scholarly journals Neuroimaging predictors of structural epilepsy in the COVID-19 catamnesis

2021 ◽  
Vol 13 (3) ◽  
pp. 274-285
Author(s):  
N. E. Maslov ◽  
N. V. Yuryeva ◽  
E. I. Khamtsova ◽  
A. A. Litvinova
Keyword(s):  
Nervous System ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Clinical Disorder ◽  
Brain Substance ◽  
Coronavirus Infection ◽  
Magnetic Resonance Imaging Mri ◽  
Structural Epilepsy ◽  
Novel Coronavirus ◽  
The Brain

Respiratory system pathology is the most common clinical disorder associated with COVID-19. However, there are also lesions of the immune, cardiovascular, genitourinary, endocrine systems, and digestive tract. In addition, there are numerous reports on infection-related neurological manifestations, which can be divided into 3 groups: central nervous system manifestations (headache and dizziness, stroke, encephalopathy, encephalitis, acute myelitis), lesions of the peripheral nervous system (anosmia, Guillain–Barre syndrome), secondary lesions in the skeletal muscles. Brain damage that occurs during novel coronavirus infection and determines some of the above-mentioned manifestations often account for the development of structural epilepsies. Only a few scarce review articles on neuroimaging features in patients with COVID-19 have been found in Russian research publications.The objective of the review was to collect, analyze and summarize the results of brain magnetic resonance imaging (MRI), currently accumulated worldwide in patients with COVID-19. We present the most common diagnoses based on brain MRI in patients with COVID-19 established by foreign researchers from March 2020 to March 2021, as well as initial attempts to interpret the pathophysiological mechanisms of the changes observed in the brain substance.

scholarly journals Pelizaeus-Merzbacher Disease: A Case Report

2020 ◽  
Author(s):  
Ghazaleh Jamalipour Soufi ◽  
Siavash Iravan
Keyword(s):  
Case Report ◽  
White Matter ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Proteolipid Protein ◽  
Radiology Department ◽  
Magnetic Resonance Imaging Mri ◽  
Normal White Matter ◽  
Pelizaeus Merzbacher Disease ◽  
The Brain

Pelizaeus-Merzbacher Disease (PMD), as a rare genetically x-linked leukodystrophy, is a disorder of proteolipid protein expression in myelin formation. This disorder is clinically presented by neurodevelopmental delay and abnormal pendular eye movements. The responsible gene for this disorder is the proteolipid protein gene (PLP1). Our case was a oneyear-old boy referred to the radiology department for evaluating the Central Nervous System (CNS) development by brain Magnetic Resonance Imaging (MRI). Clinically, he demonstrated neuro-developmental delay symptoms. The brain MRI results indicated a diffuse lack of normal white matter myelination. This case report should be considered about the possibilityof PMD in the brain MRI of patients who present a diffuse arrest of normal white matter myelination.

scholarly journals 3D U-Net for Skull Stripping in Brain MRI

2019 ◽  
Vol 9 (3) ◽  
pp. 569 ◽  
Author(s):  
Hyunho Hwang ◽  
Hafiz Zia Ur Rehman ◽  
Sungon Lee
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Brain Extraction ◽  
Skull Stripping ◽  
Segmentation Algorithms ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Network ◽  
The Brain

Skull stripping in brain magnetic resonance imaging (MRI) is an essential step to analyze images of the brain. Although manual segmentation has the highest accuracy, it is a time-consuming task. Therefore, various automatic segmentation algorithms of the brain in MRI have been devised and proposed previously. However, there is still no method that solves the entire brain extraction problem satisfactorily for diverse datasets in a generic and robust way. To address these shortcomings of existing methods, we propose the use of a 3D-UNet for skull stripping in brain MRI. The 3D-UNet was recently proposed and has been widely used for volumetric segmentation in medical images due to its outstanding performance. It is an extended version of the previously proposed 2D-UNet, which is based on a deep learning network, specifically, the convolutional neural network. We evaluated 3D-UNet skull-stripping using a publicly available brain MRI dataset and compared the results with three existing methods (BSE, ROBEX, and Kleesiek’s method; BSE and ROBEX are two conventional methods, and Kleesiek’s method is based on deep learning). The 3D-UNet outperforms two typical methods and shows comparable results with the specific deep learning-based algorithm, exhibiting a mean Dice coefficient of 0.9903, a sensitivity of 0.9853, and a specificity of 0.9953.

