Improvement of Quantification of Myocardial Synthetic ECV with Second-Generation Deep Learning Reconstruction
Synthetic ECV using second-generation DLR demonstrated the lowest bias and LOA compared to laboratory ECV among the four reconstruction methods, suggesting that second-generation DLR enables more accurate quantification.
Authors
- Ayano Onoma
- Tomokazu Sakano
- Shingo Kato (ORCID: https://orcid.org/0000-0002-7545-0669)
- Eiji Ishikawa (ORCID: https://orcid.org/0009-0006-2436-3203)
- Shungo Sawamura
- Daisuke Utsunomiya (ORCID: https://orcid.org/0000-0002-1723-1724)
- Tsubasa Morioka
- Naofumi Yasuda
- Hiroaki Nagase (ORCID: https://orcid.org/0000-0002-9454-9680)
- Toshiharu Izumi
Institutions
- Yokohama City University Hospital (JP)
- Yokohama City University (JP)
Publication Details
- Journal
- Journal of Cardiovascular Development and Disease
- Published
- 2024-10-02
- DOI
- https://doi.org/10.3390/jcdd11100304
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00