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https://hdl.handle.net/2440/126327
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Type: | Journal article |
Title: | Quantitative FLAIR MRI in amyotrophic lateral sclerosis |
Author: | Fabes, J. Matthews, L. Filippini, N. Talbot, K. Jenkinson, M. Turner, M.R. |
Citation: | Academic Radiology, 2017; 24(10):1187-1194 |
Publisher: | Elsevier |
Issue Date: | 2017 |
ISSN: | 1076-6332 1878-4046 |
Statement of Responsibility: | Jeremy Fabes, Lucy Matthews, Nicola Filippini, Kevin Talbot, Mark Jenkinson, Martin R. Turner |
Abstract: | RATIONALE AND OBJECTIVES:T2-weighted magnetic resonance imaging (MRI) hyperintensity assessed visually in the corticospinal tract (CST) lacks sensitivity for a diagnosis of amyotrophic lateral sclerosis (ALS). We sought to explore a quantitative approach to fluid-attenuated inversion recovery (FLAIR) MRI intensity across a range of ALS phenotypes. MATERIALS AND METHODS:Thirty-three classical ALS patients, 10 with a flail arm presentation, and six with primary lateral sclerosis underwent MRI at 3 Tesla. Comparisons of quantitative FLAIR intensity in the CST and corpus callosum were made between 21 healthy controls and within patient phenotypic subgroups, some of whom were studied longitudinally. RESULTS:Mean FLAIR intensity was greater in patient groups. The cerebral peduncle intensity provided the strongest subgroup classification. FLAIR intensity increased longitudinally. The rate of change of FLAIR within CST correlated with rate of decline in executive function and ALS functional rating score. CONCLUSIONS:FLAIR MRI encodes quantifiable information of potential diagnostic, stratification, and monitoring value. |
Keywords: | Amyotrophic lateral sclerosis; motor neuron disease; biomarker; neuroimaging; phenotype; prognosis |
Rights: | © 2017 The Association of University Radiologists. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
DOI: | 10.1016/j.acra.2017.04.008 |
Published version: | http://dx.doi.org/10.1016/j.acra.2017.04.008 |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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