Maximum-likelihood estimation of Rician distribution parameters
- 1 June 1998
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Medical Imaging
- Vol. 17 (3) , 357-361
- https://doi.org/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.Keywords
This publication has 16 references indexed in Scilit:
- T2 maximum likelihood estimation from multiple spin‐echo magnitude imagesMagnetic Resonance in Medicine, 1996
- Quantification and improvement of the signal-to-noise ratio in a magnetic resonance image acquisition procedureMagnetic Resonance Imaging, 1996
- Structure adaptive anisotropic filtering for magnetic resonance image enhancementPublished by Springer Nature ,1995
- Magnetic Resonance Image restorationJournal of Mathematical Imaging and Vision, 1995
- Signal-to-noise measures for magnetic resonance imagersMagnetic Resonance Imaging, 1993
- An unbiased signal‐to‐noise ratio measure for magnetic resonance imagesMedical Physics, 1993
- The use of power images to perform quantitative analysis on low SNR MR imagesMagnetic Resonance Imaging, 1993
- Nonlinear anisotropic filtering of MRI dataIEEE Transactions on Medical Imaging, 1992
- Improved detectability in low signal‐to‐noise ratio magnetic resonance images by means of a phase‐corrected real reconstructionMedical Physics, 1989
- Mathematical Analysis of Random NoiseBell System Technical Journal, 1944