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Central nervous system infectious diseases mimicking multiple sclerosis: recognizing distinguishable features using MRI

Overview of attention for article published in Arquivos de Neuro-Psiquiatria, September 2013
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Title
Central nervous system infectious diseases mimicking multiple sclerosis: recognizing distinguishable features using MRI
Published in
Arquivos de Neuro-Psiquiatria, September 2013
DOI 10.1590/0004-282x20130162
Pubmed ID
Authors

Antonio Jose da Rocha, Ingrid Aguiar Littig, Renato Hoffmann Nunes, Charles Peter Tilbery

Abstract

The current diagnostic criteria for multiple sclerosis (MS) confirm the relevant role of magnetic resonance imaging (MRI), supporting the possibility of characterizing the dissemination in space (DIS) and the dissemination in time (DIT) in a single scan. To maintain the specificity of these criteria, it is necessary to determine whether T2/FLAIR visible lesions and the gadolinium enhancement can be attributed to diseases that mimic MS. Several diseases are included in the MS differential diagnosis list, including diseases with exacerbation, remitting periods and numerous treatable infectious diseases, which can mimic the MRI features of MS. We discuss the most relevant imaging features in several infectious diseases that resemble MS and examine the primary spatial distributions of lesions and the gadolinium enhancement patterns related to MS. Recognizing imaging "red flags" can be useful for the proper diagnostic evaluation of suspected cases of MS, facilitating the correct differential diagnosis by assessing the combined clinical, laboratory and MR imaging information.

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 3%
Unknown 59 97%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 9 15%
Student > Ph. D. Student 8 13%
Researcher 8 13%
Other 7 11%
Student > Postgraduate 7 11%
Other 16 26%
Unknown 6 10%
Readers by discipline Count As %
Medicine and Dentistry 34 56%
Neuroscience 6 10%
Agricultural and Biological Sciences 3 5%
Nursing and Health Professions 2 3%
Veterinary Science and Veterinary Medicine 1 2%
Other 6 10%
Unknown 9 15%