Clinical Evaluation of A-LIKNet
A-LIKNet deep learning accelerated CINE sequences enable rapid and reliable assessment of cardiac function with excellent agreement to standard imaging. From a clinical perspective, image quality remains acceptable up to an acceleration factor of 16×, supporting routine application, while 24× acceleration may be reserved for selected use cases requiring maximal speed.
Authors
- Andreas Lingg (ORCID: https://orcid.org/0000-0001-8583-8627)
- Konstantin Nikolaou (ORCID: https://orcid.org/0000-0003-2668-7325)
- Florian Hagen (ORCID: https://orcid.org/0000-0002-1215-9428)
- Thomas Küstner (ORCID: https://orcid.org/0000-0002-0353-4898)
- Jens Kübler (ORCID: https://orcid.org/0000-0002-3450-7585)
- Patrick Krumm (ORCID: https://orcid.org/0000-0003-1705-8439)
- Jan M. Brendel (ORCID: https://orcid.org/0000-0002-2729-610X)
- Siying Xu (ORCID: https://orcid.org/0009-0000-1713-4951)
- Aya Ghoul (ORCID: https://orcid.org/0009-0002-1221-1756)
- Stanislau Chekan
Institutions
- Research Institute for Work, Technology and Culture (DE)
- Norwegian Womens Public Health Association (NO)
- University of Tübingen (DE)
Publication Details
- Journal
- Investigative Radiology
- Published
- 2025-10-17
- DOI
- https://doi.org/10.1097/rli.0000000000001243
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00