Literature DB >> 16385015

Clinical-magnetic resonance imaging correlations in multiple sclerosis.

Robert Zivadinov1, Thomas P Leist.   

Abstract

Conventional magnetic resonance imaging (MRI) has routinely been used to improve the accuracy of multiple sclerosis (MS) diagnosis and monitoring, detect the effects of disease-modifying therapy, and refine the utility of clinical assessments. However, conventional MRI measures, such as the use of lesion volume and count of gadolinium-enhancing and T2 lesions, have insufficient sensitivity and specificity to reveal the true degree of pathological changes occurring in MS. Newer metrics of MRI analysis, including T1-weighted hypointense lesions (black holes) and central nervous system (CNS) atrophy measures, are able to capture a more global picture of the range of tissue alterations caused by inflammation, demyelination, axonal loss, and neurodegeneration. There is mounting evidence that these MRI measures correlate well with existing and developing neurological impairment and disability. In so doing, these MRI techniques can help elucidate the mechanisms underlying the pathophysiology and natural history of MS. The current understanding is that T1 black holes and CNS atrophy more accurately reflect the neurodegenerative and destructive components of the MS disease process. Therefore, the short and long-term studies that aim to measure the degree and severity of the neurodegenerative MS disease process should incorporate these MRI metrics as part of their standard routine MRI protocols.

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Year:  2005        PMID: 16385015     DOI: 10.1177/1051228405283291

Source DB:  PubMed          Journal:  J Neuroimaging        ISSN: 1051-2284            Impact factor:   2.486


  37 in total

Review 1.  Magnetic resonance spectroscopy in the monitoring of multiple sclerosis.

Authors:  Ponnada A Narayana
Journal:  J Neuroimaging       Date:  2005       Impact factor: 2.486

2.  Normal-appearing white matter permeability distinguishes poor cognitive performance in processing speed and working memory.

Authors:  A Eilaghi; A Kassner; I Sitartchouk; P L Francis; R Jakubovic; A Feinstein; R I Aviv
Journal:  AJNR Am J Neuroradiol       Date:  2013-05-30       Impact factor: 3.825

3.  Fiber density index in the evaluation of the spinal cord in patients with multiple sclerosis.

Authors:  M Ukmar; A Montalbano; E Makuc; I Specogna; A Bratina; R Longo; M A Cova
Journal:  Radiol Med       Date:  2012-06-28       Impact factor: 3.469

Review 4.  MRI in multiple sclerosis: what's inside the toolbox?

Authors:  Mohit Neema; James Stankiewicz; Ashish Arora; Zachary D Guss; Rohit Bakshi
Journal:  Neurotherapeutics       Date:  2007-10       Impact factor: 7.620

5.  Normal appearing white matter permeability: a marker of inflammation and information processing speed deficit among relapsing remitting multiple sclerosis patients.

Authors:  Eldar Eftekhari; Seyed-Parsa Hojjat; Rita Vitorino; Timothy J Carroll; Charles Grady Cantrell; Liesly Lee; Matthew W Taylor; Sarah A Morrow; Haddas Benhabib; Richard I Aviv; Andrea Kassner
Journal:  Neuroradiology       Date:  2017-06-16       Impact factor: 2.804

6.  The relationships among MRI-defined spinal cord involvement, brain involvement, and disability in multiple sclerosis.

Authors:  Adam B Cohen; Mohit Neema; Ashish Arora; Elisa Dell'oglio; Ralph H B Benedict; Shahamat Tauhid; Daniel Goldberg-Zimring; Christian Chavarro-Nieto; Antonella Ceccarelli; Joshua P Klein; James M Stankiewicz; Maria K Houtchens; Guy J Buckle; David C Alsop; Charles R G Guttmann; Rohit Bakshi
Journal:  J Neuroimaging       Date:  2011-03-29       Impact factor: 2.486

7.  Magnetic resonance disease severity scale (MRDSS) for patients with multiple sclerosis: a longitudinal study.

Authors:  Jennifer Moodie; Brian C Healy; Guy J Buckle; Susan A Gauthier; Bonnie I Glanz; Ashish Arora; Antonia Ceccarelli; Shahamat Tauhid; Xue-Mei Han; Arun Venkataraman; Tanuja Chitnis; Samia J Khoury; Charles R G Guttmann; Howard L Weiner; Mohit Neema; Rohit Bakshi
Journal:  J Neurol Sci       Date:  2011-12-28       Impact factor: 3.181

8.  Composite MRI scores improve correlation with EDSS in multiple sclerosis.

Authors:  A H Poonawalla; S Datta; V Juneja; F Nelson; J S Wolinsky; G Cutter; P A Narayana
Journal:  Mult Scler       Date:  2010-09       Impact factor: 6.312

9.  Brain MRI lesion load at 1.5T and 3T versus clinical status in multiple sclerosis.

Authors:  James M Stankiewicz; Bonnie I Glanz; Brian C Healy; Ashish Arora; Mohit Neema; Ralph H B Benedict; Zachary D Guss; Shahamat Tauhid; Guy J Buckle; Maria K Houtchens; Samia J Khoury; Howard L Weiner; Charles R G Guttmann; Rohit Bakshi
Journal:  J Neuroimaging       Date:  2011-04       Impact factor: 2.486

10.  Multiple sclerosis lesion geometry in quantitative susceptibility mapping (QSM) and phase imaging.

Authors:  Sarah Eskreis-Winkler; Kofi Deh; Ajay Gupta; Tian Liu; Cynthia Wisnieff; Moonsoo Jin; Susan A Gauthier; Yi Wang; Pascal Spincemaille
Journal:  J Magn Reson Imaging       Date:  2014-08-30       Impact factor: 4.813

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