Premium
Bayesian analysis of transverse signal decay with application to human brain
Author(s) -
Bouhrara Mustapha,
Reiter David A.,
Spencer Richard G.
Publication year - 2015
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.25457
Subject(s) - rician fading , noise (video) , bayesian probability , estimation theory , imaging phantom , signal to noise ratio (imaging) , signal (programming language) , transverse plane , relaxation (psychology) , computer science , mathematics , statistical physics , statistics , physics , algorithm , artificial intelligence , optics , psychology , social psychology , decoding methods , structural engineering , fading , image (mathematics) , programming language , engineering
Purpose Transverse relaxation analysis with several signal models has been used extensively to determine tissue and material properties. However, the derivation of corresponding parameter values is notoriously unreliable. We evaluate improvements in the quality of parameter estimation using Bayesian analysis and incorporating the Rician noise model, as appropriate for magnitude MR images. Theory and Methods Monoexponential, stretched exponential, and biexponential signal models were analyzed using nonlinear least squares (NLLS) and Bayesian approaches. Simulations and phantom and human brain data were analyzed using three different approaches to account for noise. Parameter estimation bias (reflecting accuracy) and dispersion (reflecting precision) were derived for a range of signal‐to‐noise ratios (SNR) and relaxation parameters. Results All methods performed well at high SNR. At lower SNR, the Bayesian approach yielded parameter estimates of considerably greater precision, as well as greater accuracy, than did NLLS. Incorporation of the Rician noise model greatly improved accuracy and, to a somewhat lesser extent, precision, in derived transverse relaxation parameters. Analyses of data obtained from solution phantoms and from brain were consistent with simulations. Conclusion Overall, estimation of parameters characterizing several different transverse relaxation models was markedly improved through use of Bayesian analysis and through incorporation of the Rician noise model. Magn Reson Med 74:785–802, 2015. © 2014 Wiley Periodicals, Inc.