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Authors
Liebert, Andrzej https://orcid.org/0000-0002-8450-3021
Das, Badhan K.
Kapsner, Lorenz A.
Eberle, Jessica
Skwierawska, Dominika
Folle, Lukas
Schreiter, Hannes
Laun, Frederik B.
Ohlmeyer, Sabine
Uder, Michael
Wenkel, Evelyn
Bickelhaupt, Sebastian
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Funding
Funding for this research was provided by:
Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst (bidt Graduate Center for Postdocs)
Bundesministerium für Bildung und Forschung (FKZ 161B0976)
Universitätsklinikum Erlangen
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Article History
Received: 1 April 2023
Revised: 5 October 2023
Accepted: 21 October 2023
First Online: 15 December 2023
Declarations
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: The scientific guarantor of this publication is Andrzej Liebert.
: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
: Lorenz A. Kapsner and Andrzej Liebert have significant statistical expertise.
: Written informed consent was waived by the Institutional Review Board.
: Institutional Review Board (Ethics committee of the Friedrich-Alexander Universität Erlangen-Nürnberg) approval was obtained.
: A total of 2265 subjects have been previously reported in “Automated artifact detection in abbreviated dynamic contrast-enhanced (DCE) MRI-derived maximum intensity projections (MIPs) of the breast.” Eur Radiol. .A total of 1309 subjects have been previously reported in “Image quality assessment using deep learning in high b-value diffusion-weighted breast MRI.” Kapsner, L.A., Balbach, E.L., Folle, L. et al Image quality assessment using deep learning in high b-value diffusion-weighted breast MRI. Sci Rep 13, 10549 (2023).
: • retrospective• diagnostic or prognostic study• performed at one institution
