Parameter estimation from magnitude MR images

Abstract

This article deals with the estimation of model-based parameters, such as the noise variance and signal components, from magnitude magnetic resonance (MR) images. Special attention has been paid to the estimation of T1- and T2-relaxation parameters. It is shown that most of the conventional estimation methods, when applied to magnitude MR images, yield biased results. Also, it is shown how the knowledge of the proper probability density function of magnitude MR data (i.e., the Rice distribution) can be exploited so as to avoid (or at least reduce) such systematic errors. The proposed method is based on maximum likelihood (ML) estimation. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 10, 109–114, 1999