Obiekt

Tytuł: An unscented transformation approach to stochastic analysis of measurement uncertainty in magnet resonance imaging with applications in engineering

Contributor:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Group publication title:

AMCS, volume 31 (2021)

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

Resource Identifier:

oai:zbc.uz.zgora.pl:86238

DOI:

10.34768/amcs-2021-0006

Pages:

73-83

Source:

AMCS, volume 31, number 1 (2021) ; kliknij tutaj, żeby przejść

Language:

eng

License CC BY 4.0:

kliknij tutaj, żeby przejść

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

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Data ostatniej modyfikacji:

Jul 18, 2025

Data dodania obiektu:

Jul 18, 2025

Liczba wyświetleń treści obiektu:

19

Wszystkie dostępne wersje tego obiektu:

https://zbc.uz.zgora.pl/repozytorium/publication/101330

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