Work

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

Year: 2024

Type: article

Abstract: The human aorta undergoes complex morphologic changes that mirror the evolution of disease. Finite element analysis (FEA) enables the prediction of aortic pathologic states, but the absence of a biomechanical understanding hinders the applicability of this computational tool. We incorporate geometric information from computed tomography angiography (CTA) imaging scans into FEA to predict a trajectory of future geometries for four aortic disease patients. Through defining a geometric correspondence between two patient scans separated in time, a patient-specific FEA model can recreate the deformation of the aorta between the two time points, showing that pathologic growth drives morphologic heterogeneity. FEA-derived trajectories in a shape-size geometric feature space, which plots the variance of the shape index versus the inverse square root of aortic surface area (δS vs. [Formula: see text] ), quantitatively demonstrate an increase in δS. This represents a deviation from physiologic shape changes and parallels the true geometric progression of aortic disease patients.

Source: Computers in Biology and Medicine

DOI: 10.1016/j.compbiomed.2024.109194

Authors: Kameel Khabaz, Junsung Kim, Ross Milner, Nhung Nguyen, Luka Pocivavsek

Institutions: University of California, Los Angeles, University of Chicago

Language: English

Topic: Aortic Disease and Treatment Approaches

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,640