scholarly journals Magnetic Resonance Imaging Brain and Electroencephalogram (EEG) in the Evaluation of New-Onset Seizures in a Tertiary Care Centre of Nepal

2021 ◽  
Vol 10 (2) ◽  
pp. 67-70
Author(s):  
BR Pokharel ◽  
P Upadhaya ◽  
GR Sharma ◽  
SJ Budathoki ◽  
AMS Maharjan ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Tertiary Care ◽  
Resonance Imaging ◽  
Tertiary Care Centre ◽  
Mri Brain ◽  
Epileptogenic Lesion ◽  
First Onset ◽  
Magnetic Resonance Imaging Mri ◽  
New Onset

Introduction: Seizure is a common neurological condition with multiple etiological factors. This study aims to evaluate the role of magnetic resonance imaging (MRI) Brain and electroencephalography (EEG) in the diagnosis of new-onset seizures in the Nepalese population. Methods: A total of 106 patients aged between 7 to 85 years of age with first onset seizure, who underwent MRI and EEG were enrolled in the study. The sensitivity of MRI and EEG for the diagnosis of seizure when used in combination was compared with that of MRI or EEG alone. Results: Out of 106 patients, 58.5% (n=62) were males and 41.5% (n=44) were females. In 52.8% (n= 56) of the patients, there was epileptogenic lesion in MRI, and 39.6% (n=42) of the patients had an abnormal EEG. The combination of MRI with EEG was significantly better than either MRI or EEG alone in the diagnosis of seizures (p <0.001). Conclusion: MRI and EEG are frequently used for the evaluation of seizures. MRI Brain when used in combination with EEG significantly improves the diagnostic accuracy of seizures.

scholarly journals Development of a Logic Model for a Programme to Reduce the Magnetic Resonance Imaging Rate for Non-Specific Lower Back Pain in a Tertiary Care Centre

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 238
Author(s):  
Ahmed Alhowimel ◽  
Faris Alodaibi ◽  
Mazyad Alotaibi ◽  
Dalyah Alamam ◽  
Hana Alsobayel ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Back Pain ◽  
Magnetic Resonance ◽  
Lower Back Pain ◽  
Tertiary Care ◽  
Logic Model ◽  
Resonance Imaging ◽  
Tertiary Care Centre ◽  
Additional Time ◽  
Lower Back

Tertiary care centres continue to experience over-utilisation of diagnostic imaging services for lower back pain cases that may not be required. Moreover, these services may require additional time and consequently delay access to services that offer conservative management, i.e., physiotherapy, and hence, increase the direct and indirect costs with no added quality of care. A logic model was developed based on qualitative and quantitative studies that explains the plan and process evaluation strategies to reduce imaging for lower back pain in tertiary hospitals. Logic models are useful tools for defining programme components. The delivery of the components is ensured by well-defined process evaluations that identify any needed modifications. The proposed logic model provides a road map for spine clinics in tertiary care hospitals to decrease the number of patient referrals for magnetic resonance imaging and waiting times for consultations and services and promote early access to physiotherapy services.

scholarly journals Role of Magnetic Resonance Imaging and Spinal Tap in Early Diagnosis of Tuberculous Meningitis

2019 ◽  
Vol 8 (3) ◽  
pp. 127-130
Author(s):  
Salma Haji
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Early Diagnosis ◽  
Tuberculous Meningitis ◽  
Stage I ◽  
Resonance Imaging ◽  
Mri Brain ◽  
Csf Analysis ◽  
Magnetic Resonance Imaging Mri

Background: Tuberculous meningitis (TBM) is difficult to diagnose in early stages due to nonspecific symptoms. There should be high index of suspicion to diagnose TBM at an early stage. The objective of the study was to find out the role of magnetic resonance imaging (MRI) and spinal tap in early diagnosis of tuberculous meningitis. Material and Methods: A cross sectional study was conducted from July 2015 till July 2018 at Neuromedicine ward, Jinnah Postgraduate Medical Centre (JPMC), Karachi. All patients above 12 year of age, both male and female with nonspecific symptoms like headache, malaise and drowsiness or suspicion of TBM (stage I, II, and III according to British Medical Research Council TBM staging criteria) were included in the study. Patients diagnosed with other CNS disease like encephalitis, malaria and acute bacterial meningitis were excluded. Magnetic Resonance Imaging (MRI) of the brain and early spinal tap for cerebrospinal fluid (CSF) analysis were used to diagnose TBM and findings were noted. Results of MRI and CSF analysis were analyzed by SPSS version 24. Results: A total of 110 patients of TBM, with 60 (54.5%) male and 50 (45.5%) female patients were included in the study. Most of the patients belonged to a younger age group of 12-40 years (81.8%), while 18.2% were above 40 years of age. About 90% patients were diagnosed in stage I TBM and 10% in stage II and III. MRI brain findings included meningeal enhancement (60%), hydrocephalus (41.81%) cerebral edema (82.73%), tuberculoma (19%) and infarct (14.5%), respectively. CSF analysis showed low protein in 80%, low glucose in 91.8% and lymphocytic pleocytosis in 97.2%, respectively. Conclusion: Both MRI brain and spinal tap with CSF analysis played a role in the early diagnosis of TBM, which is important to prevent the lethal complications associated with late diagnosis of this disease.

