scholarly journals Alterações na Ressonância Magnética Preditoras da Conversão da Síndrome Clinicamente Isolada em Esclerose Múltipla

2016 ◽  
Vol 29 (11) ◽  
pp. 742 ◽  
Author(s):  
Sara Peixoto ◽  
Pedro Abreu
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
Magnetic Resonance ◽  
Diagnostic Criteria ◽  
Clinically Isolated Syndrome ◽  
Conventional Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Conventional Magnetic Resonance ◽  
Pubmed Database ◽  
Demyelinating Lesions

Introduction: Clinically isolated syndrome may be the first manifestation of multiple sclerosis, a chronic demyelinating disease of the central nervous system, and it is defined by a single clinical episode suggestive of demyelination. However, patients with this syndrome, even with long term follow up, may not develop new symptoms or demyelinating lesions that fulfils multiple sclerosis diagnostic criteria. We reviewed, in clinically isolated syndrome, what are the best magnetic resonance imaging findings that may predict its conversion to multiple sclerosis.Material and Methods: A search was made in the PubMed database for papers published between January 2010 and June 2015 using the following terms: ‘clinically isolated syndrome’, ‘cis’, ‘multiple sclerosis’, ‘magnetic resonance imaging’, ‘magnetic resonance’ and ‘mri’.Results: In this review, the following conventional magnetic resonance imaging abnormalities found in literature were included: lesion load, lesion location, Barkhof’s criteria and brain atrophy related features. The non conventional magnetic resonance imaging techniques studied were double inversion recovery, magnetization transfer imaging, spectroscopy and diffusion tensor imaging.Discussion: The number and location of demyelinating lesions have a clear role in predicting clinically isolated syndrome conversion to multiple sclerosis. On the other hand, more data are needed to confirm the ability to predict this disease development of non conventional techniques and remaining neuroimaging abnormalities.Conclusion: In forthcoming years, in addition to the established predictive value of the above mentioned neuroimaging abnormalities,different clinically isolated syndrome neuroradiological findings may be considered in multiple sclerosis diagnostic criteria and/or change its treatment recommendations.

Voxelwise analysis of conventional magnetic resonance imaging to predict future disability in early relapsing–remitting multiple sclerosis

2012 ◽  
Vol 18 (11) ◽  
pp. 1585-1591 ◽  
Author(s):  
Delphine Wybrecht ◽  
Françoise Reuter ◽  
Wafaa Zaaraoui ◽  
Anthony Faivre ◽  
Lydie Crespy ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
Magnetic Resonance ◽  
Physical Disability ◽  
Conventional Magnetic Resonance Imaging ◽  
Global Approach ◽  
Resonance Imaging ◽  
Conventional Magnetic Resonance ◽  
Magnetic Resonance Imaging Mri ◽  
Mri Changes

Background: The ability of conventional magnetic resonance imaging (MRI) to predict subsequent physical disability and cognitive deterioration after a clinically isolated syndrome (CIS) is weak. Objectives: We aimed to investigate whether conventional MRI changes over 1 year could predict cognitive and physical disability 5 years later in CIS. We performed analyses using a global approach (T2 lesion load, number of T2 lesions), but also a topographic approach. Methods: This study included 38 patients with a CIS. At inclusion, 10 out of 38 patients fulfilled the 2010 revised McDonald’s criteria for the diagnosis of multiple sclerosis. Expanded Disability Status Scale (EDSS) evaluation was performed at baseline, year 1 and year 5, and cognitive evaluation at baseline and year 5. T2-weighted MRI was performed at baseline and year 1. We used voxelwise analysis to analyse the predictive value of lesions location for subsequent disability. Results: Using the global approach, no correlation was found between MRI and clinical data. The occurrence or growth of new lesions in the brainstem was correlated with EDSS changes over the 5 years of follow-up. The occurrence or growth of new lesions in cerebellum, thalami, corpus callosum and frontal lobes over 1 year was correlated with cognitive impairment at 5 years. Conclusion: The assessment of lesion location at the first stage of multiple sclerosis may be of value to predict future clinical disability.

