Bayesian algorithm using spatial priors for multiexponential T₂ relaxometry from multiecho spin echo MRI.

TitleBayesian algorithm using spatial priors for multiexponential T₂ relaxometry from multiecho spin echo MRI.
Publication TypeJournal Article
Year of Publication2012
AuthorsKumar D, Nguyen TD, Gauthier SA, Raj A
JournalMagn Reson Med
Date Published2012 Nov
KeywordsAdult, Algorithms, Bayes Theorem, Brain, Brain Mapping, Diffusion Tensor Imaging, Echo-Planar Imaging, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Male, Nerve Fibers, Myelinated, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Spin Labels

Multiexponential T₂ 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 T₂ 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 T₂ 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.

Alternate JournalMagn Reson Med
PubMed ID22266707
Grant ListP41 RR023953 / RR / NCRR NIH HHS / United States

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