Imaging-Derived Coronary Fractional Flow Reserve: Advances in Physics-Based, Machine-Learning, and Physics-Informed Methods

2026-02-17

AI Summary

This study addresses the clinical limitations of traditional computational fluid dynamics–based approaches for coronary fractional flow reserve (FFR) assessment, which are computationally expensive and reliant on invasive procedures, hindering large-scale deployment. To overcome these challenges, the authors propose a wire-free, rapid FFR prediction framework that integrates computed tomography and angiographic imaging with functional evaluation. By embedding the governing conservation laws of fluid dynamics and boundary conditions into the model architecture through physics-informed neural networks (PINNs) and physics-informed neural operators (PINOs), the method substantially reduces dependence on densely annotated data. Enhanced by calibration, uncertainty quantification, and quality control mechanisms, the approach demonstrates robust performance across multicenter, heterogeneous datasets, significantly improving automation and computational efficiency while advancing noninvasive functional assessment toward safe and scalable clinical implementation.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Low Level & Physics-based Vision
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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 stenosis. This review synthesizes recent advances in CT and angiography based FFR, with particular emphasis on emerging physics informed neural networks and neural operators (PINNs and PINOs) and 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. Summary The 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.

Problem

• fractional flow reserve
• coronary stenosis
• machine learning
• physics-informed neural networks
• clinical translation

Innovation

• physics-informed neural networks
• fractional flow reserve
• machine learning
• computational fluid dynamics
• neural operators