A Patient-Specific Morphoelastic Growth Model of Aortic Dissection Evolution.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Medical Imaging Analysis

Background:

• Aortic diseases involve complex morphologic changes.
• Finite element analysis (FEA) can predict aortic pathologic states.
• A biomechanical understanding is crucial for FEA applicability in aortic disease.

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 demonstrate how pathologic growth drives morphologic heterogeneity.

Main Methods:

• Utilized geometric information from serial CTA scans of four aortic disease patients.
• Developed patient-specific FEA models to recreate aortic deformation between scans.
• Defined geometric correspondence to track aorta changes over time.

Main Results:

• Simulated aortic deformation between two time points, revealing pathologic growth.
• Quantitatively demonstrated simulated breakdown in aortic shape using a shape-size geometric feature space (δ𝒮).
• Showed that an increase in δ𝒮 closely parallels the true geometric progression of aortic disease.

Conclusions:

• Patient-specific FEA models integrating CTA data can predict aortic disease progression.
• Pathologic growth is a key driver of morphologic heterogeneity in the aorta.
• The δ𝒮 metric effectively quantifies geometric changes indicative of aortic disease evolution.