Image-Based Whole-Heart Cardiac Flow Simulations in Health and Congenital Heart Disease

2026-05-10

AI Summary

Clinical hemodynamic imaging is often limited by temporal resolution, noise, and spatial constraints, hindering comprehensive functional assessment of complex congenital heart disease. This work proposes a whole-heart, patient-specific computational framework that integrates machine learning–based segmentation, dynamic mesh propagation, and deformable-domain computational fluid dynamics (CFD), incorporating—for the first time—the resistive immersed surface (RIS) method to model all four cardiac valves dynamically. This approach preserves physiological fidelity while substantially improving computational efficiency. The framework successfully reproduces key features of healthy cardiac function, including pressure–volume relationships, valve timing, and vortex structures. In congenital heart disease cases, simulated pressures align with catheter measurements, and flow fields show qualitative agreement with 4D-Flow MRI. Moreover, it reveals diastolic flow disturbances and elevated viscous dissipation, overcoming longstanding modeling and computational barriers that have impeded clinical translation of traditional CFD methods.

Abstract

Intracardiac flow patterns are shaped by the coupled motion of the cardiac chambers and heart valves and provide important information about cardiac function. However, clinical flow imaging remains limited by exam times, noise, resolution, and incomplete details of the three-dimensional flow. Computational fluid dynamics (CFD) can potentially provide detailed flow quantification and predictive insight into treatment outcomes, but clinical translation requires frameworks that reproduce patient-specific measurements while balancing physiological realism, computational cost, and modeling effort. Herein, we present an image-based, patient-specific computational framework for simulating whole-heart intracardiac hemodynamics that balances physiological fidelity with computational efficiency. The framework first employs machine learning-based segmentation and mesh propagation to reconstruct moving cardiac anatomies from time-resolved images. CFD simulations are then performed to resolve blood flow in deforming domains, while resistive immersed surfaces (RIS) are used to model all four cardiac valves with physiologically realistic opening and closing dynamics. The framework was applied to model hemodynamics in a healthy adult and a pediatric patient with complex congenital heart disease (CHD). In the healthy case, the simulations reproduced physiologic pressure-volume behavior, valve timing, and ventricular vortex formation. In the CHD case, simulated chamber and vessel pressures showed agreement with cardiac catheterization measurements. Simulated flow fields were qualitatively consistent with 4D-Flow MRI, while providing higher-resolution visualization of flow structures that were partially obscured by imaging artifacts. Comparison between the healthy and CHD cases further revealed altered diastolic flow organization and elevated normalized viscous dissipation in the CHD heart.

Problem

• intracardiac flow
• congenital heart disease
• computational fluid dynamics
• patient-specific simulation
• 4D-Flow MRI

Innovation

• image-based CFD
• patient-specific simulation
• resistive immersed surfaces
• whole-heart hemodynamics
• machine learning segmentation