Improvement of Quantification of Myocardial Synthetic ECV with Second-Generation Deep Learning Reconstruction.
- DOI
- 10.3390/jcdd11100304
- Published
- 2024 Oct 2
- Container
- Journal of cardiovascular development and disease
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/jcdd11100304,
title = {Improvement of Quantification of Myocardial Synthetic ECV with Second-Generation Deep Learning Reconstruction.},
author = {Morioka T and Kato S and Onoma A and Izumi T and Sakano T and Ishikawa E and Sawamura S and Yasuda N and Nagase H and Utsunomiya D},
year = {2024},
journal = {Journal of cardiovascular development and disease},
doi = {10.3390/jcdd11100304},
url = {https://doi.org/10.3390/jcdd11100304}
}RIS
TY - JOUR TI - Improvement of Quantification of Myocardial Synthetic ECV with Second-Generation Deep Learning Reconstruction. AU - Morioka T AU - Kato S AU - Onoma A AU - Izumi T AU - Sakano T AU - Ishikawa E AU - Sawamura S AU - Yasuda N AU - Nagase H AU - Utsunomiya D PY - 2024 JO - Journal of cardiovascular development and disease DO - 10.3390/jcdd11100304 UR - https://doi.org/10.3390/jcdd11100304 ER -
APA
T, M., S, K., A, O., T, I., T, S., E, I., S, S., N, Y., H, N., & D, U. (2024). Improvement of Quantification of Myocardial Synthetic ECV with Second-Generation Deep Learning Reconstruction.. Journal of cardiovascular development and disease. https://doi.org/10.3390/jcdd11100304
Source records
- pubmed · retrieved 2026-09-25T04:28:53.541Z