title: Local estimation of the noise level in MRI using structural adaptation.
abstract: We present a method for local estimation of the signal-dependent noise level in magnetic resonance images. The procedure uses a multi-scale approach to adaptively infer on local neighborhoods with similar data distribution. It exploits a maximum-likelihood estimator for the local noise level. The validity of the method was evaluated on repeated diffusion data of a phantom and simulated data using T1-data corrupted with artificial noise. Simulation results were compared with a recently proposed estimate. The method was also applied to a high-resolution diffusion dataset to obtain improved diffusion model estimation results and to demonstrate its usefulness in methods for enhancing diffusion data.
authors.affiliation: Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany. Electronic address: karsten.tabelow@wias-berlin.de.
authors.forename: Karsten
authors.initials: K
authors.lastname: Tabelow
authors.affiliation: Citigroup Biomedical Imaging Center, Weill Cornell Medical College, New York, USA.
authors.forename: Henning U
authors.initials: HU
authors.lastname: Voss
authors.affiliation: Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany.
authors.forename: Jörg
authors.initials: J
authors.lastname: Polzehl
mesh_terms: D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D008279:Magnetic Resonance Imaging / Q000379:methods*; D019047:Phantoms, Imaging
keywords: Magnetic resonance imaging; Maximum Likelihood estimation; Noise estimation; Noncentral chi-distribution