Rauh, Andreas ; John, Kristine ; Wüstenhagen, Carolin ; Bruschewski, Martin ; Grundmann, Sven
Contributor:Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.
Title: Group publication title: Subject and Keywords:magnet resonance imaging ; compressed sensing ; stochastic uncertainty ; unscented transformation
Abstract:In the frame of stochastic filtering for nonlinear (discrete-time) dynamic systems, the unscented transformation plays a vital role in predicting state information from one time step to another and correcting a priori knowledge of uncertain state estimates by available measured data corrupted by random noise. In contrast to linearization-based techniques, such as the extended Kalman filter, the use of an unscented transformation not only allows an approximation of a nonlinear process or measurement model in terms of a first-order Taylor series expansion at a single operating point, but it also leads to an enhanced quantification of the first two moments of a stochastic probability distribution by a large signal-like sampling of the state space at the so-called sigma points which are chosen in a deterministic manner. ; In this paper, a novel application of the unscented transformation technique is presented for the stochastic analysis of measurement uncertainty in magnet resonance imaging (MRI). A representative benchmark scenario from the field of velocimetry for engineering applications which is based on measured data gathered at an MRI scanner concludes this contribution.
Publisher:Zielona Góra: Uniwersytet Zielonogórski
Date: Resource Type: DOI: Pages: Source:AMCS, volume 31, number 1 (2021) ; click here to follow the link
Language: License CC BY 4.0: Rights: