Automated alignment of MRI brain scan by anatomic landmarks

2009 ◽  
Author(s):  
Li Zhang ◽  
Qing Xu ◽  
Chong Chen ◽  
Carol L. Novak
Keyword(s):  
Anatomic Landmarks ◽  
Mri Brain ◽  
Brain Scan ◽  
Automated Alignment

Clinical and etiological evaluation of secondary seizures by computed tomography and electroencephalogram

2016 ◽  
Vol 5 (10) ◽  
pp. 4982
Author(s):  
Archana Aher* ◽  
Satish Gore
Keyword(s):  
Meta Analysis ◽  
Temporal Region ◽  
Government Medical College ◽  
Occipital Region ◽  
Parietal Region ◽  
Medical College ◽  
Mri Brain ◽  
Brain Scan ◽  
Ct Brain ◽  
Clonic Convulsions

This study was conducted to determine the clinical evaluation and various etiological factors of secondary seizures in patients admitted to Government Medical College, Nagpur. We evaluated 58 patients of secondary seizures from Dec 2011 to Oct 2013. Secondary seizures were defined as case of seizure with CT (brain) or MRI (brain) abnormality1. Out of 58 cases 35 were males and 23 were females. Mean age of study subjects was 34.85. The commonest presenting feature was generalized tonic clonic convulsions (42 patients) followed by focal seizures (16 patients).  Todd’s palsy was observed in 4 cases. Aura was present in 24 cases. According to CT brain scan the aetiology was – neurocysticercosis (34.48%), post stroke (27.59%), tuberculoma (24.14%). Space occupying lesions(SOLs) were present in 8 patients, out of whom 4 had brain tumour, 2 patients had brain abscess, 1 had hydatid cyst and 1 had metastasis. Majority of lesions were located in frontal region (58.62%), followed by in parietal region (44.83%), in temporal region (25.86%) and in occipital region (13.79 % patients). In our study neurocysticercosis was found to be the commonest cause of secondary seizures. As in a meta-analysis it was found that cysticidal drugs result in better outcome in patients of neurocysticecosis, we recommend that the patients of secondary seizures should be identified for the aetiology and treated at the earliest2.

scholarly journals CT/MRI brain scan findings: Significant or not?

2016 ◽  
Vol 1 (2) ◽  
pp. 25-25
Author(s):  
Cesar M. Limjoco
Keyword(s):  
Mri Brain ◽  
Brain Scan

scholarly journals MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1033
Author(s):  
Ali M. Hasan ◽  
Hamid A. Jalab ◽  
Rabha W. Ibrahim ◽  
Farid Meziane ◽  
Ala’a R. AL-Shamasneh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Short Term Memory ◽  
Local Binary Patterns ◽  
Quantum Entropy ◽  
Brain Scans ◽  
Quantum Calculus ◽  
Mri Brain ◽  
Brain Scan ◽  
The Brain

Brain tumor detection at early stages can increase the chances of the patient’s recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy difference defined in terms of Marsaglia formula (usually used to describe two different figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP–DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification.

scholarly journals Adult onset recurrent seizures as the first presentation of primary hypoparathyroidism

F1000Research ◽  
2012 ◽  
Vol 1 ◽  
pp. 51
Author(s):  
Pamela Correia ◽  
Rajeev Ranjan ◽  
Chandrashekhar Agrawal
Keyword(s):  
Previous History ◽  
Serum Parathyroid Hormone ◽  
Mri Brain ◽  
Vitamin D Levels ◽  
Brain Scan ◽  
Phenytoin Sodium ◽  
Metabolic Conditions ◽  
History Of ◽  
25 Oh Vitamin D ◽  
First Time

Introduction: Hypoparathyroidism leading to hypocalcemia is an important treatable cause of recurrent seizures. Primary hypoparathyroidism presenting for the first time as seizures in adulthood is quite infrequent. Patients may present with hypocalcemic seizures even in the absence of subtle hypocalcemic signs.Case report: A 30 year old male, was presented to the emergency facility in an unconscious condition. He was intubated on the way to the hospital as he had suffered from two episodes of ventricular tachycardia. He had previous history of recurrent seizures for 6 years inspite of multiple anticonvulsants including phenytoin sodium, sodium valproate, and levetiracetam. The seizure frequency increased in the last year and he would have 5-6 episodes/ month. A MRI brain scan and EEG at the onset were both normal, as was the general examination but he had history of bilateral cataracts. There were no signs of tetany. Investigations revealed a normal hemoglobin and glucose level with normal electrolytes and both TLC and DLC levels were also normal. He had a serum calcium level of 3.3 mg% with a serum parathyroid hormone level of 1pg/ml, serum 25(OH) vitamin D levels of 6.6ng/ml and hypomagnesemia. NCCT head scan showed bilateral basal ganglia, and deep white matter calcification.Conclusions: 1) Ironically, increasing reliance on high end investigations such as a MRI brain scan could lead to certain conditions being missed; conditions that could be easily identifiable by the humble CT scan. 2) All treatable metabolic conditions should be excluded at first before commencing with anticonvulsants; this will restrict patients from burdensome polytherapy and related side effects.

scholarly journals Moving from neurodegenerative dementias, to cognitive proteinopathies, replacing “where” by “what”…

2020 ◽  
Vol 14 (3) ◽  
pp. 237-242
Author(s):  
Ricardo Francisco Allegri
Keyword(s):  
Molecular Mechanisms ◽  
Buenos Aires ◽  
Amyloid Pet ◽  
Amyloid A ◽  
Mri Brain ◽  
Brain Scan ◽  
Altered Proteins ◽  
Neurodegenerative Dementias ◽  
T Tau ◽  
Abnormal Tissue

ABSTRACT. Neurodegenerative dementias have been described based on their phenotype, in relation to selective degeneration occurring in a particular neuroanatomical system. More recently however, the term proteinopathy has been introduced to describe diseases in which one or more altered proteins can be detected. Neurodegenerative diseases can be produced by more than one abnormal protein and each proteinopathy can determine different clinical phenotypes. Specific biomarkers have now been linked to certain molecular pathologies in live patients. In 2016, a new biomarker-based classification, currently only approved for research in Alzheimer’s disease, was introduced. It is based on the evaluation three biomarkers: amyloid (A) detected on amyloid-PET or amyloid- beta 42 assay in CSF; tau (T) measured in CSF as phosphorylated tau or on tau PET imaging; and neuronal injury/neurodegeneration (N), detected by total T-tau in CSF, FDG PET hypometabolism and on MRI brain scan. Results of clinical research using the ATN biomarkers at FLENI, a Neurological Institute in Buenos Aires, Argentina have, since 2011, contributed to ongoing efforts to move away from the concept of neurodegenerative dementias and more towards one of cognitive proteinopathies. Today, clinical diagnosis in dementia can only tell us “where” abnormal tissue is found but not “what” molecular mechanisms are involved.

Neuropsychological deficits and morphological MRI brain scan abnormalities in apparently healthy non-encephalopathic patients with cirrhosis

1989 ◽  
Vol 9 (3) ◽  
pp. 319-325 ◽  
Author(s):  
J.W. Moore ◽  
A.A. Dunk ◽  
J.R. Crawford ◽  
H. Deans ◽  
J.A.O. Besson ◽  
...  
Keyword(s):  
Neuropsychological Deficits ◽  
Mri Brain ◽  
Brain Scan ◽  
Apparently Healthy
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