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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:
Open Access
|Apr 2021

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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: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times 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 Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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