Measurement of Whole-Brain and Gray Matter Atrophy in Multiple Sclerosis: Assessment with MR Imaging.

Purpose To compare available methods for whole-brain and gray matter (GM) atrophy estimation in multiple sclerosis (MS) in terms of repeatability (same magnetic resonance [MR] imaging unit) and reproducibility (different system/field strength) for their potential clinical applications. Materials and Methods The softwares ANTs-v1.9, CIVET-v2.1, FSL-SIENAX/SIENA-5.0.1, Icometrix-MSmetrix-1.7, and SPM-v12 were compared. This retrospective study, performed between March 2015 and March 2017, collected data from (a) eight simulated MR images and longitudinal data (2 weeks) from 10 healthy control subjects to assess the cross-sectional and longitudinal accuracy of atrophy measures, (b) test-retest MR images in 29 patients with MS acquired within the same day at different imaging unit field strengths/manufacturers to evaluate precision, and (c) longitudinal data (1 year) in 24 patients with MS for the agreement between methods. Tissue segmentation, image registration, and white matter (WM) lesion filling were also evaluated. Multiple paired t tests were used for comparisons. Results High values of accuracy (0.87-0.97) for whole-brain and GM volumes were found, with the lowest values for MSmetrix. ANTs showed the lowest mean error (0.02%) for whole-brain atrophy in healthy control subjects, with a coefficient of variation of 0.5%. SPM showed the smallest mean error (0.07%) and coefficient of variation (0.08%) for GM atrophy. Globally, good repeatability (P > .05) but poor reproducibility (P < .05) were found for all methods. WM lesion filling technique mainly affected ANTs, MSmetrix, and SPM results (P < .05). Conclusion From this comparison, it would be possible to select a software for atrophy measurement, depending on the requirements of the application (research center, clinical trial) and its goal (accuracy and repeatability or reproducibility). An improved reproducibility is required for clinical application. © RSNA, 2018 Online supplemental material is available for this article.

[1]  Jeffrey A. Cohen,et al.  Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria , 2011, Annals of neurology.

[2]  M. Filippi,et al.  MRI evidence for multiple sclerosis as a diffuse disease of the central nervous system , 2005, Journal of Neurology.

[3]  M. Sormani,et al.  Inclusion of brain volume loss in a revised measure of ‘no evidence of disease activity’ (NEDA-4) in relapsing–remitting multiple sclerosis , 2015, Multiple sclerosis.

[4]  M. Filippi,et al.  Inflammatory demyelination and neurodegeneration in early multiple sclerosis , 2007, Journal of the Neurological Sciences.

[5]  F. Barkhof,et al.  Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy , 2017, NeuroImage: Clinical.

[6]  Saurabh Jain,et al.  Reliable measurements of brain atrophy in individual patients with multiple sclerosis , 2016, Brain and behavior.

[7]  M. Horsfield,et al.  Gray matter damage predicts the accumulation of disability 13 years later in MS , 2013, Neurology.

[8]  Stephen M. Smith,et al.  Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.

[9]  S. Hatton,et al.  Automated brain volumetrics in multiple sclerosis: a step closer to clinical application , 2016, Journal of Neurology, Neurosurgery & Psychiatry.

[10]  Brian B. Avants,et al.  An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data , 2011, Neuroinformatics.

[11]  R. Rudick,et al.  Gray matter atrophy in multiple sclerosis: A longitudinal study , 2008, Annals of neurology.

[12]  D. Arnold,et al.  Treatment effect on brain atrophy correlates with treatment effect on disability in multiple sclerosis , 2014, Annals of neurology.

[13]  À. Rovira,et al.  Treating relapsing–remitting multiple sclerosis: therapy effects on brain atrophy , 2015, Journal of Neurology.

[14]  M. Battaglini,et al.  Evaluating and reducing the impact of white matter lesions on brain volume measurements , 2012, Human brain mapping.

[15]  C. Enzinger,et al.  Measuring Gray Matter and White Matter Damage in MS: Why This is Not Enough , 2015, Front. Neurol..

[16]  P. Matthews,et al.  Normalized Accurate Measurement of Longitudinal Brain Change , 2001, Journal of computer assisted tomography.

[17]  Kunio Nakamura,et al.  Segmentation of brain magnetic resonance images for measurement of gray matter atrophy in multiple sclerosis patients , 2009, NeuroImage.

[18]  Amir Alansary,et al.  MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans , 2015, Comput. Intell. Neurosci..

[19]  Deborah Pareto,et al.  Brain Atrophy in Multiple Sclerosis: Clinical Relevance and Technical Aspects. , 2017, Neuroimaging clinics of North America.

[20]  Erich P Huang,et al.  Metrology Standards for Quantitative Imaging Biomarkers. , 2015, Radiology.

[21]  Marco Battaglini,et al.  Measuring Brain Atrophy in Multiple Sclerosis , 2007, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[22]  C. Lucchinetti,et al.  Pathology of demyelinating diseases. , 2012, Annual review of pathology.

[23]  M. Filippi,et al.  Gray matter trophism, cognitive impairment, and depression in patients with multiple sclerosis , 2017, Multiple sclerosis.

[24]  D. Louis Collins,et al.  Evaluation of automated techniques for the quantification of grey matter atrophy in patients with multiple sclerosis , 2010, NeuroImage.