Patient-specific models of blood flow are being used clinically to diagnose and plan treatment for coronary artery disease. A remaining challenge is bridging scales from flow in arteries to the micro-circulation supplying the myocardium. Previously proposed models are descriptive rather than predictive and have not been applied to human data. The goal here is to develop a multiscale patient-specific model enabling blood flow simulation from large coronary arteries to myocardial tissue. Patient vasculatures are segmented from coronary computed tomography angiography data and extended from the image-based model down to the arteriole level using a space-filling forest of synthetic trees. Blood flow is modeled by coupling a 1D model of the coronary arteries to a single-compartment Darcy myocardium model. Simulated results on five patients with non-obstructive coronary artery disease compare overall well to [\(^{15}\)O]\(\text {H}_{{2}}\)O PET exam data for both resting and hyperemic conditions. Results on a patient with severe obstructive disease link coronary artery narrowing with impaired myocardial blood flow, demonstrating the model’s ability to predict myocardial regions with perfusion deficit. This is the first report of a computational model for simulating blood flow from the epicardial coronary arteries to the left ventricle myocardium applied to and validated on human data.

临床上正在使用患者特定的血流模型来诊断和规划冠状动脉疾病的治疗。剩下的一个挑战是将动脉血流与供应心肌的微循环连接起来。以前提出的模型是描述性的而不是预测性的，尚未应用于人类数据。这里的目标是开发一个多尺度的患者特定模型，能够模拟从大冠状动脉到心肌组织的血流。患者脉管系统从冠状动脉计算机断层扫描血管造影数据中分割出来，并使用充满空间的合成树森林从基于图像的模型扩展到小动脉水平。通过将冠状动脉的一维模型与单室达西心肌模型耦合来模拟血流。\(^{15}\) O] \(\text {H}_{{2}}\) O 静息和充血条件下的 PET 检查数据。严重阻塞性疾病患者的结果将冠状动脉狭窄与心肌血流受损联系起来，证明该模型能够预测灌注不足的心肌区域。这是用于模拟从心外膜冠状动脉到左心室心肌的血流的计算模型的第一份报告，该模型应用于人类数据并对其进行了验证。