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      Whole Brain Volume Measured from 1.5T versus 3T MRI in Healthy Subjects and Patients with Multiple Sclerosis

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          Whole brain atrophy is a putative outcome measure in monitoring relapsing‐remitting multiple sclerosis (RRMS). With the ongoing MRI transformation from 1.5T to 3T, there is an unmet need to calibrate this change. We evaluated brain parenchymal volumes (BPVs) from 1.5T versus 3T in MS and normal controls (NC).


          We studied MS [ n = 26, age (mean, range) 43 (21‐55), 22 (85%) RRMS, Expanded Disability Status Scale (EDSS) 1.98 (0‐6.5), timed 25 foot walk (T25FW) 5.95 (3.2‐33.0 seconds)] and NC [ n = 9, age 45 (31‐53)]. Subjects underwent 1.5T (Phillips) and 3T (GE) 3‐dimensional T1‐weighted scans to derive normalized BPV from an automated SIENAX pipeline. Neuropsychological testing was according to consensus panel recommendations.


          BPV‐1.5T was higher than BPV‐3T [mean (95% CI) + 45.7 mL (+35.3, +56.1), P < .00001], most likely due to improved tissue‐CSF contrast at 3T. BPV‐3T showed a larger volume decrease and larger effect size in detecting brain atrophy in MS versus NC [−74.5 mL (−126.5, −22.5), P = .006, d = .92] when compared to BPV‐1.5T [−51.3.1 mL (−99.8, −2.8), P = .04, d = .67]. Correlations between BPV‐1.5T and EDSS ( r = −.43, P = .027) and BPV‐3T and EDSS ( r = −.49, P = .011) and between BPV‐1.5T and T25FW ( r = −.46, P = .018) and BPV‐3T and T25FW ( r = −.56, P = .003) slightly favored 3T. BPV‐cognition correlations were significant ( P < .05) for 6 of 11 subscales to a similar degree at 1.5T ( r range = .44‐.58) and 3T ( r range = .43‐.53).


          Field strength may impact whole brain volume measurements in patients with MS though the differences are not too divergent between 1.5T and 3T.

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          Most cited references 30

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          The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).
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            One method of evaluating the degree of neurologic impairment in MS has been the combination of grades (0 = normal to 5 or 6 = maximal impairment) within 8 Functional Systems (FS) and an overall Disability Status Scale (DSS) that had steps from 0 (normal) to 10 (death due to MS). A new Expanded Disability Status Scale (EDSS) is presented, with each of the former steps (1,2,3 . . . 9) now divided into two (1.0, 1.5, 2.0 . . . 9.5). The lower portion is obligatorily defined by Functional System grades. The FS are Pyramidal, Cerebellar, Brain Stem, Sensory, Bowel & Bladder, Visual, Cerebral, and Other; the Sensory and Bowel & Bladder Systems have been revised. Patterns of FS and relations of FS by type and grade to the DSS are demonstrated.
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              An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods. Copyright 2002 Wiley-Liss, Inc.

                Author and article information

                J Neuroimaging
                J Neuroimaging
                Journal of Neuroimaging
                John Wiley and Sons Inc. (Hoboken )
                Jan-Feb 2016
                28 June 2015
                : 26
                : 1 ( doiID: 10.1111/jon.2016.26.issue-1 )
                : 62-67
                [ 1 ] Departments of Neurology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research Partners MS Center, Harvard Medical School Boston MA
                [ 2 ] Departments of Radiology, Brigham and Women's Hospital, Laboratory for Neuroimaging Research Partners MS Center, Harvard Medical School Boston MA
                Author notes
                [* ] Correspondence: Address correspondence to Rohit Bakshi, Laboratory for Neuroimaging Research, One Brookline Place, Brookline, MA 02445, USA. E‐mail: rbakshi@ .
                © 2015 The Authors. Journal of Neuroimaging published by Wiley Periodicals, Inc. on behalf of American Society of Neuroimaging

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                Page count
                Pages: 6
                Funded by: National Multiple Sclerosis Society
                Award ID: RG3798A2
                Short Communication
                Short Communications
                Custom metadata
                January/February 2016
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.9.1 mode:remove_FC converted:07.07.2016


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