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.

翻译：心腔内血流模式由心脏腔室与心脏瓣膜的耦合运动塑造，并提供关于心脏功能的重要信息。然而，临床血流成像仍受限于检查时间、噪声、分辨率及三维血流细节不完整等问题。计算流体动力学（CFD）有潜力提供精细的血流量化分析及对治疗结果的预测性见解，但临床转化需要能复现患者特异性测量的框架，同时平衡生理真实性、计算成本与建模复杂度。本文提出了一种基于图像的、患者特异性的计算框架，用于模拟完整心脏的心内血流动力学，在生理保真度与计算效率之间取得平衡。该框架首先采用基于机器学习的分割与网格传播技术，从时间分辨图像中重建运动心脏解剖结构。随后执行CFD模拟以解析变形域中的血流动力学，同时利用电阻浸入式表面（RIS）对全部四个心脏瓣膜进行建模，并实现符合生理学的开闭动力学。该框架被应用于模拟健康成人及复杂先天性心脏病（CHD）患儿的心内血流动力学。在健康案例中，模拟结果复现了生理性压力-容积关系、瓣膜时序及心室涡旋形成。在CHD案例中，模拟出的腔室与血管压力与心导管测量结果一致。模拟流场与4D-Flow MRI在定性上吻合，同时提供了对部分被成像伪影遮蔽的血流结构的高分辨率可视化。健康与CHD案例的对比进一步揭示了CHD心脏中舒张期血流组织的改变及归一化粘性耗散的升高。