OBJECTIVE: Brain atrophy is part of the pathophysiology of Multiple Sclerosis (MS), correlates with clinical outcomes and is an important parameter of patients’ follow-up. In clinical practice both 1.5T and 3T MRI are used. Therefore, in this study we investigate the comparison between brain atrophy measurements performed on 1.5T and 3T systems.
BACKGROUND: The typical brain atrophy rate per year is around 0.7-1[percnt] in MS patients and around 0.1-0.3[percnt] in healthy subjects. The differences in brain atrophy measurements between 1.5 and 3T should be much smaller than the brain atrophy rate in order to be comparable.
DESIGN/METHODS: 18 MS patients were scanned on the same day on a 1.5T and 3T scanner (Philips Achieva systems). On both systems, 3D T1 and 3D FLAIR were acquired. Measurements were performed using a Jacobian integration-based software (MSmetrix). Lesions were segmented based on the FLAIR and filled on the T1 images before atrophy was measured. As no atrophy is expected within one day, these data sets can be used to evaluate the median percentage error of the brain atrophy measurement as well as the Intraclass Correlation (ICC) for gray matter (GM) volume and parenchymal volume (PV) between 1.5T and 3T scanners.
RESULTS: The median percentage error of the brain atrophy measurement is 0.52[percnt] for GM volume and 0.35[percnt] for PV. The ICC for GM volume is equal to 0.994 and for PV it is equal to 0,998.
CONCLUSIONS: Brain atrophy measurements performed at 1.5T and 3T can be considered to be equivalent in MS patients.
Study Supported by: - Disclosure: Dr. Lysandropoulos has received personal compensation for activities with Biogen Idec, Novartis, Genzyme, Merck & Company, and Teva Neuroscience. Dr. Ribbens has nothing to disclose. Dr. Jain has nothing to disclose. Dr. Maertens has nothing to disclose. Dr. Van Hecke has nothing to disclose. Dr. Mavroudakis has nothing to disclose. Dr. Absil has nothing to disclose. Dr. Metens has nothing to disclose. Dr. David has nothing to disclose.
Poster: ECR 2015 / C-2223 / Quantifying brain atrophy in clinical practice for MS patients: a feasibility study on the measurement error by: Ribbens1, M. Cambron2, J. De Keyser2, A.-M. Van Binst2, J. de Mey3, S. Jain1, G. Nagels2, A. Maertens1, W. Van Hecke 1; 1Leuven/BE, 2Brussel/BE, 3Brussels/BE
Abstract Introduction As 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. Method In 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 MS metrix . MS metrix 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 ). Conclusion Therefore, we can conclude that MS metrix could be of added value in clinical practice for the follow‐up of treatment and disease progression in MS patients.
Background It is well known that Crohn’s disease can involve the stomach. However, most often this upper gastrointestinal tract involvement is asymptomatic. Typically, there is involvement of the small intestine with the typical associated symptoms of Crohn’s disease: abdominal cramps, diarrhoea and weight loss.Methods We report a case of a young woman with complaints of dyspepsia since 2 months.Results Gastroscopy revealed severe aphthous pangastritis with biopsies showing a focal active and chronic gastritis with presence of granulomas. We therefore performed a coloscopy showing an aphthous terminal ileum. The pathologic report indicated granulomatous reaction concordant with a slightly active, mildly chronic terminal ileitis typical for Crohn's disease.Conclusion The incidence of upper gastrointestinal tract involvement of Crohn’s disease is still underestimated, partially due to the asymptomatic nature in two thirds of patients. IBD gastritis should always be included in the differential diagnosis of gastritis, considering the increased risk of a more severe disease course and complications.
Abstract Introduction There 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. Methods Therefore, 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 MS metrix, which is developed especially for use in the MS clinical care path, and compared to Siena ( FSL ), a widely used software for research purposes. Results The MS metrix 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 MS metrix and Siena. Conclusions MS metrix 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.
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.
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