title: Data distributions in magnetic resonance images: a review.
abstract: Many image processing methods applied to magnetic resonance (MR) images directly or indirectly rely on prior knowledge of the statistical data distribution that characterizes the MR data. Also, data distributions are key in many parameter estimation problems and strongly relate to the accuracy and precision with which parameters can be estimated. This review paper provides an overview of the various distributions that occur when dealing with MR data, considering both single-coil and multiple-coil acquisition systems. The paper also summarizes how knowledge of the MR data distributions can be used to construct optimal parameter estimators and answers the question as to what precision may be achieved ultimately from a particular MR image.
authors: iMinds-Vision Lab, University of Antwerp, Universiteitsplein 1, N.1, 2610 Wilrijk, Belgium; Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands. Electronic address: a.j.dendekker@tudelft.nl.
authors: A J
authors: AJ
authors: den Dekker
authors: iMinds-Vision Lab, University of Antwerp, Universiteitsplein 1, N.1, 2610 Wilrijk, Belgium.
authors: J
authors: J
authors: Sijbers
mesh_terms: D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D059629:Signal-To-Noise Ratio
keywords: Magnetic resonance imaging; Noise modeling; Parameter estimation; Statistical data distributions