血流储备分数（FFR）与微循环阻力（IMR）是反映心血管生理功能的关键参数，基于常规冠状动脉CT血管造影（CCTA）准确计算FFR与IMR的临床意义重大。然而，现有方法应用于冠状动脉临界病变的计算精度有限。本研究拟针对FFR与IMR仿真计算的关键问题展开研究。主要研究包括：1）分析钙化斑块对CCTA影像血管管腔低估的影响，实现一种在CCTA影像上矫正晕状伪影并准确重建出冠状动脉树的方法；2）设计一种结合冠状动脉树与病变特征的个体化血流估计方法，实现准确的最大血流量估计，并将其作为计算流体力学仿真的边界条件，进行FFR与IMR的仿真计算；3）利用压力导丝测量的FFR作为参考标准，验证基于CCTA计算FFR的准确性，并分析FFR与IMR的相关关系，为下一步联合应用无创FFR与IMR计算方法优化冠心病的诊断与治疗提供思路。

Fractional flow reserve (FFR) and index of microcirculation resistance (IMR) are two key coronary physiological indices. Computation of FFR and IMR from conventional coronary CT angiography (CCTA) has important clinical implications. However, current approaches have limited accuracy when applied to intermediate coronary stenoses. The goal of this project is to resolve the fundamental limitations that hamper the improved accuracy in computation of FFR and IMR. Main research points include: 1) analyze the relation between calcified plaques and the associated lumen underestimation by CCTA images, and implement a method for accurate reconstruction of coronary tree after correcting the blooming artifact on CCTA images; 2) Design a patient-specific method for accurate estimation of maximum coronary flow at hyperemia, that is used subsequently as boundary condition in the flow simulation by computational fluid dynamics to derive FFR and IMR; 3) validate the accuracy of the computational FFR and IMR, using the pressure wire-based FFR as the reference standard. In addition, the correlation between computational FFR and IMR will be analyzed, so as to facilitate the future applications in integrating computational FFR and IMR for online optimization of the diagnosis of coronary artery disease and its therapeutic strategies.