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Maximum-likelihood estimation of Rician distribution parameters
J Sijbers1, A J den Dekker, P Scheunders
1Department of Physics, University of Antwerp, Belgium. sijbers@ruca.ua.ac.be
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.
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