sliding window mechanism, in which a sized window sweeps through the whole tumorous MR scan and the window is classified as tumorous or non-tumorous. The third phase consists of fuzzy c means clustering to get the exact location of the tumour by removing the misclassified windows obtained from Phase 2. 2D single-spectral anatomical FLAIR MRI scans are considered for experiment. Outcomes demonstrate significant results in terms of sensitivity, specificity, accuracy, dice similarity coefficient in comparison with the other existing methods.

High tissue contrast image synthesis via multistage attention-GAN: Application to segmenting brain MR scans

Magnetic resonance imaging (MRI) presents a detailed image of the internal organs via a magnetic field. Given MRI’s non-invasive advantage in repeated imaging, the low-contrast MR images in the target area make segmentation of tissue a challenging problem. This study shows the potential advantages of synthetic high tissue contrast (HTC) images through image-to-image translation techniques. Mainly, we use a novel cycle generative adversarial network (Cycle-GAN), which provides a mechanism of attention to increase the contrast within the tissue. The attention block and training on HTC images are beneficial to our model to enhance tissue visibility. We use a multistage architecture to concentrate on a single tissue as a preliminary and filter out the irrelevant context in every stage in order to increase the resolution of HTC images. The multistage architecture reduces the gap between source and target domains and alleviates synthetic images’ artefacts. We apply our HTC image synthesising method to two public datasets. In order to validate the effectiveness of these images we use HTC MR images in both end-to-end and two-stage segmentation structures. The experiments on three segmentation baselines on BraTS’18 demonstrate that joining the synthetic HTC images in the multimodal segmentation framework develops the average Dice similarity scores (DSCs) of 0.8%, 0.6%, and 0.5% respectively on the whole tumour (WT), tumour core (TC), and enhancing tumour (ET) while removing one real MRI channels from the segmentation pipeline. Moreover, segmentation of infant brain tissue in T1w MR slices through our framework improves DSCs approximately 1% in cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM) compared to state-of-the-art segmentation techniques. The source code of synthesising HTC images is publicly available.

Magnetic resonance imaging morphological study of the effects of juvenile febrile convulsions on the brain structure of medial temporal lobe epilepsy

Objective: To investigate the effect of febrile convulsions on gray matter volume (GMV) in medial temporal lobe epilepsy (mTLE) and its correlation with disease duration. Methods: A retrospective study was conducted to collect 41 mTLE patients with a history of febrile convulsions (mTLE-FC), 42 mTLE patients with no initial precipitating injury (mTLE-noIPI), and 42 normal and age and sex matched normal controls. High-resolution T1-weighted (T(1)WI) whole brain MR scans were performed on all subjects. Voxel-based morphometry were used to obtain GMV brain maps, and the GMV differences between the three groups of subjects were compared (P<0.01, GRF corrected). Finally, Spearmen rank correlation analysis was used to explore the correlation between GMV changes and the course of disease. Results: Compared with the normal control subjects, each mTLE group showed extensive GMV reduction, mainly in the affected hippocampus, thalamus, temporal lobe, and bilateral cerebellum. Further analysis found that mTLE-FC group had more significant reductions in GMV than the mTLE-noIPI group in the affected hippocampus, amygdala, inferior temporal gyrus, contralateral hippocampus, para hippocampus, and inner cingulate gyrus. At the same time, the affected amygdala and hippocampal GMV in the mTLE-FC group was significantly negatively related to the course of disease (r=-0.381, P=0.014), while the mTLE-noIPI group had no downward trend (r=0.081, P=0.611). The atrophic trend of the affected amygdala and hippocampus in patients with mTLE-FC was significantly greater than that in patients with mTLE-noIPI (P=0.029, permutation test). Conclusions: There is extensive damage to the gray matter structure of bilateral cerebral hemispheres, mainly in the hippocampus, in mTLE patients. The brain damage of mTLE patients with a history of juvenile fever convulsions is more extensive and serious, and the trend of progressive exacerbation with the course of the disease is more obvious, suggesting mTLE associated with juvenile fever convulsions may have different pathophysiological mechanisms.

Mesial bifrontal stroke presenting as isolated spontaneous confabulations

A 52-year-old man with paroxysmal atrial fibrillation presented with spontaneous confabulation, working memory deficits, and frontal release signs. MR scan of brain showed bifrontal mesial ischaemic strokes and angiography demonstrated...

Hypoglossal palsy from an atlanto-axial synovial cyst.

