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Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
Published on: February 19, 2021
The use of multiparametric quantitative magnetic resonance imaging for evaluating visually assigned lesion groups in
Christian Thaler1, Tobias D Faizy2, Jan Sedlacik2
1Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany. c.thaler@uke.de.
Abstract:
In multiple sclerosis (MS), inflammatory lesions present a broad spectrum of histopathologic processes. For a better discrimination, lesions are visually defined into different lesion groups according to their appearance on conventional magnetic resonance imaging (MRI). The aim of this study was to investigate the properties of different MS lesion groups using multiparametric quantitative MRI. 35 patients diagnosed with relapsing-remitting MS received 3 Tesla MRI including magnetization-prepared 2 rapid acquisition gradient echo, diffusion tensor imaging and magnetization transfer imaging. Lesion segmentation was performed for T2 lesions, black holes and contrast-enhancing lesions. A subtraction mask was created including only T2 lesions that did not correspond to a black hole or contrast-enhancing lesion. T1 relaxation time (T1-RT), magnetization transfer ratio (MTR), mean diffusivity (MD) and fractional anisotropy (FA) were determined for every lesion and in normal-appearing white matter. Only MD differed significantly between all lesion groups and NAWM (p < 0.05), while FA differed between all lesion groups but not NAWM. T1-RT and MTR were not useful imaging biomarkers to distinguish between lesion groups. A lack of sensitivity and specificity and unproportional alterations of quantitative MRI measures, due to heterogenous histopathologic processes within lesions, may be a possible explanation for missing discrimination. Thus, not only interpretation of visually defined MS lesion but also interpretation of quantitative MRI measures remains challenging and should be conducted carefully.
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