Maximum-likelihood estimation of Rician distribution parameters.
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.
Sijbers
J
J
Department of Physics, University of Antwerp, Belgium. sijbers@ruca.ua.ac.be
den Dekker
A J
AJ
Scheunders
P
P
Van Dyck
D
D
D003198:Computer Simulation; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods*