Abstract:
The 4D extended cardiac-torso (XCAT) phantom has been a valuable tool in medical imaging research, providing a realistic and flexible model of human anatomy and cardiorespiratory motions. However, a significant limitation was its inability to accurately simulate altered functions of the heart resulting from cardiac pathologies, particularly myocardial infarction (MI). This paper addresses this limitation by incorporating a biomechanically realistic left ventricle (LV) finite element (FE) model, capable of defining infarction and its subsequent impact on cardiac function, into the XCAT imaging phantom. This advancement is crucial for enhancing the realism of medical imaging simulations.
The core of this biomechanical integration involves an active and passive FE model of the LV, which meticulously captures the complex mechanical behavior of myocardial tissue, including changes due to infarction. The model can simulate both infarcted (scarred) and non-infarcted regions, accounting for alterations in tissue stiffness and contractility that are characteristic of MI. The implementation allows for variations in the size, transmurality (extent through the wall), and location of the infarction, providing a versatile tool for studying diverse pathological scenarios. To accurately represent cardiac motion, myocardial strains were calculated within the FE model and then used to drive the deformation of the XCAT phantom's heart. This approach ensures that the phantom's motion precisely reflects the underlying biomechanical behavior of the diseased heart.
The resulting phantom, now endowed with a biomechanically realistic infarcted LV, demonstrates significant improvements in simulating pathological cardiac motion and regional wall dysfunction, which are key clinical indicators of MI. This enhanced XCAT phantom is a powerful new tool for a wide range of medical imaging applications, including image reconstruction algorithm development, motion correction techniques, and the quantitative assessment of cardiac function in the presence of disease. By providing a more accurate biomechanical representation of a diseased heart, this research contributes significantly to the advancement of cardiac imaging and diagnosis.
