Streptococcus pneumoniae Meningoencephalitis With Bilateral Basal Ganglia Necrosis

Streptococcus pneumoniae ( S pneumoniae) is a common cause of bacterial meningitis, frequently leading to death or severe neurological impairment. We report an exceptional case of a 4-month-old boy with meningitis caused by S pneumoniae. Computed tomography (CT) and magnetic resonance imaging (MRI) examinations of the brain showed bilateral symmetrical necrosis involving the lentiform and caudate nuclei, as well as the thalamus. T1-weighted MR images showed patchy areas of increased signal intensity, consistent with hemorrhagic transformation of the lesions. Autopsy revealed widespread necrosis of the basal ganglia with clear signs of vasculitis. Severe complications of S pneumoniae meningoencephalitis are known in infants but to our knowledge, such lesions in the basal ganglia have only been reported thrice in adults and never in children.

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Paroxysmal Autonomic Instability with Dystonia after Pneumococcal Meningoencephalitis

Case Reports in Medicine ◽

10.1155/2012/965932 ◽

2012 ◽

Vol 2012 ◽

pp. 1-4 ◽

Cited By ~ 8

Author(s):

Layal Safadieh ◽

Rana Sharara-Chami ◽

Omar Dabbagh

Keyword(s):

Traumatic Brain Injury ◽

Nervous System ◽

Streptococcus Pneumoniae ◽

Basal Ganglia ◽

Bacterial Meningitis ◽

Neurological Impairment ◽

Common Cause ◽

First Case ◽

Autonomic Instability ◽

Severe Neurological Impairment

Streptococcus pneumoniaeis a common cause of bacterial meningitis, frequently resulting in severe neurological impairment. A seven-month-old child presenting withStreptococcus pneumoniaemeningoencephalitis developed right basal ganglia and hypothalamic infarctions. Daily episodes of agitation, hypertension, tachycardia, diaphoresis, hyperthermia, and decerebrate posturing were observed. The diagnosis ofparoxysmal autonomic instability with dystoniawas established. The patient responded to clonidine, baclofen, and benzodiazepines. Although this entity has been reported in association with traumatic brain injury, and as a sequel to some nervous system infections, this is the first case, to our knowledge, associated with pneumococcal meningoencephalitis.

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A Review on Some Classification Methods of Brain Tumor MRI Datasets

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2062114 ◽

2020 ◽

pp. 421-424

Author(s):

Nirmal Mungale ◽

Snehal Kene ◽

Amol Chaudhary

Keyword(s):

Brain Tumor ◽

Brain Mri ◽

Mr Images ◽

Classification Techniques ◽

Abnormal Growth ◽

Life Threatening ◽

Magnetic Resonance Imaging Mri ◽

Recent Classification ◽

The Brain

Brain tumor is a life-threatening disease. Brain tumor is formed by the abnormal growth of cells inside and around the brain. Identification of the size and type of tumor is necessary for deciding the course of treatment of the patient. Magnetic Resonance Imaging (MRI) is one of the methods for detection of tumor in the brain. The classification of MR Images is a difficult task due to variety and complexity of brain tumors. Various classification techniques have been identified for brain MRI tumor images. This paper reviews some of these recent classification techniques.

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Variable MR and pathologic patterns of hemorrhage after iodinated contrast infusion in MCA occlusion/reperfusion model

Journal of NeuroInterventional Surgery ◽

10.1136/neurintsurg-2016-012777 ◽

2016 ◽

Vol 9 (12) ◽

pp. 1248-1252 ◽

Cited By ~ 3

Author(s):

Humberto Morales ◽

Aigang Lu ◽

Yuko Kurosawa ◽

Joseph F Clark ◽

Thomas Tomsick

Keyword(s):

Area Under The Curve ◽

Hemorrhagic Transformation ◽

Brain Parenchyma ◽

Mr Images ◽

Exact Test ◽

Iodinated Contrast ◽

Receiver Operator Curve Analysis ◽

Radiographic Contrast Media ◽

The Brain ◽

Operator Curve

ObjectiveTo examine the hypothesis that IA reperfusion with iso-osmolar iodixanol, low-osmolar iopamidol, or saline causes different effects on MR signal changes and pathologic cut-brain section related to hemorrhagic transformation (HT) or iodinated radiographic contrast media (IRCM) deposition.MethodsInfarct was induced in 30 rats by middle cerebral artery suture occlusion. Reperfusion was performed after 5 hours with iso-osmolar iodixanol (n=9), low-osmolar iopamidol (n=12) or saline (n=9). MR images were obtained immediately after reperfusion and rats were sacrificed at 24 hours. Hypointense areas within the infarction on T2-weighted (T2-WI) or gradient echo (GRE) images were recorded and compared with HT on pathology. Fisher's exact test was used for proportions, and receiver operator curve analysis to evaluate MRI discrimination of hemorrhage.ResultsTwo types of HT were noted on pathology: confluent >0.2 mm petechial hemorrhage (PeH, 78%) or well-defined ≤0.2 mm hemorrhagic focus (HF, 22%). PeH was least common in the iodixanol subgroup (p<0.02). HF was more common in the IRCM group. Hypointense areas on T2-WI but not on GRE were significantly more common in the IRCM group (p<0.05). Hypointense areas on T2-WI and GRE discriminated HT (area under the curve: 0.714, p<0.002).ConclusionsIRCM and saline induced different MRI signal and pathologic patterns in our sample. We postulate that T2 hypointensity with no GRE hypointensity might be associated with IRCM deposition; and decreased frequency of PeH after iodixanol infusion and the presence of HF almost exclusively in the IRCM group might represent a direct/indirect effect of contrast infusion/deposition in the brain parenchyma after reperfusion. Our results support previous observations in IMS III and are hypothesis generating.