Application of Artificial Intelligence in Magnetic Resonance Imaging to reduce Gadolinium accumulation in patients with multiple sclerosis: A Review

2021 ◽  
Vol 1 (4) ◽  
pp. 416-428
Author(s):  
Vijay Anant Athavale ◽  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Artificial Intelligence ◽  
Multiple Sclerosis ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Mri Scans ◽  
The Central Nervous System ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
New Onset

Gadolinium (Gd) is a based contrast agent is used for Magnetic Resonance Imaging (MRI). In India, gadobutrolhas been is approved for MRI of the Central Nervous System (CNS), liver, kidneys, and breast. It has been noted in several studies that the accumulation of gadolinium occurs in different structures in the brain. Patients with Multiple Sclerosis (MS) are regularly followed up with MRI scans and MRI with contrast enhancement is the most common method of distinguishing new-onset pathological changes. Developments in technology and methods in artificial intelligence have shown that there is reason to map out the X-ray technician’s work with examinations and medicines administered to patients may be altered to prevent the accumulation of gadolinium.

STUDY OF STAGING OF MAGNETIC RESONANCE IMAGING IN AVASCULAR NECROSIS OF FEMORAL HEAD

2021 ◽  
pp. 74-75
Author(s):  
Vinaychandra Sulgante ◽  
Vijaykumar S Mane
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Femoral Head ◽  
Avascular Necrosis ◽  
Imaging Modality ◽  
Tertiary Care ◽  
Resonance Imaging ◽  
Tertiary Care Centre ◽  
Classi Cation ◽  
Necrosis Of Femoral Head

Avascular Necrosis refers to the bone death in epiphyseal or subarticular location secondary to interruption of blood supply. MRI has become the most sensitive, specic and widely used diagnostic imaging modality for evaluation of AVN of femoral head. The aim was to study the stages of presentation of Avascular Necrosis of femoral head on Magnetic Resonance Imaging in tertiary care centre .It is a Hospital record based descriptive study carried out at the Department of Radio-diagnosis, those patients which showed signs for avascular necrosis of femoral head over a period of three years were included in the study. Staging of Avascular Necrosis of femoral head was done as per Ficat and Arlet classication and distribution was done on the basis of Age, Gender and Laterality. Out of 129 patients with suspected AVN, 68 patients showed features of AVN of femoral head on MRI .It was found that males were commonly affected with mean age group between 31-40 years. Most of the patients had bilateral involvement. It was found that most of the patients presented during stage III of the disease. It was also observed that Coronal sequence of T1w image was useful in diagnosing most cases of AVN and hence can be used as a rapid protocol in diagnosing AVN.

Percutaneous Drainage and Serial Magnetic Resonance Imaging in the Diagnosis of Symptomatic Posttraumatic Syringomyelia: Case Report and Review of the Literature

Neurosurgery ◽  
1991 ◽  
Vol 29 (3) ◽  
pp. 429-434 ◽  
Author(s):  
Robert Levy ◽  
Szymon Rosenblatt ◽  
Eric Russell
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Percutaneous Drainage ◽  
Resonance Imaging ◽  
Posttraumatic Syringomyelia ◽  
Magnetic Resonance Imaging Mri ◽  
High Cervical ◽  
Serial Mri ◽  
New Onset

Abstract A patient with high cervical tetraplegia with new-onset headaches and posttraumatic syringomyelia is presented. Percutaneous drainage of the syrinx resulted in a resolution of the headaches and collapse of the syrinx on follow-up magnetic resonance imaging (MRI). The return of the symptoms correlated with the re-expansion of the syrinx on MRI. The patient underwent syringopleural shunting with persistent resolution of the symptoms and collapse of the syrinx on MRI. The value of percutaneous drainage and serial MRI to determine the clinical significance of posttraumatic syringomyelia is discussed.

Further Magnetic Resonance Imaging (MRI) Brain Delineation of 49,XXXXY Syndrome

2011 ◽  
Vol 27 (5) ◽  
pp. 650-653 ◽  
Author(s):  
Brahim Tabarki ◽  
Shatha Al Shafi ◽  
Nawal Al Adwani ◽  
Saad Al Shahwan
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Mri Brain ◽  
Magnetic Resonance Imaging Mri

scholarly journals Classification on Magnetic Resonance Imaging (MRI) Brain Tumour using BPNN, SVM and CNN

2019 ◽  
Vol 8 (3) ◽  
pp. 8601-8607
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Image Segmentation ◽  
Magnetic Resonance ◽  
Brain Tumour ◽  
Support Vector ◽  
Low Grade ◽  
Resonance Imaging ◽  
Mri Brain ◽  
Magnetic Resonance Imaging Mri

In this works, the main objective is to detect the high grade gliomas (HGG) and low grade gliomas (LGG) from Magnetic Resonance Imaging (MRI) Brain Tumour images by applying the efficient image segmentation and classify among them. So hybrid image segmentation techniques applied in this work, first one is canny edge detection which is used to locate the boundary of the image and second is fuzzy c-mean clustering which is used to clubbed together of the similarity intensity value into clusters. Also further eight feature extracted using Intensity based Histogram and GrayLevel Co-occurrence Matrix (GLCM). Now three classifiers learning algorithm applied in this system, first one is backpropogation neural network (BPNN) which consists of multi-layer perceptrons to solve the complex problem for the given inputs. Second one is convolution neural network (CNN) are the part of neural networks which have very effective in areas such as image recognition and image classification. Third is Support vector machine (SVM) which can be used for both classification and regression challenges. Each of one is evaluated performance based on different techniques. It found that SVM and CNN gives 88% accuracy for this work.

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