Virtual myocardial blood flow and flow reserve from static PET imaging using artificial intelligence
AI-predicted virtual stress MBF and MFR assessment using static and gated PET data is feasible and generalizable across cohorts. By removing the dependency on dynamic acquisitions, this approach has the potential to broaden the clinical adoption of flow quantification.
Authors
- Mark H. Lemley (ORCID: https://orcid.org/0000-0002-1531-880X)
- Gilbert Ramı́rez (ORCID: https://orcid.org/0000-0001-7559-3646)
- Jacek Kwieciński (ORCID: https://orcid.org/0000-0001-8202-6359)
- Wanda Acampa (ORCID: https://orcid.org/0000-0003-2187-4076)
- Meghana Urs
- Piotr J Slomka
- Steve Mason
- René R S Packard
- Mouaz Al-Mallah
- Robert DeKemp
- Damini Dey
- Panithaya Chareonthaitawee
- Robert J.H Miller
- Aakash Shanbhag
- Viet T Le
- Daniel S Berman
- Marcelo Di Carli
- Aditya Killekar
Institutions
- Cedars-Sinai Medical Center (US)
- Brigham and Women's Hospital (US)
- West Los Angeles College (US)
- Houston Methodist (US)
- University of Ottawa (CA)
- University of California, Los Angeles (US)
- University of Calgary (CA)
- University of Utah (US)
- Intermountain Medical Center (US)
- Mayo Clinic in Arizona (US)
- Institute of Cardiology (PL)
- University of Naples Federico II (IT)
Publication Details
- Journal
- medRxiv
- Published
- 2026-02-05
- DOI
- https://doi.org/10.64898/2026.02.03.26345376
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Amgen
- Gilead Sciences
- Alnylam Pharmaceuticals
- Cedars-Sinai Medical Center
- Novo Nordisk
- Sun Pharma
- National Institutes of Health
- National Heart, Lung, and Blood Institute