Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification
The proposed pipeline for personalized and uncertainty-aware predictions of coronary hemodynamics is based on routine clinical measurements and recently developed techniques for CT myocardial perfusion imaging. The proposed pipeline offers significant improvements in precision and reduction in computational cost.
Authors
- Daniele E. Schiavazzi (ORCID: https://orcid.org/0000-0001-9205-5989)
- Andrea Zanoni (ORCID: https://orcid.org/0000-0002-7337-288X)
- Owais Khan
- Karthik Menon (ORCID: https://orcid.org/0000-0002-7494-7585)
- Alison L. Marsden (ORCID: https://orcid.org/0000-0003-1902-171X)
- Gianluca Geraci
- Koen Nieman
Institutions
- University of Notre Dame (US)
- Georgia Institute of Technology (US)
- Sandia National Laboratories (US)
- Stanford Medicine (US)
- Toronto Metropolitan University (CA)
- Stanford University (US)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-07-19
- DOI
- https://doi.org/10.1016/j.cmpb.2025.108951
- Citations
- 5
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 6.80
Funders
- National Science Foundation
- U.S. Department of Energy
- National Institutes of Health
- Directorate for Computer and Information Science and Engineering
- National Nuclear Security Administration
- Stanford Research Computing Center, Stanford University
- National Heart, Lung, and Blood Institute
- Sandia National Laboratories