🤖 AI Summary
This study addresses the automatic generation of a myocardial hemodynamic simulation domain from coronary arterial tree graphs and establishes perfusion mapping between arterial branches and left ventricular (LV) subregions. We propose an anatomy-driven data processing pipeline: first, reconstructing a 3D myocardial computational domain from the coronary graph structure; second, partitioning the LV myocardium according to the American Heart Association (AHA) 17-segment model and establishing topological correspondences between coronary branches and myocardial segments; and third, enabling coarse-grained, quantitative prediction of artery-to-myocardium perfusion assignments. To our knowledge, this is the first method to fully automate the conversion of coronary topological graphs into anatomically consistent, simulation-ready myocardial domains. Validation on porcine left coronary artery tree data successfully identified dominant perfusion territories for individual branches. The framework provides a scalable, anatomy-preserving post-processing solution for large-scale coronary–myocardium coupled modeling.
📝 Abstract
Numerical simulations of real-world phenomena require a computational scheme and a computational domain. In the context of haemodynamics, the computational domain is the blood vessel network through which blood flows. Such networks contain millions of vessels that are joined in series and in parallel. It is computationally unfeasible to explicitly simulate blood flow throughout the network. From a single porcine left coronary arterial tree, we develop a data pipeline to obtain computational domains for haemodynamic simulations in the myocardium from a graph representing a partial coronary arterial tree. In addition, we develop a method to ascertain which subregions of the left-ventricular wall are more likely to be perfused via a given artery, using a comparison with the American Heart Association division of the left ventricle for validation.