Generation of a Library of Clinically Relevant Virtual Heart Models for Virtual Cardiac Imaging Research

Abstract

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with non-invasive imaging playing a crucial role in its diagnosis and management. However, optimizing these imaging technologies to improve patient outcomes remains a challenge. Virtual imaging trials (VITs) provide a powerful alternative to traditional clinical studies by using computational phantoms, virtual patients that can be imaged with simulated scanners, to systematically evaluate imaging technologies. The 4D extended Cardiac-Torso (XCAT) phantom series is one of the most widely used computational phantoms, providing anatomically detailed whole-body models with cardiac and respiratory motion. Despite their utility, the current XCAT cardiac models are limited in their ability to represent population variability and physiologically informed motion. The existing models are derived from gated 4D CT data of only two healthy individuals (one male, one female), restricting their ability to capture diverse anatomical and functional variations. Furthermore, abnormal cardiac motion is introduced manually, lacking a physiological basis and reducing realism and scalability. To address these limitations, this work explores techniques to develop a new series of 4D beating heart models derived from multiple sets of 4D cardiac-gated CT data. By segmenting and analyzing patient-specific cardiac motion, we first investigate an image-based method to construct a population of anatomically variable heart models that better reflect real-world variability, comparing the motion patterns to values reported in the literature. To further enhance realism, we explore a workflow for generating controlled variations in both normal and abnormal cardiac motion using finite element (FE) simulations. This approach integrates patient-specific electrophysiology and morphological parameters, allowing for the generation of physiologically informed cardiac motion models. These enhanced models can be integrated into whole-body XCAT phantoms to provide a novel population of virtual subjects with realistic anatomical and functional variability. The models can provide a valuable tool for virtual imaging trials, enabling the evaluation of emerging cardiac imaging technologies on a representative range of patient anatomies and motion patterns.