Analysis of multiple sclerosis lesions via spatially varying coefficients
نویسندگان
چکیده
منابع مشابه
Analysis of Multiple Sclerosis Lesions via Spatially Varying Coefficients.
Magnetic resonance imaging (MRI) plays a vital role in the scientific investigation and clinical management of multiple sclerosis. Analyses of binary multiple sclerosis lesion maps are typically "mass univariate" and conducted with standard linear models that are ill suited to the binary nature of the data and ignore the spatial dependence between nearby voxels (volume elements). Smoothing the ...
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This paper considers modelling spatially varying regression effects for multivariate mortality count outcomes. Alternative approaches to spatial regression heterogeneity are considered: the multivariate normal conditional autoregressive (MCAR) model is contrasted with a flexible set of priors based on the multiple membership approach. These include spatial factor priors and a nonparametric appr...
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this paper introduces a novel methodology for the segmentation of brain ms lesions in mri volumes using a new clustering algorithm named scpfcm. scpfcm uses membership, typicality and spatial information to cluster each voxel. the proposed method relies on an initial segmentation of ms lesions in t1-w and t2-w images by applying scpfcm algorithm, and the t1 image is then used as a mask and is ...
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BACKGROUND AND PURPOSE Diffusion-weighted MR imaging and the trace apparent diffusion coefficient (ADC) provide important structural information about tissues. The purpose of this study was to investigate the relationship between trace ADC values and the enhancement pattern of multiple sclerosis (MS) lesions. METHODS Ninety-six lesions, identified in 24 patients with MS, were characterized by...
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ژورنال
عنوان ژورنال: The Annals of Applied Statistics
سال: 2014
ISSN: 1932-6157
DOI: 10.1214/14-aoas718