基于体外模拟循环的冠状动脉血流动力学实验研究

冠状动脉血栓生成是一个突出的临床问题，针对这一问题的病理研究关系到人们的生命健康，具有重要的研究意义。项目拟从血流动力学的角度对这一问题进行实验研究。项目将研制体外模拟循环系统（MCS）来模拟冠脉的血流动力学过程，对冠脉微循环进行零维模型的实验模拟，冠脉模型采用硅胶模型。实验采用层析PIV测量技术对冠脉血流的三维速度场和压力场进行测量，验证并优化零维模型。采用荧光粒子追踪技术对冠脉溶血/凝血的现象进行实验模拟，来研究血小板沉积形成血栓的血流动力学机理。通过111套关于冠脉血流储备分数研究的CTA临床数据，提取冠脉形态学和血流动力学指标，结合溶血/凝血特性指标，利用机器学习的方法建立反映冠脉溶血/凝血机理的数据驱动模型，实现通过形态学和血流动力学特性预测溶血/凝血发生的概率，为临床进行冠脉病症辅助诊断提供理论依据。

Coronary artery thrombosis is a prominent problem, and the pathological research on this problem has important research significance. This project intends to conduct an experimental study on this issue from the perspective of hemodynamics. The project will develop an in vitro mock circulation system (MCS) to achieve the hemodynamic process of the coronary arteries, and conduct an experimental simulation of a lumped-parameter model of coronary microcirculation. The coronary arterial model is made of a silicone model. The experiment uses tomographic particle image velocimetry to measure the three-dimensional velocity field and pressure field of coronary blood flow to verify and optimize the lumped-parameter model. Fluorescent particle tracking technology is used to simulate the coronary hemolysis/coagulation phenomenon to study the hemodynamic mechanism. Through the 111 sets of CTA data from a study of fractional flow reserve, the coronary morphology and hemodynamic indexes were extracted, combined with the hemolysis/coagulation characteristics indexes, and the machine learning method was used to establish a data-driven model reflecting coronary hemolysis/coagulation mechanism. The data-driven model realizes the prediction of the occurrence of hemolysis/coagulation through morphological and hemodynamic characteristics, and provides a theoretical support for the clinical diagnosis of coronary artery disease.