Maximum-likelihood estimation of Rician distribution parameters

IEEE Trans Med Imaging. 1998 Jun;17(3):357-61. doi: 10.1109/42.712125.

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.

MeSH terms

  • Computer Simulation
  • Likelihood Functions
  • Magnetic Resonance Imaging / methods*