- Tytuł:
- An unscented transformation approach to stochastic analysis of measurement uncertainty in magnet resonance imaging with applications in engineering
- Autorzy:
-
Rauh, Andreas
John, Kristine
Wüstenhagen, Carolin
Bruschewski, Martin
Grundmann, Sven - Powiązania:
- https://bibliotekanauki.pl/articles/1838185.pdf
- Data publikacji:
- 2021
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
magnet resonance imaging
compressed sensing
stochastic uncertainty
unscented transformation
rezonans magnetyczny
próbkowanie oszczędne
niepewność stochastyczna - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 73-83
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł