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Bayesian algorithm using spatial priors for multiexponential T 2 relaxometry from multiecho spin echo MRI
Author(s) -
Kumar Dushyant,
Nguyen Thanh D.,
Gauthier Susan A.,
Raj Ashish
Publication year - 2012
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.24170
Subject(s) - relaxometry , white matter , bayesian probability , nuclear magnetic resonance , computer science , imaging phantom , spin echo , magnetic resonance imaging , multiple sclerosis , algorithm , pattern recognition (psychology) , artificial intelligence , nuclear medicine , physics , medicine , radiology , psychiatry
Multiexponential T 2 relaxometry is a powerful research tool for detecting brain structural changes due to demyelinating diseases such as multiple sclerosis. However, because of unusually high signal‐to‐noise ratio requirement compared with other MR modalities and ill‐posedness of the underlying inverse problem, the T 2 distributions obtained with conventional approaches are frequently prone to noise effects. In this article, a novel multivoxel Bayesian algorithm using spatial prior information is proposed. This prior takes into account the expectation that volume fractions and T 2 relaxation times of tissue compartments change smoothly within coherent brain regions. Three‐dimensional multiecho spin echo MRI data were collected from five healthy volunteers at 1.5 T and myelin water fraction maps were obtained using the conventional and proposed algorithms. Compared with the conventional method, the proposed method provides myelin water fraction maps with improved depiction of brain structures and significantly lower coefficients of variance in white matter. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.