title: Maximum-likelihood estimation of Rician distribution parameters.
abstract: The problem of parameter estimation from Rician distributed data (e.g., magnitude magnetic resonance images) is addressed. The properties of conventional estimation methods are discussed and compared to maximum-likelihood (ML) estimation which is known to yield optimal results asymptotically. In contrast to previously proposed methods, ML estimation is demonstrated to be unbiased for high signal-to-noise ratio (SNR) and to yield physical relevant results for low SNR.
authors.affiliation: Department of Physics, University of Antwerp, Belgium. sijbers@ruca.ua.ac.be
authors.forename: J
authors.initials: J
authors.lastname: Sijbers
authors.forename: A J
authors.initials: AJ
authors.lastname: den Dekker
authors.forename: P
authors.initials: P
authors.lastname: Scheunders
authors.forename: D
authors.initials: D
authors.lastname: Van Dyck
mesh_terms: D003198:Computer Simulation; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods*