Towards fully automated synthetic ECV quantification: an open-access machine learning-based approach for fast blood draw-free CMR.
- DOI
- 10.1038/s41598-026-43624-3
- Published
- 2026-03-10
- Container
- Sci Rep
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-43624-3,
title = {Towards fully automated synthetic ECV quantification: an open-access machine learning-based approach for fast blood draw-free CMR.},
author = {Beyer RE and Hüllebrand M and Doeblin P and Laube A and Müller ML and Stehning C and Werhahn SM and Chen W and Hennemuth A and Kelle S.},
year = {2026},
journal = {Sci Rep},
doi = {10.1038/s41598-026-43624-3},
url = {https://doi.org/10.1038/s41598-026-43624-3}
}RIS
TY - JOUR TI - Towards fully automated synthetic ECV quantification: an open-access machine learning-based approach for fast blood draw-free CMR. AU - Beyer RE AU - Hüllebrand M AU - Doeblin P AU - Laube A AU - Müller ML AU - Stehning C AU - Werhahn SM AU - Chen W AU - Hennemuth A AU - Kelle S. PY - 2026 JO - Sci Rep DO - 10.1038/s41598-026-43624-3 UR - https://doi.org/10.1038/s41598-026-43624-3 ER -
APA
RE, B., M, H., P, D., A, L., ML, M., C, S., SM, W., W, C., A, H., & S., K. (2026). Towards fully automated synthetic ECV quantification: an open-access machine learning-based approach for fast blood draw-free CMR.. Sci Rep. https://doi.org/10.1038/s41598-026-43624-3
Source records
- europe-pmc · retrieved 2026-09-27T02:19:22.572Z
- doaj · retrieved 2026-09-27T02:19:22.574Z