Late gadolinium enhancement cardiovascular magnetic resonance with generative artificial intelligence
The generative AI-based model enhances the image quality of 2D LGE images while reducing the scan time and preserving imaging sharpness. Further evaluation in a large cohort is needed to assess the clinical utility of AI-enhanced LGE images for scar evaluation, as this proof-of-concept study does not provide evidence of an impact on diagnosis.
Authors
- Warren J. Manning (ORCID: https://orcid.org/0000-0001-7199-2543)
- Reza Nezafat (ORCID: https://orcid.org/0000-0002-1963-7138)
- Jennifer Rodriguez (ORCID: https://orcid.org/0000-0002-6291-9173)
- Kelvin Chow (ORCID: https://orcid.org/0000-0003-0698-1746)
- Patrick Pierce (ORCID: https://orcid.org/0000-0002-0774-1827)
- Fahime Ghanbari (ORCID: https://orcid.org/0000-0002-4062-4550)
- Scott E. Johnson (ORCID: https://orcid.org/0000-0002-6925-5373)
- Ömer Burak Demirel (ORCID: https://orcid.org/0000-0003-4726-0590)
- Tess Wallace (ORCID: https://orcid.org/0000-0002-8519-5913)
- Connie W. Tsao (ORCID: https://orcid.org/0000-0003-3104-0681)
- Long Ngo (ORCID: https://orcid.org/0000-0001-9349-7981)
- Adele Carty (ORCID: https://orcid.org/0009-0006-4024-2671)
- Jordan Street
- Kathryn Arcand
- Christopher Hoeger
Institutions
- Beth Israel Deaconess Medical Center (US)
- Harvard University (US)
- Siemens (Canada) (CA)
- Medical Solutions (GB)
- Siemens Healthcare (United States) (US)
Publication Details
- Journal
- Journal of Cardiovascular Magnetic Resonance
- Published
- 2024-11-28
- DOI
- https://doi.org/10.1016/j.jocmr.2024.101127
- Citations
- 5
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- National Institutes of Health