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Ann Appl Stat. 2014;8(2):1095-1118.

ANALYSIS OF MULTIPLE SCLEROSIS LESIONS VIA SPATIALLY VARYING COEFFICIENTS.

Author information

1
Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai 200433, China & Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK ; Department of Statistics & Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, UK.
2
Medical Image Analysis Center (MIAC), University Hospital Basel, CH-4031 Basel, Switzerland.
3
Department of Statistics & Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, UK.
4
Department of Biostatistics, School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109.

Abstract

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 lesion maps does not entirely eliminate the non-Gaussian nature of the data and requires an arbitrary choice of the smoothing parameter. Here we present a Bayesian spatial model to accurately model binary lesion maps and to determine if there is spatial dependence between lesion location and subject specific covariates such as MS subtype, age, gender, disease duration and disease severity measures. We apply our model to binary lesion maps derived from T2-weighted MRI images from 250 multiple sclerosis patients classified into five clinical subtypes, and demonstrate unique modeling and predictive capabilities over existing methods.

KEYWORDS:

Conditional autoregressive model; Image analysis; Lesion probability map; Magnetic resonance imaging; Markov random fields; Multiple sclerosis; Spatially varying coefficients

PMID:
25431633
PMCID:
PMC4243942

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