Model Generation of the Extended Coronary Vasculature for More Realistic Simulation of Coronary Artery Disease

Abstract

Virtual imaging trials (VITs) are a vital tool for medical imaging research. The four-dimensional extended cardiac-torso (XCAT) phantom is extensively used in these simulations due to its realistic computational model of human anatomy, including cardiac and respiratory motions. However, the current XCAT phantom has limitations in terms of its representation of the beating heart, such as the absence of a detailed model of the extended coronary vasculature and restricted capabilities to simulate disease. This study aims to address these limitations by applying a modern tree generation algorithm to extend the coronary vasculature within XCAT heart models, and integrating this with models for motion, coronary plaque generation and insertion, and blood flow to create a framework to simulate diseased states of the heart. The growth algorithm was applied to a new series of XCAT cardiac models based upon 4D CT imaging data from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) national clinical trial. Complete coronary vasculature was generated for each heart, growing from the initial larger branches of the vessels defined by segmentation. Combined with previously developed tools to simulate cardiac motion, coronary plaques of various sizes and composition, and blood flow, this enhanced vasculature provides the foundation for more realistic simulation of coronary artery disease. Plaques can be placed within the vessels, and the downstream blood flow can be analyzed to determine the affected myocardium and alter its motion. We demonstrate the utility of this framework through an example CT simulation comparing plaque visualization with standard and photon-counting CT. The results illustrate the significant potential of these tools to simulate both normal and abnormal cases to evaluate cardiac imaging techniques.