A 63-year-old man had a 5-week history of a constant right-sided pulsating headache that was initially temporo-parietal but migrated to the suboccipital area. This was associated with new-onset of dysarthria. On examination, there was deviation of the tongue to the right, and the right-side had a wasted surface appearance, despite what he described as ‘wave-like’ prominence when looking at his tongue in the mirror (figure 1A). The remaining examination was normal. Figure 1 Clear right-sided tongue deviation on protrusion with right-sided wasting (A). Electromyography showing myokymia, comprising highly polyphasic units suggesting mild-to-moderate partial denervation of the right side of the tongue (B). Routine laboratory tests and erythrocyte sedimentation rate were normal. MR scan of brain and internal auditory meatuses showed a cystic lesion in the right hypoglossal canal, appearing to abut the right 12th cranial nerve and …

Changes in the intracranial volume from early adulthood to the sixth decade of life: A longitudinal study

Normal brain-aging occurs at all structural levels. Excessive pathophysiological changes in the brain, beyond the normal one, are implicated in the etiology of brain disorders such as severe forms of the schizophrenia spectrum and dementia. To account for brain-aging in health and disease, it is critical to study the age-dependent trajectories of brain biomarkers at various levels and among different age groups.The intracranial volume (ICV) is a key biological marker, and changes in the ICV during the lifespan can teach us about the biology of development, aging, and gene X environment interactions. However, whether ICV changes with age in adulthood is not resolved.Applying a semi-automatic in-house-built algorithm for ICV extraction on T1w MR brain scans in the Dutch longitudinal cohort (GROUP), we measured ICV changes. Individuals between the ages of 16 and 55 years were scanned up to three consecutive times with 3.32±0.32 years between consecutive scans (N = 482, 359, 302). Using the extracted ICVs, we calculated ICV longitudinal aging-trajectories based on three analysis methods; direct calculation of ICV differences between the first and the last scan, fitting all ICV measurements of individuals to a straight line, and applying a global linear mixed model fitting. We report statistically significant increase in the ICV in adulthood until the fourth decade of life (average change +0.03%/y, or about 0.5 ml/y, at age 20), and decrease in the ICV afterward (−0.09%/y, or about −1.2 ml/y, at age 55). To account for previous cross-sectional reports of ICV changes, we analyzed the same data using a cross-sectional approach. Our cross-sectional analysis detected ICV changes consistent with the previously reported cross-sectional effect. However, the reported amount of cross-sectional changes within this age range was significantly larger than the longitudinal changes. We attribute the cross-sectional results to a generational effect.In conclusion, the human intracranial volume does not stay constant during adulthood but instead shows a small increase during young adulthood and a decrease thereafter from the fourth decade of life. The age-related changes in the longitudinalmeasure are smaller than those reported using cross-sectional approaches and unlikely to affect structural brain imaging studies correcting for intracranial volume considerably. As to the possible mechanisms involved, this awaits further study, although thickening of the meninges and skull bones have been proposed, as well as a smaller amount of brain fluids addition above the overall loss of brain tissue.

High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Segmentation

Magnetic resonance imaging (MRI) provides varying tissue contrast images of internal organs based on a strong magnetic field. Despite the non-invasive advantage of MRI in frequent imaging, the low contrast MR images in the target area make tissue segmentation a challenging problem. This paper demonstrates the potential benefits of image-to-image translation techniques to generate synthetic high tissue contrast (HTC) images. Notably, we adopt a new cycle generative adversarial network (CycleGAN) with an attention mechanism to increase the contrast within underlying tissues. The attention block, as well as training on HTC images, guides our model to converge on certain tissues. To increase the resolution of HTC images, we employ multi-stage architecture to focus on one particular tissue as a foreground and filter out the irrelevant background in each stage. This multi-stage structure also alleviates the common artifacts of the synthetic images by decreasing the gap between source and target domains. We show the application of our method for synthesizing HTC images on brain MR scans, including glioma tumor. We also employ HTC MR images in both the end-to-end and two-stage segmentation structure to confirm the effectiveness of these images. The experiments over three competitive segmentation baselines on BraTS 2018 dataset indicate that incorporating the synthetic HTC images in the multi-modal segmentation framework improves the average Dice scores 0.8%, 0.6%, and 0.5% on the whole tumor, tumor core, and enhancing tumor, respectively, while eliminating one real MRI sequence from the segmentation procedure.

Classification of Multiple Sclerosis Disease using Cumulative Histogram

Multiple sclerosis (MS) is a chronic disease that affects different body parts including the brain. Detection and classification of MS brain lesions is of immense importance to physicians for the administration of appropriate treatment. Thus, this study investigates an automated framework for the diagnoses and classification of MS lesions in brain using magnetic resonance imaging (MRI). First, the MRI images format converted from dicom images of each patient into TIF format as MS lesion appears in white matter (WM) obviously. This is followed by a brain tissue segmentation using a k-nearest neighbor classifier. Then, cumulative empirical distributions or cumulative histograms (CH) of the segmented lesions are estimated along with other texture/statistical features that work on the difference between the intensity of MS lesions and its surrounding tissues. Finally, these CDFs are fused with and the statistical features for the classification of MS using K mean classifiers. Experiments are conducted, using transverse T2-weighted MR brain scans from 20 patients that are highly sensitive in detecting MS plaques, with gold standard classification obtained by an experienced MS. By comparing the evaluated performance with statistical features, our proposed fusion scored the highest accuracy with 98% and a false-positive rate of 1%.

