Feasibility of virtual T2-weighted fat-saturated breast MRI images by convolutional neural networks
Breast MRI T2w-fat-saturated (FS) images can be virtually generated using convolutional neural networks. Image blurring in virtual T2w-FS images currently limits their clinical applicability. Best quantitative performance could be achieved when using full dynamic-contrast-enhanced acquisition and DWI as input of the neural network.
Authors
- Frederik Bernd Laun (ORCID: https://orcid.org/0000-0002-9269-5609)
- Luise Brock (ORCID: https://orcid.org/0000-0002-4591-4354)
- Dominique Hadler (ORCID: https://orcid.org/0000-0002-7334-8855)
- Evelyn Wenkel
- Andrzej Liebert (ORCID: https://orcid.org/0000-0002-8450-3021)
- Sebastian Bickelhaupt (ORCID: https://orcid.org/0000-0003-1043-4462)
- Lorenz A. Kapsner (ORCID: https://orcid.org/0000-0003-1866-860X)
- Sabine Ohlmeyer (ORCID: https://orcid.org/0000-0001-9163-8840)
- Hannes Schreiter (ORCID: https://orcid.org/0000-0002-4373-6996)
- Michael Uder (ORCID: https://orcid.org/0000-0001-6238-4247)
- Jessica Eberle
- Julius Emons (ORCID: https://orcid.org/0000-0002-2520-4443)
- Ramona Erber (ORCID: https://orcid.org/0000-0003-0315-1229)
- Chris Ehring (ORCID: https://orcid.org/0009-0002-4910-0937)
Institutions
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Universitätsklinikum Erlangen (DE)
- Comprehensive Cancer Center Erlangen (DE)
- University of Regensburg (DE)
Publication Details
- Journal
- European Radiology Experimental
- Published
- 2025-05-02
- DOI
- https://doi.org/10.1186/s41747-025-00580-3
- Citations
- 1
- Primary Topic
- MRI in cancer diagnosis
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Deutsche Forschungsgemeinschaft
- Bayerisches Staatsministerium für Wissenschaft, Forschung und Kunst