Temporal geometric mapping defines morphoelastic growth model of Type B aortic dissection evolution.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Medical Imaging Analysis

Background:

• The human aorta exhibits complex morphologic changes linked to disease evolution.
• Finite element analysis (FEA) is a tool for predicting aortic pathologic states, but requires biomechanical understanding.
• Current FEA applications are limited by the lack of insight into disease-driven biomechanics.

Purpose of the Study:

• To incorporate geometric data from computed tomography angiography (CTA) into FEA.
• To predict future aortic geometries in patients with aortic disease.
• To establish a biomechanical understanding of aortic disease progression using FEA.

Main Methods:

• Utilized computed tomography angiography (CTA) imaging scans for geometric data.
• Developed patient-specific FEA models to simulate aortic deformation between scans.
• Defined geometric correspondence between sequential patient scans to track changes.
• Analyzed FEA-derived trajectories in a shape-size geometric feature space (δS vs. 1/√A).

Main Results:

• FEA models successfully recreated aortic deformation between two time points for four patients.
• Pathologic growth was identified as a driver of morphologic heterogeneity in the aorta.
• FEA-derived trajectories demonstrated a quantitative increase in the shape index variance (δS).
• The observed increase in δS signifies a deviation from normal physiologic shape changes.

Conclusions:

• Patient-specific FEA, integrating CTA data, can predict aortic disease progression.
• The study quantitatively links biomechanical changes to the geometric evolution of aortic disease.
• FEA-derived shape-size trajectories offer a novel method for monitoring disease progression and heterogeneity.