scholarly journals Demyelinating lesions behaving like aggressive tumours on advanced MRI techniques

2019 ◽  
Vol 32 (2) ◽  
pp. 103-107 ◽  
Author(s):  
Brainner Campos Barbosa ◽  
Edson Marchiori ◽  
Caio Leal Leidersnaider ◽  
Lara Brandao ◽  
Mauricio Castillo
Keyword(s):  
Magnetic Resonance Imaging ◽  
White Matter ◽  
Magnetic Resonance ◽  
High Grade ◽  
Conventional Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
High Permeability ◽  
Conventional Magnetic Resonance ◽  
Demyelinating Lesions ◽  
High Grade Gliomas

Tumefactive demyelinating lesions are a rare disorder in which inflammatory demyelination manifests as solitary or multiple focal brain lesions (greater than 2 cm in size), which can be mistaken for glioma, lymphoma, metastasis and in some cases even brain abscess. The symptomatology of tumefactive demyelinating lesions depends on the white matter area involved and includes quickly progressing neurological deterioration of motor, sensory and visual function, praxis, language and mood impairment, as well as seizures. Recognising the key imaging features in a patient with a prior history of demyelination may expedite appropriate management. Preoperative diagnosis or at least the consideration of a demyelinating process is important to avoid unnecessary surgery. We report three patients with demyelinating lesions who presented with findings suggestive of demyelination on conventional magnetic resonance imaging studies. However, in all patients the lesions showed high perfusion and in two high permeability, which are findings generally seen with high-grade neoplasias. In rare instances, tumefactive demyelinating lesions may show increased perfusion and high permeability, imaging findings more commonly seen in high-grade gliomas. We suggest that if white matter lesions on conventional magnetic resonance imaging are compatible with tumefactive demyelinating lesions, atypical findings of high perfusion/permeability should not dissuade the radiologist from suggesting the presence of tumefactive demyelinating lesions rather than high-grade gliomas.

A Segmentation Method for Multiple Sclerosis White Matter Lesions on Conventional Magnetic Resonance Imaging Based on Kernel Fuzzy Clustering

2013 ◽  
Vol 339 ◽  
pp. 361-365 ◽  
Author(s):  
Yan Xiang ◽  
Jian Feng He ◽  
Lei Ma ◽  
San Li Yi ◽  
Jia Ping Xu
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
White Matter ◽  
Magnetic Resonance ◽  
Automatic Segmentation ◽  
White Matter Lesions ◽  
Region Growing ◽  
Conventional Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Conventional Magnetic Resonance

Multiple sclerosis (MS) is a chronic disease that affects the central nervous system and impacts substantially on patients. MS lesions are visible in conventional magnetic resonance imaging (cMRI) and the automatic segmentation of MS lesions enables the efficient processing of images for research studies and in clinical trials. A new method for the segmentation of MS white matter lesions (WML) on cMRI is presented in this paper. Firstly the Kernel Fuzzy C-Means Clustering (KFCM) is applied to the preprocessed T1-weight (T1-w) image for extracting the white matter (WM) region. Then region growing algorithm is applied to the WM region image to make a binary mask which is then superimposed on the corresponding T2-weight (T2-w) image to yield a masked image only containing WM structures and lesions. The KFCM is then reapplied to the masked image to obtain MS lesions. The testing results show that the proposed method is able to segment WML on cMRI automatically and effectively.

Deep Learning on Conventional Magnetic Resonance Imaging Improves the Diagnosis of Multiple Sclerosis Mimics

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Maria A. Rocca ◽  
Nicoletta Anzalone ◽  
Loredana Storelli ◽  
Anna Del Poggio ◽  
Laura Cacciaguerra ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Conventional Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Conventional Magnetic Resonance
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