Abstract
In an earlier paper, we generalized the CGME (Conjugate Gradient Minimal Error) algorithm to the ℓ2-regularized weighted least-squares problem. Here, we use this Generalized CGME method to reconstruct images from actual signals measured using a low-field MRI scanner. We analyze the convergence of both GCGME and the classical Generalized Conjugate Gradient Least Squares (GCGLS) method for the simple case when a Laplace operator is used as a regularizer and indicate when GCGME is to be preferred in terms of convergence speed. We also consider a more complicated ℓ1-penalty in a compressed sensing framework.
Original language | English |
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Title of host publication | Numerical Mathematics and Advanced Applications, ENUMATH 2019 - European Conference |
Editors | Fred J. Vermolen, Cornelis Vuik |
Place of Publication | Cham |
Publisher | Springer |
Pages | 673-681 |
Number of pages | 9 |
ISBN (Electronic) | 978-3-030-55874-1 |
ISBN (Print) | 978-3-030-55873-4 |
DOIs | |
Publication status | Published - 2021 |
Event | European Numerical Mathematics and Advanced Applications Conference 2019 - Hotel Zuiderduin , Egmond aan Zee, Netherlands Duration: 30 Sept 2019 → 4 Oct 2019 https://www.enumath2019.eu/ |
Publication series
Name | Lecture Notes in Computational Science and Engineering |
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Volume | 139 |
ISSN (Print) | 1439-7358 |
ISSN (Electronic) | 2197-7100 |
Conference
Conference | European Numerical Mathematics and Advanced Applications Conference 2019 |
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Abbreviated title | ENUMATH 2019 |
Country/Territory | Netherlands |
City | Egmond aan Zee |
Period | 30/09/19 → 4/10/19 |
Internet address |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.