Structure–Function Relations in the Coronary Vasculature

  • Benjamin Kaimovitz
  • Yunlong Huo
  • Yoram Lanir
  • Ghassan S. KassabEmail author


The function of the coronary system is to deliver blood to the capillary network, to nourish the myocardium and to autoregulate coronary blood flow. The coronary vessels run through cyclically contracting, cardiac muscle. Consequently, intramyocardial pressures have a major influence on vascular transmural pressure and, therefore, on the flow. It is widely acknowledged that the distribution of coronary blood is spatially heterogeneous. Aside from the interaction between myocardial contraction and flow, causing impediment at the local level, the stochastic nature of the coronary tree geometry contributes to the inhomogeneity of the blood flow distribution. Additionally, variations of flow exist from epicardium to endocardium. Flow heterogeneity is further enhanced in pathologies, such as ischemia, which induces vulnerability of the subendocardium to ischemia. The coronary perfusion distribution as well as local coronary flow is difficult to measure, especially in the endocardium. Hence it is not yet fully understood as to what the specific effects cardiac contraction, local neurogenic controls, cardiac and vascular pathologies, and specific therapeutic modalities (e.g., drugs) have on the extent and distribution of coronary perfusion. For this reason, simulation is an attractive methodological alternative. Accordingly, realistic analysis of the flow distribution must be carried out within a framework of a realistic three-dimensional (3D) representation of the coronary geometry and its biological variability. Recent morphological studies facilitate realistic reconstructions of the coronary vasculature to serve as a foundation for simulation of the flow in the entire coronary system.


Right Coronary Artery Coronary Vasculature Great Cardiac Vein Epicardial Vessel Venous Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2016

Authors and Affiliations

  • Benjamin Kaimovitz
    • 1
  • Yunlong Huo
    • 2
  • Yoram Lanir
    • 1
  • Ghassan S. Kassab
    • 1
    Email author
  1. 1.IUPUIIndianapolisUSA
  2. 2.Peking UniversityBeijingChina

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