scholarly journals Identifying Type 2 Diabetic Brains by Investigating Disease-Related Structural Changes in Magnetic Resonance Imaging

2021 ◽  
Vol 15 ◽  
Author(s):  
Yuna Chen ◽  
Yongsheng Pan ◽  
Shangyu Kang ◽  
Junshen Lu ◽  
Xin Tan ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Structure ◽  
Brain Mri ◽  
Brain Diseases ◽  
Imaging Biomarkers ◽  
Resonance Imaging ◽  
High Blood Glucose ◽  
The Brain

Diabetes with high blood glucose levels may damage the brain nerves and thus increase the risk of dementia. Previous studies have shown that dementia can be reflected in altered brain structure, facilitating computer-aided diagnosis of brain diseases based on structural magnetic resonance imaging (MRI). However, type 2 diabetes mellitus (T2DM)-mediated changes in the brain structures have not yet been studied, and only a few studies have focused on the use of brain MRI for automated diagnosis of T2DM. Hence, identifying MRI biomarkers is essential to evaluate the association between changes in brain structure and T2DM as well as cognitive impairment (CI). The present study aims to investigate four methods to extract features from MRI, characterize imaging biomarkers, as well as identify subjects with T2DM and CI.

Safety and Feasibility of Magnetic Resonance Imaging of the Brain at 1.5 Tesla in Patients with Temporary Transmyocardial Pacing Leads

2017 ◽  
Vol 67 (02) ◽  
pp. 086-091 ◽  
Author(s):  
Rami Homsi ◽  
Fritz Mellert ◽  
Roger Luechinger ◽  
Daniel Thomas ◽  
Jonas Doerner ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Troponin I ◽  
Brain Mri ◽  
Maximum Temperature ◽  
Safe Procedure ◽  
Resonance Imaging ◽  
The Brain ◽  
Head Coil ◽  
Pacing Leads

Background Temporary transmyocardial pacing leads (TTPLs) represent an absolute contraindication to magnetic resonance imaging (MRI). The purpose of this study was to evaluate the safety and feasibility of MRI at 1.5 Tesla (T) using a transmit/receive (T/R) head coil in patients with TTPL. Methods TTPLs (220 cm, Osypka TME, Dr. Osypka GmbH, Rheinfelden, Germany) were implanted in a phantom and exposed to conditions of a 1.5 T brain examination using a T/R head coil. Temperature changes at the lead tip were continuously recorded. A total of 28 patients with TTPL and an urgent indication for a brain MRI underwent MRI at 1.5 T with vital sign monitoring. A T/R head coil was used to minimize radiofrequency exposure of the TTPL. Before and immediately after the MRI scan, TTPL lead impedance, pacing capture threshold (PCT), signal slope, and sensing were measured. Serum troponin I was determined before and after MRI to detect thermal myocardial injury. Results In vitro, the maximum temperature increase from radiofrequency-induced heating of the TTPL tip was < 1°C. In vivo, no complications, such as heating sensations, dizziness, unexpected changes in heart rate or rhythm, or other unusual signs or symptoms were observed. No significant changes in the lead impedance, PCT, signal slope, or sensing were recorded. There were no increases of serum troponin I after the MRI examination. Conclusions MRI of the brain may be performed safely at 1.5 T using a T/R head coil in case of an urgent clinical need in patients with TTPL and may be considered a feasible and safe procedure when appropriate precautionary measures are taken.

scholarly journals Clustering of Brain Tumors in Brain MRI Images based on Extraction of Textural and Statistical Features

2020 ◽  
Vol 16 (12) ◽  
pp. 116
Author(s):  
Hamed Samadi Ghoushchi ◽  
Yaghoub Pourasad
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Support Vector ◽  
Statistical Features ◽  
Resonance Imaging ◽  
The Brain

<p>The purpose of this article is to investigate techniques for classifying tumor grade from magnetic resonance imaging (MRI). This requires early diagnosis of the brain tumor and its grade. Magnetic resonance imaging may show a clear tumor in the brain, but doctors need to measure the tumor in order to treat more or to advance treatment. For this purpose, digital imaging techniques along with machine learning can help to quickly identify tumors and also treatments and types of surgery. These combined techniques in understanding medical images for researchers are an important tool to increase the accuracy of diagnosis. In this paper, classification methods for MRI images of tumors of the human brain are performed to review the astrocytoma-containing glands. Methods used to classify brain tumors, including preprocessing, screening, tissue extraction, and statistical features of the tumor using two types of T<sub>1</sub>W and Flair brain MRI images and also the method of dimensionality reduction of extracted features and how to train them in classification are also explained. Determine the tumor area using three classification of Fuzzy Logic <em>C</em><em>-</em><em>Means</em><em> </em>Clustering (FCM), Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM). In this paper, simulated and real MRI images are used. The results obtained from the proposed methods in this paper are compared with the reference results and the results show that the proposed approach can increase the reliability of brain tumor diagnosis.</p>

scholarly journals Current approaches to magnetic resonance imaging diagnosis of epileptogenic and associated lesions of the brain

