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

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

International Journal of Applied Mathematics and Computer Science
Volume 31 (2021): Issue 1 (March 2021)
By: Andreas Rauh,  Kristine John,  Carolin Wüstenhagen,  Martin Bruschewski and  Sven Grundmann  
Open Access
|Apr 2021

Authors

Andreas Rauh

Andreas.Rauh@interval-methods.de

Lab-STICC, Brest, France

Kristine John

Kristine.John@uni-rostock.de

Institute of Fluid Mechanics, University of Rostock, Rostock, Germany

Carolin Wüstenhagen

Carolin.Wuestenhagen@uni-rostock.de

Institute of Fluid Mechanics, University of Rostock, Rostock, Germany

Martin Bruschewski

Martin.Bruschewski@uni-rostock.de

Institute of Fluid Mechanics, University of Rostock, Rostock, Germany

Sven Grundmann

Sven.Grundmann@uni-rostock.de

Institute of Fluid Mechanics, University of Rostock, Rostock, Germany
DOI: https://doi.org/10.34768/amcs-2021-0006 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
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Language: English
Page range: 73 - 83
Submitted on: Jun 5, 2020
Accepted on: Dec 27, 2020
Published on: Apr 3, 2021
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
magnet resonance imaging,
compressed sensing,
stochastic uncertainty,
unscented transformation
Related subjects:
Mathematics,
Applied mathematics

© 2021 Andreas Rauh, Kristine John, Carolin Wüstenhagen, Martin Bruschewski, Sven Grundmann, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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