Cardiac Function Assessment with Deep-Learning-Based Automatic Segmentation of Free-Running 4D Whole-Heart CMR
This study validates a DL-based method for fast and accurate segmentation of whole-heart free-running 4D cardiac MRI. Robust performance across diverse protocols and evaluation with complementary metrics that match state-of-the-art benchmarks supports its integration into clinical and research workflows, helping to overcome a key barrier to the broader adoption of free-running imaging.
Authors
- Ruud B. van Heeswijk (ORCID: https://orcid.org/0000-0001-5028-4521)
- Jonas Richiardi (ORCID: https://orcid.org/0000-0002-6975-5634)
- David C. Rotzinger (ORCID: https://orcid.org/0000-0002-6321-3180)
- Angela Rocca (ORCID: https://orcid.org/0000-0002-1634-6992)
- Aisha Touray
- Augustin C. Ogier (ORCID: https://orcid.org/0000-0001-9178-9964)
- Jaume Banús (ORCID: https://orcid.org/0000-0001-9318-6323)
- Jean‐Baptiste Ledoux (ORCID: https://orcid.org/0000-0003-0447-5073)
- Isabel Montón Quesada
- Salomé Baup
- Gorun Ilanjian
- Jérôme Yerly
- Robert J. Holtackers
- Martin Nicoletti
- Matthias Stuber
- Roger Hullin
Institutions
- Centre d'Imagerie BioMedicale (CH)
- University of Lausanne (CH)
Publication Details
- Journal
- Journal of Cardiovascular Magnetic Resonance
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1016/j.jocmr.2025.102677
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00