scholarly journals Transient Periictal Brain Imaging Abnormality in a Saudi Patient with Probable Celiac Disease Epilepsy and Occipital Calcification Syndrome

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
Soha Khan ◽  
Asma AlNajjar ◽  
Abdullah Alquaydheb ◽  
Shahpar Nahrir
Keyword(s):  
Celiac Disease ◽  
Energy Demand ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Pump Failure ◽  
Mediterranean Countries ◽  
Apparent Diffusion ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Celiac disease epilepsy and occipital calcification (CEC) syndrome is a rare, emerging disease first described in 1992. To date, fewer than 200 cases have been reported worldwide. CEC syndrome is generally thought to be a genetic, noninherited, and ethnically and geographically restricted disease in Mediterranean countries. However, we report the first ever case of probable CEC in a Saudi patient. Furthermore, the patient manifested a magnitude of brain magnetic resonance imaging (MRI) signal abnormalities during the periictal period which, to the best of our knowledge, has never been described in CEC. The brain MRI revealed diffusion-weighted imaging (DWI) restriction with a concordant area of apparent diffusion coefficient (ADC) hypointensity around bilateral occipital area of calcification. An imbalance between the heightened energy demand during ictal phase of the seizure and unadjusted blood supply may have caused an electric pump failure and cytotoxic edema, which then led to DWI/ADC signal alteration.

Brain MRI in Inherited Metabolic Diseases of Adulthood

2016 ◽  
Author(s):  
Alessandro Burlina ◽  
Renzo Manara
Keyword(s):  
Magnetic Resonance Imaging ◽  
Metabolic Diseases ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Diagnostic Process ◽  
Resonance Imaging ◽  
Inherited Metabolic Diseases ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Brain magnetic resonance imaging (MRI) is an important tool to investigate inherited metabolic diseases in adulthood. In the present chapter the major neuroradiological findings that brain MRI can provide to adult metabolic clinicians will be presented, classified according to white and gray matter involvement.The role of brain MRI in the diagnostic process and clinical monitoring of specific inherited metabolic affecting the brain will be examined.

scholarly journals Beyond Oncological Hyperthermia: Physically Drivable Magnetic Nanobubbles as Novel Multipurpose Theranostic Carriers in the Central Nervous System

Molecules ◽  
2020 ◽  
Vol 25 (9) ◽  
pp. 2104 ◽  
Author(s):  
Eleonora Ficiarà ◽  
Shoeb Anwar Ansari ◽  
Monica Argenziano ◽  
Luigi Cangemi ◽  
Chiara Monge ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Brain Tumors ◽  
Ad Hoc ◽  
Superparamagnetic Iron Oxide ◽  
Conventional Radiotherapy ◽  
The Central Nervous System ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Magnetic Oxygen-Loaded Nanobubbles (MOLNBs), manufactured by adding Superparamagnetic Iron Oxide Nanoparticles (SPIONs) on the surface of polymeric nanobubbles, are investigated as theranostic carriers for delivering oxygen and chemotherapy to brain tumors. Physicochemical and cyto-toxicological properties and in vitro internalization by human brain microvascular endothelial cells as well as the motion of MOLNBs in a static magnetic field were investigated. MOLNBs are safe oxygen-loaded vectors able to overcome the brain membranes and drivable through the Central Nervous System (CNS) to deliver their cargoes to specific sites of interest. In addition, MOLNBs are monitorable either via Magnetic Resonance Imaging (MRI) or Ultrasound (US) sonography. MOLNBs can find application in targeting brain tumors since they can enhance conventional radiotherapy and deliver chemotherapy being driven by ad hoc tailored magnetic fields under MRI and/or US monitoring.