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Development of a Convolutional Neural Network Based Skull Segmentation in MRI Using Standard Tesselation Language Models

Journal of Personalized Medicine ◽

10.3390/jpm11040310 ◽

2021 ◽

Vol 11 (4) ◽

pp. 310

Author(s):

Rodrigo Dalvit Carvalho da Silva ◽

Thomas Richard Jenkyn ◽

Victor Alexander Carranza

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Language Models ◽

Dice Similarity Coefficient ◽

Imaging Analysis ◽

Resonance Imaging ◽

Mr Images ◽

Magnetic Resonance Imaging Mri ◽

3D Cnn ◽

The Brain

Segmentation is crucial in medical imaging analysis to help extract regions of interest (ROI) from different imaging modalities. The aim of this study is to develop and train a 3D convolutional neural network (CNN) for skull segmentation in magnetic resonance imaging (MRI). 58 gold standard volumetric labels were created from computed tomography (CT) scans in standard tessellation language (STL) models. These STL models were converted into matrices and overlapped on the 58 corresponding MR images to create the MRI gold standards labels. The CNN was trained with these 58 MR images and a mean ± standard deviation (SD) Dice similarity coefficient (DSC) of 0.7300 ± 0.04 was achieved. A further investigation was carried out where the brain region was removed from the image with the help of a 3D CNN and manual corrections by using only MR images. This new dataset, without the brain, was presented to the previous CNN which reached a new mean ± SD DSC of 0.7826 ± 0.03. This paper aims to provide a framework for segmenting the skull using CNN and STL models, as the 3D CNN was able to segment the skull with a certain precision.

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A three-dimensional histological atlas of the human basal ganglia. II. Atlas deformation strategy and evaluation in deep brain stimulation for Parkinson disease

Journal of Neurosurgery ◽

10.3171/2008.3.17469 ◽

2009 ◽

Vol 110 (2) ◽

pp. 208-219 ◽

Cited By ~ 72

Author(s):

Eric Bardinet ◽

Manik Bhattacharjee ◽

Didier Dormont ◽

Bernard Pidoux ◽

Grégoire Malandain ◽

...

Keyword(s):

Deep Brain Stimulation ◽

Basal Ganglia ◽

Parkinson Disease ◽

Brain Stimulation ◽

Magnetic Resonance Images ◽

Mr Images ◽

Registration Method ◽

Automated Method ◽

The Brain ◽

Deep Brain

Object The localization of any given target in the brain has become a challenging issue because of the increased use of deep brain stimulation to treat Parkinson disease, dystonia, and nonmotor diseases (for example, Tourette syndrome, obsessive compulsive disorders, and depression). The aim of this study was to develop an automated method of adapting an atlas of the human basal ganglia to the brains of individual patients. Methods Magnetic resonance images of the brain specimen were obtained before extraction from the skull and histological processing. Adaptation of the atlas to individual patient anatomy was performed by reshaping the atlas MR images to the images obtained in the individual patient using a hierarchical registration applied to a region of interest centered on the basal ganglia, and then applying the reshaping matrix to the atlas surfaces. Results Results were evaluated by direct visual inspection of the structures visible on MR images and atlas anatomy, by comparison with electrophysiological intraoperative data, and with previous atlas studies in patients with Parkinson disease. The method was both robust and accurate, never failing to provide an anatomically reliable atlas to patient registration. The registration obtained did not exceed a 1-mm mismatch with the electrophysiological signatures in the region of the subthalamic nucleus. Conclusions This registration method applied to the basal ganglia atlas forms a powerful and reliable method for determining deep brain stimulation targets within the basal ganglia of individual patients.

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Neuro-Enteric Dysfunction in Children with Severe Neurological Impairment

Malaysian Journal of Paediatrics and Child Health ◽

10.51407/mjpch.v25i1.25 ◽

2019 ◽

Vol 25 (1) ◽

pp. 1-5

Author(s):

Fahisham Taib ◽

Nik Zainal Abidin Nik Ismail

Keyword(s):

Holistic Approach ◽

Neurological Impairment ◽

Gastrointestinal Diseases ◽

Visceral Hyperalgesia ◽

Gastrointestinal Pathology ◽

Physiologic Mechanism ◽

Impaired Children ◽

The Brain ◽

Severe Neurological Impairment ◽

Neurological System

Visceral hyperalgesia, intestinal dysfunction and unexplained irritability in neurological impaired children is a medical enigma for many healthcare professionals. The neuro-medical management can be challenging and difficult, due to poor understanding of the underlying aetiology and pathophysiology of the condition. Neuro-enteric axis has been proposed as emerging physiologic mechanism in the pathogenesis of many gastrointestinal diseases. The bidirectional connection between enteric and central nervous system may represent a direct relationship between neurological system and gut physiology. Insult to the brain indirectly contribute to the ongoing gut and brain axis sequalae. Microbiota has been an important modulator in the brain-gut axis. Irritability episodes in severe neurological impairment children has been commonly associated with pain originated from gastrointestinal pathology. Management of such condition requires a holistic approach to tackle multidimensional factors that has contributed to the ‘totality’ of the symptoms.

