Impact of Geometric Uncertainty on the Computation of Abdominal Aortic Aneurysm Wall Strain

2025-09-15

AI Summary

Rupture risk assessment for abdominal aortic aneurysms (AAAs) relies primarily on diameter and growth rate, offering limited individualized predictive capability. While wall stress/strain analysis holds promise, its clinical utility is hindered by geometric uncertainties—particularly from image segmentation—and the underlying mechanisms governing wall strain remain poorly understood. Method: This study systematically quantifies, for the first time, the impact of geometric uncertainty on computational wall strain estimation. Using longitudinal 4D-CTA data, we integrate deformable image registration with surface-normal geometric perturbation modeling—including both stochastic variations and systematic boundary shifts—to perform comprehensive uncertainty analysis. Results: Inward boundary shifts induce significantly larger strain errors than outward shifts; peak strain exhibits high sensitivity but poor robustness, whereas the 99th-percentile strain demonstrates superior stability. To ensure reliable individualized risk assessment, geometric errors must be constrained within the typical wall thickness (~1.5 mm). These findings provide critical accuracy benchmarks for strain-based AAA risk stratification.

Technology Category

• Reasoning under Uncertainty: Uncertainty Representations
• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by permanent enlargement of the aorta, often detected incidentally during imaging for unrelated conditions. Current management relies primarily on aneurysm diameter and growth rate, which may not reliably predict patient-specific rupture risk. Computation of AAA wall stress and strain has the potential to improve individualized risk assessment, but these analyses depend on image-derived geometry, which is subject to segmentation uncertainty and lacks a definitive ground truth for the wall boundary. While the effect of geometric uncertainty on wall stress has been studied, its influence on wall strain remains unclear. In this study, we assessed the impact of geometric uncertainty on AAA wall strain computed using deformable image registration of time-resolved 3D computed tomography angiography (4D-CTA). Controlled perturbations were applied to the wall geometry along the surface normal, parameterized by the standard deviation for random variation and the mean for systematic inward or outward bias, both scaled relative to wall thickness. Results show that uncertainties in AAA wall geometry reduce the accuracy of computed strain, with inward bias (toward the blood lumen and intraluminal thrombus) consistently causing greater deviations than outward bias (toward regions external to the aortic wall). Peak strain is more sensitive but less robust, whereas the 99th percentile strain remains more stable under perturbations. We concluded that, for sufficiently accurate strain estimation, geometric uncertainty should remain within one wall thickness (typically 1.5 mm).

Problem

• Assessing geometric uncertainty's impact on AAA wall strain computation
• Evaluating segmentation errors on aneurysm strain analysis accuracy
• Determining tolerable geometric error bounds for reliable strain estimation

Innovation

• Deformable image registration for 4D-CTA
• Controlled geometric perturbations along surface normal
• Quantifying uncertainty impact on wall strain accuracy