2018 ◽  
Vol 10 (1S) ◽  
pp. 4-11
Author(s):  
E. M. Perepelova ◽  
V. A. Perepelov ◽  
M. S. Merkulova ◽  
V. E. Sinitsyn
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Structure ◽  
Pulse Sequences ◽  
High Sensitivity ◽  
Resonance Imaging ◽  
Functional Changes ◽  
Associated Lesions ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

With the development of current neuroimaging techniques, their role in diagnosing epilepsy is becoming more significant and that is not only in identifying the disease that plays a key role in  epileptogenesis, but also in assisting a clinician in the subsequent  formulation of the diagnosis, in correcting drug therapy, and, in  some cases, in addressing the issue of surgical treatment in the  patient. The priority technique in this case is magnetic resonance  imaging (MRI) that has high sensitivity and specificity in defining the  location of minor and more major lesions of the brain structure  and that includes a set of current sequences that can obtain  important diagnostic information about the functional state of the  brain. This article highlights the International League Against  Epilepsy guidelines for MRI in patients with suspected epilepsy,  assesses the use of and briefly characterizes both structural and  functional pulse sequences that are most commonly included in the  epileptological protocol. It considers major pathological processes  and evaluates anatomical and functional changes in the brain  structure, which play an important role in epileptogenesis.

Outpatient Procedural Sedation of Patients With Autism Spectrum Disorders for Magnetic Resonance Imaging of the Brain Using Propofol

2018 ◽  
Vol 33 (5) ◽  
pp. 313-319 ◽  
Author(s):  
Pradip P. Kamat ◽  
Marie K. Karaga ◽  
Benjamin L. Wisniewski ◽  
Courtney E. McCracken ◽  
Harold K. Simon ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Mri ◽  
Autism Spectrum ◽  
Resonance Imaging ◽  
Serious Adverse Events ◽  
Children With Asd ◽  
Spectrum Disorders ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Objective: To quantify the number of personnel, time to induce and complete sedation using propofol for outpatient magnetic resonance imaging (MRI) of the brain, and the frequency of serious adverse events (SAEs) in children with autism spectrum disorder (ASD) compared with children without ASD. Results: Baseline characteristics were the same between both groups. Overall sedation success was 99%. Although most children were sedated with ≤3 providers, 10% with ASD needed ≥4 providers (P = .005). The duration of sedation was less for the ASD group compared with the non-ASD group (49 minutes vs 56 minutes, P = .005). There was no difference in SAE frequency between groups (ASD 14% vs non-ASD 16%, P = .57). Conclusion: Children with ASD can be sedated for brain MRI using propofol with no increased frequency of SAEs compared with children without ASD. Sedation teams should anticipate that 10% of children with ASD may need additional personnel before propofol induction.

Effects of early low-level lead exposure on human brain structure, organization and functions

2010 ◽  
Vol 2 (1) ◽  
pp. 17-24 ◽  
Author(s):  
K. M. Cecil
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Lead Exposure ◽  
Brain Structure ◽  
Diffusion Tensor ◽  
Adult Brain ◽  
Resonance Spectroscopy ◽  
Resonance Imaging ◽  
Environmental Lead ◽  
The Brain

Advanced neuroimaging techniques offer unique insights into how childhood lead exposure impacts the brain. Volumetric magnetic resonance imaging affords anatomical information about the size of global, regional and subcomponent structures within the brain. Diffusion tensor imaging provides information about white matter architecture by quantitatively describing how water molecules diffuse within it. Proton magnetic resonance spectroscopy generates quantitative measures of neuronal, axonal and glial elements via concentration levels of select metabolites. Functional magnetic resonance imaging infers neuronal activity associated with a given task performed. Employing these techniques in the study of the Cincinnati Lead Study, a relatively homogeneous birth cohort longitudinally monitored for over 30 years, one can non-invasively and quantitatively explore how childhood lead exposure is associated with adult brain structure, organization and function. These studies yield important findings how environmental lead exposure impacts human health.

scholarly journals Magnetic resonance imaging biomarkers of exercise-induced improvement of oxidative stress and inflammation in the brain of old high-fat-fed ApoE−/−mice

2016 ◽  
Vol 594 (23) ◽  
pp. 6969-6985 ◽  
Author(s):  
Erica N. Chirico ◽  
Vanessa Di Cataldo ◽  
Fabien Chauveau ◽  
Alain Geloën ◽  
David Patsouris ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Imaging Biomarkers ◽  
Resonance Imaging ◽  
High Fat ◽  
Exercise Induced ◽  
The Brain

scholarly journals Cerebral X-linked adrenoleukodystrophy: follow-up with magnetic resonance imaging

