• O Goodkin, F Prados, S B Vos, H Pemberton, S Collorone, M H J Hagens, M J Cardoso, T A Yousry, J S Thornton, C H Sudre, F Barkhof, and MAGNIMS study group.
• Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom; Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom. Electronic address: o.goodkin@ucl.ac.uk.
• Neuroimage Clin. 2021 Jan 1; 29: 102542.

BackgroundMRI assessment in multiple sclerosis (MS) focuses on the presence of typical white matter (WM) lesions. Neurodegeneration characterised by brain atrophy is recognised in the research field as an important prognostic factor. It is not routinely reported clinically, in part due to difficulty in achieving reproducible measurements. Automated MRI quantification of WM lesions and brain volume could provide important clinical monitoring data. In general, lesion quantification relies on both T1 and FLAIR input images, while tissue volumetry relies on T1. However, T1-weighted scans are not routinely included in the clinical MS protocol, limiting the utility of automated quantification.ObjectivesWe address an aspect of this important translational challenge by assessing the performance of FLAIR-only lesion and brain segmentation, against a conventional approach requiring multi-contrast acquisition. We explore whether FLAIR-only grey matter (GM) segmentation yields more variability in performance compared with two-channel segmentation; whether this is related to field strength; and whether the results meet a level of clinical acceptability demonstrated by the ability to reproduce established biological associations.MethodsWe used a multicentre dataset of subjects with a CIS suggestive of MS scanned at 1.5T and 3T in the same week. WM lesions were manually segmented by two raters, 'manual 1' guided by consensus reading of CIS-specific lesions and 'manual 2' by any WM hyperintensity. An existing brain segmentation method was adapted for FLAIR-only input. Automated segmentation of WM hyperintensity and brain volumes were performed with conventional (T1/T1 + FLAIR) and FLAIR-only methods.ResultsWM lesion volumes were comparable at 1.5T between 'manual 2' and FLAIR-only methods and at 3T between 'manual 2', T1 + FLAIR and FLAIR-only methods. For cortical GM volume, linear regression measures between conventional and FLAIR-only segmentation were high (1.5T: α = 1.029, R2 = 0.997, standard error (SE) = 0.007; 3T: α = 1.019, R2 = 0.998, SE = 0.006). Age-associated change in cortical GM volume was a significant covariate in both T1 (p = 0.001) and FLAIR-only (p = 0.005) methods, confirming the expected relationship between age and GM volume for FLAIR-only segmentations.ConclusionsFLAIR-only automated segmentation of WM lesions and brain volumes were consistent with results obtained through conventional methods and had the ability to demonstrate biological effects in our study population. Imaging protocol harmonisation and validation with other MS phenotypes could facilitate the integration of automated WM lesion volume and brain atrophy analysis as clinical tools in radiological MS reporting.Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.

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