Imaging-derived coronary fractional flow reserve: advances in physics-based, machine learning, and physics-informed methods.
Authors
Affiliations (5)
Affiliations (5)
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, 49931-1295, United States.
- Computer Science, Kennesaw State University Marietta Campus, 1100 South Marietta Pkwy, Marietta, Georgia, 30060-2855, United States.
- Biomedical Engineering, Michigan Technological University College of Engineering, 1400 Townsend Dr, Houghton, Michigan, 49931-1295, United States.
- Department of Cardiology, Medical University of South Carolina, 171 Ashley Avenue, Charleston, South Carolina, 29425, United States.
- Department of Mathematical Sciences, Michigan Technological University, Fisher 313, 1400 Townsend Drive, Houghton, Michigan, 49931-1295, United States.
Abstract
Purpose of Review Imaging-derived fractional flow reserve (FFR) is rapidly evolving beyond conventional computational fluid dynamics (CFD)-based pipelines toward machine learning (ML), deep learning (DL), and physics-informed approaches that enable fast, wire-free, and scalable functional assessment of coronary artery stenosis. This review synthesizes recent advances in computed tomography (CT)-and angiography-based FFR measurement, with particular emphasis on emerging physics-informed neural networks and neural operators (PINNs and PINOs), as well as key considerations for their clinical translation.Recent Findings ML/DL approaches have markedly improved automation and computational speed, enabling prediction of pressure and FFR from anatomical descriptors or angiographic contrast dynamics. However, their real-world performance and generalizability can remain variable and sensitive to domain shift, due to multi-center heterogeneity, interpretability challenges, and differences in acquisition protocols and image quality. Physics-informed learning introduces conservation structure and boundary-condition consistency into model training, improving generalizability and reducing dependence on dense supervision while maintaining rapid inference. Recent evaluation trends increasingly highlight deployment-oriented metrics, including calibration, uncertainty quantification, and quality-control gatekeeping, as essential for safe clinical use.SummaryThe field is converging toward imaging-derived FFR methods that are faster, more automated, and more reliable. While ML/DL offers substantial efficiency gains, physics-informed frameworks such as PINNs and PINOs may provide a more robust balance between speed and physical consistency. Prospective multi-center validation and standardized evaluation will be critical to support broad and safe clinical adoption.