2006 ◽  
Vol 64 (4) ◽  
pp. 1033-1035 ◽  
Author(s):  
Emerson L. Gasparetto ◽  
Juliana Mecunhe Rosa ◽  
Taísa Davaus ◽  
Arnolfo de Carvalho Neto
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Mri ◽  
Resonance Imaging ◽  
Mri Findings ◽  
Magnetic Resonance Imaging Mri ◽  
One Year ◽  
Occipital Lobes ◽  
The Brain

OBJECTIVE: To report a case of childhood cerebral X-linked adrenoleukodystrophy (X-ADL), emphasizing the magnetic resonance imaging (MRI) findings at initial evaluation and at the follow-up. CASE REPORT: Five year-old boy, who was asymptomatic, presented with diagnosis of X-ADL for MRI evaluation. The initial brain MRI showed a focal area of enhancement at the splenium of the corpus calosum. One year later, the follow-up MRI showed a progression of the corpus calosus lesion, as well as other lesions in the parietal and occipital lobes. CONCLUSION: The brain MRI follow-up of patients with X-ADL is important to show the progression of the lesions.

scholarly journals Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 372
Author(s):  
Muhannad Faleh Alanazi ◽  
Muhammad Umair Ali ◽  
Shaik Javeed Hussain ◽  
Amad Zafar ◽  
Mohammed Mohatram ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Brain Mri ◽  
Learning Model ◽  
High Accuracy ◽  
Resonance Imaging ◽  
Deep Learning Model ◽  
The Brain

With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers of isolated convolutional-neural-network (CNN) models are built from scratch to check their performances for brain MRI images. Then, the 22-layer, binary-classification (tumor or no tumor) isolated-CNN model is re-utilized to re-adjust the neurons’ weights for classifying brain MRI images into tumor subclasses using the transfer-learning concept. As a result, the developed transfer-learned model has a high accuracy of 95.75% for the MRI images of the same MRI machine. Furthermore, the developed transfer-learned model has also been tested using the brain MRI images of another machine to validate its adaptability, general capability, and reliability for real-time application in the future. The results showed that the proposed model has a high accuracy of 96.89% for an unseen brain MRI dataset. Thus, the proposed deep-learning framework can help doctors and radiologists diagnose brain tumors early.

Brain magnetic resonance imaging findings in relapsing neuromyelitis optica

2007 ◽  
Vol 13 (2) ◽  
pp. 186-192 ◽  
Author(s):  
José A Cabrera-Gómez ◽  
L Quevedo-Sotolongo ◽  
A González-Quevedo ◽  
S Lima ◽  
Y Real-González ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
White Matter ◽  
Magnetic Resonance ◽  
Neuromyelitis Optica ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Imaging Protocol ◽  
The Brain

Background Some studies showed abnormalities in brain magnetic resonance imaging (MRI) of relapsing neuromyelitis optica (R-NMO) from 12 to 46%. These abnormalities are described as compatible/non-compatible with multiple sclerosis (MS). Objective To describe the abnormal brain MRI lesions in R-NMO with imaging studies conducted with more sensitive white matter change techniques. Methods Thirty patients with R-NMO were selected. All MRI brain studies were performed with a 1.5-T Siemens MRI system according to the Standardized MR Imaging Protocol for Multiple Sclerosis from the Consortium of MS Centers Consensus Guidelines. Results Brain MRI images were evaluated in 29 R-NMO cases because in one case the MRI images were not appropriate for the study. Of these 29 brain MRI studies, 19 cases (65.5%) had at least one or more lesions (1–57) and 10 were negative (34.4%). Brain MRI findings in 19 cases were characterized in T2/fluid-attenuated inversion-recovery (FLAIR) by the presence of subcortical/deep white matter lesions in 16 (84.2%) cases (1–50), most of them < 3 mm and without juxtacortical localization. Periventricular lesions were observed in 13 (68.4%) cases, but morphologically they were not oval, ovoid or perpendicularly orientated. Infratentorial lesions, all >3 mm, were observed in 4 (21.05%) cases without cerebellar involvement. T1 studies demonstrated absence of hypointense regions. Optic nerve enhancement was observed in 6/19 patients (31.5%). None of the brain MRI abnormalities observed were compatible with Barkhof et al. criteria of MS. Conclusions This study, based on a Cuban patient population, with long duration of disease, good sample size and detailed characterization by MRI, demonstrated the brain MRI pattern of R-NMO patients, which is different from MS. Multiple Sclerosis 2007; 13: 186–192. http://msj.sagepub.com

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