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Estimating cerebral atrophy in multiple sclerosis patients from various MR pulse sequences

Neuroimmunology Branch, National Institutes of Neurological Diseases and Stroke, National Institutes of Health, Clinical Center, 10 Center Drive, Room 5B16, Bethesda, Maryland 20892, USA

J Ostuni

Laboratory of Diagnostic Radiology Research, Clinical Center, National Institutes of Health, Building 10 Room B1N256, 10 Center Drive, MSC 1074, Bethesda, Maryland 20892, USA

D Pham

Laboratory of Personality and Cognition, National Institutes of Aging, National Institutes of Health, Bethesda, Maryland 20892, USA

A Goldszal

Diagnostic Radiology Department, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892, USA

B K Lewis

Laboratory of Diagnostic Radiology Research, Clinical Center, National Institutes of Health, Building 10 Room B1N256, 10 Center Drive, MSC 1074, Bethesda, Maryland 20892, USA

T Howard

Laboratory of Diagnostic Radiology Research, Clinical Center, National Institutes of Health, Building 10 Room B1N256, 10 Center Drive, MSC 1074, Bethesda, Maryland 20892, USA

N Richert

Laboratory of Diagnostic Radiology Research, Clinical Center, National Institutes of Health, Building 10 Room B1N256, 10 Center Drive, MSC 1074, Bethesda, Maryland 20892, USA

H McFarland

Neuroimmunology Branch, National Institutes of Neurological Diseases and Stroke, National Institutes of Health, Clinical Center, 10 Center Drive, Room 5B16, Bethesda, Maryland 20892, USA

J A Frank

Laboratory of Diagnostic Radiology Research, Clinical Center, National Institutes of Health, Building 10 Room B1N256, 10 Center Drive, MSC 1074, Bethesda, Maryland 20892, USA, jafrank{at}helix.nih.gov

Purpose: The purpose of this study was to determine how measures reflecting cerebral atrophy (CA) are influenced by pulse sequence (PS) and segmentation algorithm (SA) used in multiple sclerosis (MS) patients and healthy control (HC)s. Methods: Magnetic resonance imaging (MRI) scans from 10 relapsing-remitting MS (RRMS) patients and five HCs were used to determine the change in brain fractional volume (BFV) over a two-year period. T1-weighted, fluid-attenuated inversion recovery (FLAIR), and proton density (PD)/T2-weighted sequences were analysed. Image segmentation to determine brain volume was performed using the following: a histogram SA, an adaptive fuzzy c-means algorithm (AFCM), and an adaptive Bayesian segmentation with a K-means clustering. Results: Combinations of the SA and PS in MS patients demonstrated significant differences in the per cent change in BFV from baseline. For the combination of PS and SA the per cent change in BFV for year one and year two varied from +2.05% to –1.6% and +0.79% to –3.11%, respectively. Analysis of the HCs data revealed fluctuations in BFV varying from +0.26% to –0.29%. Conclusions: MRI estimates of CA are dependent on both the type of PS and SA; therefore, the choice of SA technique and PS should be consistent during an MS treatment trial. The progression of CA in MS should only be used as a secondary or tertiary outcome measure in treatment trials until a better understanding of how this measurement is affected by the disease, the image acquisition and analysis techniques.

Key Words: cerebral atrophy • clinical trials • image segmentation • MRI • MS

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