Is there an association between orthostatic hypotension and cerebral white matter hyperintensities in older people? The Irish longitudinal study on ageing.

IntroductionOrthostatic Hypotension (OH) is an abnormal drop in blood pressure (BP) that occurs following orthostatic challenge. OH is associated with increased risk of falls, cognitive impairment and death. White Matter Hyperintensities (WMH) on MR Brain are associated with vascular risk factors such as hypertension, diabetes and age. We examined whether extent White matter intensities were associated with presence of OH detected in a community dwelling population of older people.MethodsIndividuals from the MR sub-study of the Irish Longitudinal Study of Ageing underwent a 3 Tesla MR Brain scan to assess WMH severity (Schelten’s Score). The scans were performed during the Wave 3 TILDA health assessment phase when the subjects also underwent assessment for OH with an active stand protocol. Data was analysed for association between WMH and vascular risks and orthostatic change in BP 10 second intervals during the OH evaluation.Results440 subjects were investigated; median age 72 years (65–92 years) and 228 (51.5%) female. Range of Scheltens’ Scores was 0–32. Mean score was 9.72 (SD 5.87). OH was detected in 68.4% (301). On linear regression, positive associations were found between Scheltens’ Score and age, hypertension, prior history of stroke and TIA, and with OH at 30, 70, 90 and 100 seconds following standing (p < 0.05, O.R. 1.9–2.5).ConclusionWMD is associated with OH detected at multiple time points using active stand in community dwelling older subjects. Further research is necessary to evaluate the direction of this association.

A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis.

Longitudinal magnetic resonance imaging (MRI) has an important role in multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new T2-w lesions on brain MR scans is considered a predictive biomarker for the disease. In this study, we propose a fully convolutional neural network (FCNN) to detect new T2-w lesions in longitudinal brain MR images. One year apart, multichannel brain MR scans (T1-w, T2-w, PD-w, and FLAIR) were obtained for 60 patients, 36 of them with new T2-w lesions. Modalities from both temporal points were preprocessed and linearly coregistered. Afterwards, an FCNN, whose inputs were from the baseline and follow-up images, was trained to detect new MS lesions. The first part of the network consisted of U-Net blocks that learned the deformation fields (DFs) and nonlinearly registered the baseline image to the follow-up image for each input modality. The learned DFs together with the baseline and follow-up images were then fed to the second part, another U-Net that performed the final detection and segmentation of new T2-w lesions. The model was trained end-to-end, simultaneously learning both the DFs and the new T2-w lesions, using a combined loss function. We evaluated the performance of the model following a leave-one-out cross-validation scheme. In terms of the detection of new lesions, we obtained a mean Dice similarity coefficient of 0.83 with a true positive rate of 83.09% and a false positive detection rate of 9.36%. In terms of segmentation, we obtained a mean Dice similarity coefficient of 0.55. The performance of our model was significantly better compared to the state-of-the-art methods (p<0.05). Our proposal shows the benefits of combining a learning-based registration network with a segmentation network. Compared to other methods, the proposed model decreases the number of false positives. During testing, the proposed model operates faster than the other two state-of-the-art methods based on the DF obtained by Demons.

3 tesla magnetic resonance imaging and multiplanar reconstruction of the brain and its associated structures in sheep

The purpose of the study was to scan the brain and related structures in sheep with high-resolution magnetic resonance imaging (MRI) and three-dimensional (3D) multiplanar reconstruction for defining the anatomical regions. Six adult sheep, three of six were male and three of six were female used as cadaver, were utilized in this research. Heads were scanned at 3 Tesla Siemens Magnetom Spin-Echo MRI devices using the human head coil. The processes were acquired in both T1 and T2 weighted slices and were reconstructed by using Leonardo workstation. The cranioencephalic structures and anatomical details were defined and labeled in all slice of 3 planes that were obtained 1 mm thickness sequential images in sagittal, frontal, and transversal planes. According to the MRI measurements obtained intracranially from the brain, the average value of length, height, and width of the brain were 87.1 ± 0.3, 46.8 ± 0.7, and 62.2 ± 0.4 mm, respectively. It was observed that in T1 weighted images were more effective to identify deep brain structures and anatomical details. On 3D reconstructed images obtained from the study can be used as a reference in head and brain MR scans in multidisciplinary studies where sheep are used as animal models.