Parameter estimation from magnitude MR images

This article deals with the estimation of model-based parameters, such as t he noise variance and signal components, from magnitude magnetic resonance (MR) images. Special attention has been paid to the estimation of T-1 - and T-2-relaxation parameters. It is shown that most of the conventional estim ation methods, when applied to magnitude MR images, yield biased results. A lso, it is shown how the knowledge of the proper probability density functi on of magnitude MR data (i.e., the Rice distribution) can be exploited so a s to avoid (or at least reduce) such systematic errors. The proposed method is based on maximum likelihood (ML) estimation. (C) 1999 John Wiley & Sons , Inc.