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A Robust Method for MR Image Segmentation and Multiple Scleroses Detection

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2019.2690 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1119-1130

Author(s):

H. Zouaoui ◽

A. Moussaoui ◽

M. Oussalah ◽

A. Taleb-Ahmed

Keyword(s):

White Matter ◽

Automatic Segmentation ◽

Fuzzy Model ◽

Mr Images ◽

New Approach ◽

Magnetic Resonance Imaging Mri ◽

Mr Image Segmentation ◽

The Brain

In the present article, we propose a new approach for the segmentation of the MR images of the Multiple Sclerosis (MS). The Magnetic Resonance Imaging (MRI) allows the visualization of the brain and it is widely used in the diagnosis and the follow-up of the patients suffering from MS. Aiming to automate a long and tedious process for the clinician, we propose the automatic segmentation of the MS lesions. Our algorithm of segmentation is composed of three stages: segmentation of the brain into regions using the algorithm Fuzzy Particle Swarm Optimization (FPSO) in order to obtain the characterization of the different healthy tissues (White matter, grey matter and cerebrospinal fluid (CSF)) after the extraction of white matter (WM), the elimination of the atypical data (outliers) of the white matter by the algorithm Fuzzy C-Means (FCM), finally, the use of a Mamdani-type fuzzy model to extract the MS lesions among all the absurd data.

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Automated Detection and Classification of Meningioma Tumor from MR Images Using Sea Lion Optimization and Deep Learning Models

Axioms ◽

10.3390/axioms11010015 ◽

2021 ◽

Vol 11 (1) ◽

pp. 15

Author(s):

Aswathy Sukumaran ◽

Ajith Abraham

Keyword(s):

Brain Tumors ◽

Optimization Methods ◽

Mr Images ◽

Automated Identification ◽

Non Invasive ◽

Life Threatening ◽

Magnetic Resonance Imaging Mri ◽

Experimental Findings ◽

The Brain

Meningiomas are the most prevalent benign intracranial life-threatening brain tumors, with a life expectancy of a few months in the later stages, so this type of tumor in the brain image should be recognized and detected efficiently. The source of meningiomas is unknown. Radiation exposure, particularly during childhood, is the sole recognized environmental risk factor for meningiomas. The imaging technique of magnetic resonance imaging (MRI) is commonly used to detect most tumor forms as it is a non-invasive and painless method. This study introduces a CNN-HHO integrated automated identification model, which makes use of SeaLion optimization methods for improving overall network optimization. In addition to these techniques, various CNN models such as Resnet, VGG, and DenseNet have been utilized to give an overall influence of CNN with SeaLion in each methodology. Each model is tested on our benchmark dataset for accuracy, specificity, dice coefficient, MCC, and sensitivity, with DenseNet outperforming the other models with a precision of 98%. The proposed methods outperform existing alternatives in the detection of brain tumors, according to the existing experimental findings.

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Central Nerve System Impairment, AMA Guides, Sixth Edition: An Overview

Guides Newsletter ◽

10.1001/amaguidesnewsletters.2018.janfeb03 ◽

2018 ◽

Vol 23 (1) ◽

pp. 10-13

Author(s):

James B. Talmage ◽

Jay Blaisdell

Keyword(s):

Spinal Cord ◽

Central Nervous System ◽

Nervous System ◽

Neurological Impairment ◽

Sixth Edition ◽

Central Nerve System ◽

The Central Nervous System ◽

The One ◽

Nerve System ◽

The Brain

Abstract Injuries that affect the central nervous system (CNS) can be catastrophic because they involve the brain or spinal cord, and determining the underlying clinical cause of impairment is essential in using the AMA Guides to the Evaluation of Permanent Impairment (AMA Guides), in part because the AMA Guides addresses neurological impairment in several chapters. Unlike the musculoskeletal chapters, Chapter 13, The Central and Peripheral Nervous System, does not use grades, grade modifiers, and a net adjustment formula; rather the chapter uses an approach that is similar to that in prior editions of the AMA Guides. The following steps can be used to perform a CNS rating: 1) evaluate all four major categories of cerebral impairment, and choose the one that is most severe; 2) rate the single most severe cerebral impairment of the four major categories; 3) rate all other impairments that are due to neurogenic problems; and 4) combine the rating of the single most severe category of cerebral impairment with the ratings of all other impairments. Because some neurological dysfunctions are rated elsewhere in the AMA Guides, Sixth Edition, the evaluator may consult Table 13-1 to verify the appropriate chapter to use.

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