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      Improved Determination of the Myelin Water Fraction in Human Brain using Magnetic Resonance Imaging through Bayesian Analysis of mcDESPOT

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          Abstract

          Myelin water fraction (MWF) mapping with magnetic resonance imaging has led to the ability to directly observe myelination and demyelination in both the developing brain and in disease. Multicomponent driven equilibrium single pulse observation of T 1 and T 2 (mcDESPOT) has been proposed as a rapid approach for multicomponent relaxometry and has been applied to map MWF in human brain. However, even for the simplest two-pool signal model consisting of MWF and non-myelin-associated water, the dimensionality of the parameter space for obtaining MWF estimates remains high. This renders parameter estimation difficult, especially at low-to-moderate signal-to-noise ratios (SNR), due to the presence of local minima and the flatness of the fit residual energy surface used for parameter determination using conventional nonlinear least squares (NLLS)-based algorithms. In this study, we introduce three Bayesian approaches for analysis of the mcDESPOT signal model to determine MWF. Given the high dimensional nature of mcDESPOT signal model, and, thereby, the high dimensional marginalizations over nuisance parameters needed to derive the posterior probability distribution of MWF parameter, the introduced Bayesian analyses use different approaches to reduce the dimensionality of the parameter space. The first approach uses normalization by average signal amplitude, and assumes that noise can be accurately estimated from signal-free regions of the image. The second approach likewise uses average amplitude normalization, but incorporates a full treatment of noise as an unknown variable through marginalization. The third approach does not use amplitude normalization and incorporates marginalization over both noise and signal amplitude. Through extensive Monte Carlo numerical simulations and analysis of in-vivo human brain datasets exhibiting a range of SNR and spatial resolution, we demonstrated the markedly improved accuracy and precision in the estimation of MWF using these Bayesian methods as compared to the stochastic region contraction (SRC) implementation of NLLS.

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          Author and article information

          Journal
          9215515
          20498
          Neuroimage
          Neuroimage
          NeuroImage
          1053-8119
          1095-9572
          24 October 2015
          22 October 2015
          15 February 2016
          15 February 2017
          : 127
          : 456-471
          Affiliations
          Magnetic Resonance Imaging and Spectroscopy Section, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
          Author notes
          Address correspondence to: Mustapha Bouhrara, Ph.D., bouhraram@ 123456mail.nih.gov , or Richard G. Spencer, M.D., Ph.D., NIH/National Institute on Aging, Intramural Research Program, BRC 04B-116, 251 Bayview Boulevard, Baltimore, MD 21224, USA., Tel: 410-558-8226 spencer@ 123456helix.nih.gov
          Article
          PMC4854306 PMC4854306 4854306 nihpa732450
          10.1016/j.neuroimage.2015.10.034
          4854306
          26499810
          2ee20025-2fa3-4ce4-8100-e683ab671c92
          History
          Categories
          Article

          Nonlinear least squares,Brain,Stochastic region contraction algorithm,Bayesian analysis,Myelin water fraction,mcDESPOT

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