Pediatric Reversible Cerebral Vasoconstriction Syndrome: Two Unique Cases with a Review of all Reported Children

2021 ◽  
Author(s):  
Neelu Desai ◽  
Rahul Badheka ◽  
Nitin Shah ◽  
Vrajesh Udani
Keyword(s):  
Magnetic Resonance ◽  
Cerebral Arteries ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Reversible Cerebral Vasoconstriction Syndrome ◽  
Neurological Deficits ◽  
Cerebral Vasoconstriction ◽  
Magnetic Resonance Imaging Mri ◽  
Reversible Encephalopathy ◽  
Mr Angiogram

AbstractReversible cerebral vasoconstriction syndrome (RCVS) has been well described in adults, but pediatric cases are yet under recognized. We describe two children with RCVS and review similar already published pediatric cases. The first patient was a 10-year-old girl who presented with severe headaches and seizures 3 days after blood transfusion. Brain magnetic resonance imaging (MRI) showed changes compatible with posterior reversible encephalopathy syndrome and subarachnoid hemorrhage. Magnetic resonance angiogram showed diffuse vasoconstriction of multiple cerebral arteries. The second patient was a 9-year-old boy who presented with severe thunderclap headaches. Brain MRI showed isolated intraventricular hemorrhage. Computed tomography/MR angiogram and digital subtraction angiogram were normal. A week later, he developed focal neurological deficits. Repeated MR angiogram showed diffuse vasospasm of multiple intracranial arteries. Both children recovered completely. A clinico-radiological review of previously reported childhood RCVS is provided.

scholarly journals Prevalence and Clinical Implications of Incidentally Detected Parotid Lesions as Blind Spot on Brain MRI: A Single-Center Experience

Medicina ◽  
2021 ◽  
Vol 57 (8) ◽  
pp. 836
Author(s):  
In-Chul Nam ◽  
Hye-Jin Baek ◽  
Kyeong-Hwa Ryu ◽  
Jin-Il Moon ◽  
Eun Cho ◽  
...  
Keyword(s):  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Blind Spot ◽  
Clinical Implications ◽  
Single Center Experience ◽  
Superficial Lobe ◽  
Mean Size ◽  
Magnetic Resonance Imaging Mri ◽  
Clinical Awareness ◽  
Low Prevalence

Background and objective: This study was conducted to assess the prevalence and clinical implications of parotid lesions detected incidentally during brain magnetic resonance imaging (MRI) examination. Materials and Methods: Between February 2016 and February 2021, we identified 86 lesions in the brain MRI reports of 84 patients that contained the words “parotid gland” or “PG”. Of these, we finally included 49 lesions involving 45 patients following histopathological confirmation. Results: Based on the laboratory, radiological or histopathological findings, the prevalence of incidental parotid lesions was low (1.2%). Among the 45 study patients, 41 (91.1%) had unilateral lesions, and the majority of the lesions were located in the superficial lobe (40/49, 81.6%). The mean size of the parotid lesions was 1.3 cm ± 0.4 cm (range, 0.5 cm–2.8 cm). Of these, 46 parotid lesions (93.9%) were benign, whereas the remaining three lesions were malignant (6.1%). Conclusions: Despite the low prevalence and incidence of malignancy associated with incidental parotid lesions detected on brain MRI, the clinical implications are potentially significant. Therefore, clinical awareness and appropriate imaging work-up of these lesions are important for accurate diagnosis and timely management.

scholarly journals Ensemble classification and segmentation for intracranial metastatic tumors on MRI images based on 2D U-nets

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Cheng-Chung Li ◽  
Meng-Yun Wu ◽  
Ying-Chou Sun ◽  
Hung-Hsun Chen ◽  
Hsiu-Mei Wu ◽  
...  
Keyword(s):  
Active Contour Model ◽  
Brain Mri ◽  
Gamma Knife Radiosurgery ◽  
Imbalanced Data ◽  
Brain Magnetic Resonance Imaging ◽  
Region Of Interest ◽  
Ensemble Classification ◽  
Metastatic Tumors ◽  
Post Contrast ◽  
Magnetic Resonance Imaging Mri

