The location and extent of white matter lesions on magnetic resonance imaging (MRI) are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS). Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM) lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM) and the appearance (hyperintense on FLAIR) of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice) between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC) equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default parameter settings.

IntroductionAs neurodegeneration is recognized as a major contributor to disability in multiple sclerosis (MS), brain atrophy quantification could have a high added value in clinical practice to assess treatment efficacy and disease progression, provided that it has a sufficiently low measurement error to draw meaningful conclusions for an individual patient.MethodIn this paper, we present an automated longitudinal method based on Jacobian integration for measuring whole‐brain and gray matter atrophy based on anatomical magnetic resonance images (MRI), named MSmetrix. MSmetrix is specifically designed to measure atrophy in patients with MS, by including iterative lesion segmentation and lesion filling based on FLAIR and T1‐weighted MRI scans.Results MS metrix is compared with SIENA with respect to test–retest error and consistency, resulting in an average test–retest error on an MS data set of 0.13% (MS metrix) and 0.17% (SIENA) and a consistency error of 0.07% (MS metrix) and 0.05% (SIENA). On a healthy subject data set including physiological variability the test–retest is 0.19% (MS metrix) and 0.31% (SIENA).ConclusionTherefore, we can conclude that MSmetrix could be of added value in clinical practice for the follow‐up of treatment and disease progression in MS patients.

IntroductionThere is emerging evidence that brain atrophy is a part of the pathophysiology of Multiple Sclerosis (MS) and correlates with several clinical outcomes of the disease, both physical and cognitive. Consequently, brain atrophy is becoming an important parameter in patients' follow‐up. Since in clinical practice both 1.5Tesla (T) and 3T magnetic resonance imaging (MRI) systems are used for MS patients follow‐up, questions arise regarding compatibility and a possible need for standardization.MethodsTherefore, in this study 18 MS patients were scanned on the same day on a 1.5T and a 3T scanner. For each scanner, a 3D T1 and a 3D FLAIR were acquired. As no atrophy is expected within 1 day, these datasets can be used to evaluate the median percentage error of the brain volume measurement for gray matter (GM) volume and parenchymal volume (PV) between 1.5T and 3T scanners. The results are obtained with MSmetrix, which is developed especially for use in the MS clinical care path, and compared to Siena (FSL), a widely used software for research purposes.ResultsThe MSmetrix median percentage error of the brain volume measurement between a 1.5T and a 3T scanner is 0.52% for GM and 0.35% for PV. For Siena this error equals 2.99%. When data of the same scanner are compared, the error is in the order of 0.06–0.08% for both MSmetrix and Siena.Conclusions MSmetrix appears robust on both the 1.5T and 3T systems and the measurement error becomes an order of magnitude higher between scanners with different field strength.

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