Understanding the evolving nature of coronary hemodynamics is crucial for early disease detection and monitoring progression. We require digital twins that mimic a patient’s circulatory system by integrating continuous physiological data and computing hemodynamic patterns over months. Current models match clinical flow measurements but are limited to single heartbeats. To this end, we introduced the longitudinal hemodynamic mapping framework (LHMF), designed to tackle critical challenges: (1) computational intractability of explicit methods; (2) boundary conditions reflecting varying activity states; and (3) accessible computing resources for clinical translation. We show negligible error (0.0002–0.004%) between LHMF and explicit data of 750 heartbeats. We deployed LHMF across traditional and cloud-based platforms, demonstrating high-throughput simulations on heterogeneous systems. Additionally, we established LHMF_C, where hemodynamically similar heartbeats are clustered to avoid redundant simulations, accurately reconstructing longitudinal hemodynamic maps (LHMs). This study captured 3D hemodynamics over 4.5 million heartbeats, paving the way for cardiovascular digital twins.

了解冠状动脉血流动力学的演变性质对于早期疾病检测和监测进展至关重要。我们需要通过集成连续的生理数据和计算几个月的血流动力学模式来模拟患者的循环系统的数字双胞胎。目前的模型与临床流量测量相匹配，但仅限于单次心跳。为此，我们引入了纵向血流动力学映射框架（LHMF），旨在解决以下关键挑战：（1）显式方法的计算困难性； (2)反映不同活动状态的边界条件； (3) 可用于临床翻译的计算资源。我们发现 LHMF 和 750 次心跳的显式数据之间的误差可以忽略不计 (0.0002–0.004%)。我们跨传统平台和基于云的平台部署了 LHMF，展示了异构系统上的高吞吐量模拟。此外，我们建立了 LHMF _C ，其中血流动力学相似的心跳被聚类以避免冗余模拟，从而准确地重建纵向血流动力学图（LHM）。这项研究捕获了超过 450 万次心跳的 3D 血流动力学，为心血管数字双胞胎铺平了道路。