AbstractThe extraction of brain tumor tissues in 3D Brain Magnetic Resonance Imaging (MRI) plays an important role in diagnosis before the gamma knife radiosurgery (GKRS). In this article, the post-contrast T1 whole-brain MRI images had been collected by Taipei Veterans General Hospital (TVGH) and stored in DICOM format (dated from 1999 to 2018). The proposed method starts with the active contour model to get the region of interest (ROI) automatically and enhance the image contrast. The segmentation models are trained by MRI images with tumors to avoid imbalanced data problem under model construction. In order to achieve this objective, a two-step ensemble approach is used to establish such diagnosis, first, classify whether there is any tumor in the image, and second, segment the intracranial metastatic tumors by ensemble neural networks based on 2D U-Net architecture. The ensemble for classification and segmentation simultaneously also improves segmentation accuracy. The result of classification achieves a F1-measure of $$75.64\%$$ 75.64 % , while the result of segmentation achieves an IoU of $$84.83\%$$ 84.83 % and a DICE score of $$86.21\%$$ 86.21 % . Significantly reduce the time for manual labeling from 30 min to 18 s per patient.

Manifestation of cognitive impairments after a prior coronavirus infection COVID-19 in a patient with ApoE ε4 genotype

2021 ◽  
Vol 26 (2) ◽  
pp. 25-29
Author(s):  
M. S. Novikova ◽  
V. V. Zakharov ◽  
N. V. Vakhnina
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Small Vessel Disease ◽  
Brain Mri ◽  
Periventricular White Matter ◽  
Everyday Activity ◽  
Apoe Ε4 ◽  
Coronavirus Infection ◽  
Novel Coronavirus

Nowadays, the novel coronavirus infection (COVID-19) pandemic is one of the most important global health problems. There is increasing evidence that COVID-19 affects central and peripheral nervous system as well. The paper presents a clinical case of a 47 old patient with the ApoE ε4 haplotype and family history of Alzheimer’s disease who developed cognitive impairment after acute COVID-19. Before the infection the patient has no cognitive complaints and preserved everyday activity. After novel coronavirus infection, which was observed in mild form, the patient had started to complain on constant excessive forgetfulness. Neuropsychological assessment confirmed the presence of pre-mild cognitive impairment of predominantly single domain amnestic type. However, brain MRI showed only subtle periventricular white matter changes usually attributed to small vessel disease. Memory complaints were observed for 3 months of follow up despite intensive cognitive training, optimization of lifestyle and therapy with choline alphoscerate. Probable links between coronavirus infectious and cognitive impairment manifestation are discussed. There is data that ApoE ε4 haplotype is associated with increase of microglia mediated neuro-inflammation and it can be significant for accelerating of progression of neurodegenerative diseases after COVID-19. Further follow up of the patient is necessary for determination of nosological diagnosis explaining manifested predominantly amnestic type pre-mild cognitive impairment.

Prediction of Neurological Disorders Using Visual Saliency

2021 ◽  
pp. 1-11
Author(s):  
Sreelakshmi S. ◽  
Anoop V. S.
Keyword(s):  
Neurological Disorders ◽  
Early Stage ◽  
Brain Magnetic Resonance Imaging ◽  
Visual Saliency ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Magnetic Resonance Imaging Mri ◽  
Processing Techniques ◽  
The Brain ◽  
Prompt Diagnosis

Neurological disorders are diseases of the central and peripheral nervous system and most commonly affect middle- or old-age people. Accurate classification and early-stage prediction of such disorders are very crucial for prompt diagnosis and treatment. This chapter discusses a new framework that uses image processing techniques for detecting neurological disorders so that clinicians prevent irreversible changes that may occur in the brain. The newly proposed framework ensures reliable and accurate machine learning techniques using visual saliency algorithms to process brain magnetic resonance imaging (MRI). The authors also provide ample hints and dimensions for the researchers interested in using visual saliency features for disease prediction and detection.

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