Artificial intelligence-based bi-ventricular systolic and diastolic volume, ejection fraction using non-contrast ECG-gated cardiac computed tomography
AI-derived ventricular volumes from NCCT show moderate to strong correlations, but EF is underestimated. The derived EF can be a screening tool to rule out significant ventricular dysfunction.
Authors
- Abhiraj Sinha (ORCID: https://orcid.org/0000-0002-7282-6368)
- Juan Carlos Ramírez Giraldo
- Su-Min Chang (ORCID: https://orcid.org/0000-0003-4654-5922)
- Saikiran Rapaka (ORCID: https://orcid.org/0000-0001-6527-0974)
- Kristina A. Hallam (ORCID: https://orcid.org/0000-0002-7387-7861)
- Min‐Fang Chao (ORCID: https://orcid.org/0000-0002-5542-4594)
- Kristian Hay Kragholm (ORCID: https://orcid.org/0000-0001-9629-8670)
- Athira Jacob (ORCID: https://orcid.org/0000-0002-5948-3655)
- Puneet Sharma (ORCID: https://orcid.org/0000-0002-4295-1379)
Institutions
- Houston Methodist (US)
- Kaohsiung Medical University (TW)
- Cornell University (US)
- Aalborg University Hospital (DK)
- Methodist Hospital (US)
- Siemens (United States) (US)
- Siemens Healthcare (United States) (US)
- Weill Cornell Medicine (US)
- Aalborg University (DK)
Publication Details
- Journal
- European Heart Journal - Imaging Methods and Practice
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1093/ehjimp/qyaf121
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00