Patient-Specific AAA Analysis

Updated 1 December 2025

Patient-Specific AAA Analysis is a tailored approach that reconstructs arterial geometry from clinical images to assess rupture risk.
It employs finite element analysis and machine learning to quantify wall stress, strain, and RSII with high precision and reduced bias.
Automated, reproducible workflows integrate segmentation, meshing, and predictive modeling, streamlining clinical decision-making.

Abdominal aortic aneurysm (AAA) analysis in a patient-specific framework encompasses the customized extraction, modeling, simulation, and quantification of rupture-relevant biomechanical and geometric quantities using clinical imaging. The goal is to predict rupture risk, inform surveillance strategies, and support procedural planning by quantifying local vessel stress, strain, kinematics, and growth in an individualized manner. Methodologies span deterministic computational biomechanics, high-resolution kinematic mapping from time-resolved imaging, advanced machine learning for geometry and progression prediction, and efficient, automated pipelines for integration into clinical workflows.

1. Patient-Specific Geometry Acquisition and Reconstruction

The foundation of all individualized AAA analysis is the accurate capture and reconstruction of arterial geometry from clinical imaging modalities—primarily contrast-enhanced CT angiography (CTA), though MRI and 4D-CTA are increasingly used for dynamic analysis. The canonical workflow includes:

Image preprocessing: Noise reduction (e.g., non-local means, Gaussian filtering), intensity normalization to standardize CT Hounsfield units or MRI intensities (Lucio et al., 13 Mar 2025).
Segmentation: Automated or semi-automatic delineation of lumen, wall, and intraluminal thrombus (ILT) using tools such as 3D Slicer, ITK-SNAP, nnU-Net, or PRAEVAorta for AI-assisted segmentation (Ou et al., 1 Oct 2025, Lucio et al., 13 Mar 2025, Jamshidian et al., 23 May 2025, López-Linares et al., 2019).
Centerline and profile extraction: Vessel centerline tracing and cross-sectional radius/ILT thickness measurement along the arc-length (Lucio et al., 13 Mar 2025).
Surface and mesh generation: Marching-cubes or NURBS-based surface extraction, Laplacian/ACVD smoothing, export to watertight STL or VTK meshes (Lucio et al., 13 Mar 2025, Jamshidian et al., 23 May 2025, Alkhatib et al., 2022). Conformal, structured hexahedral meshes (two or more layers through thickness) ensure fast, convergent finite-element solutions for the wall, while Delaunay tetrahedralization is standard for ILT (Alkhatib et al., 2022).
Quality control: Detailed evaluation of mesh metrics—aspect ratio (<3), scaled Jacobian (>0.6), face angles (45–135°), tetrahedral skew (<0.95)—minimizes finite element integration error and simulation bias (Alkhatib et al., 2022).
Temporal alignment: For longitudinal progression or kinematic studies, rigid registration and vertebral referencing ensure cross-frame spatial correspondences (Alblas et al., 2023, Jamshidian et al., 23 May 2025).

2. Biomechanical Modeling: Stress, Strain, and Wall Integrity

Patient-specific AAA biomechanical assessment quantifies local wall stress and related descriptors to improve rupture risk stratification beyond diameter alone.

Finite element analysis (FEA): Patient-specific wall and ILT geometries are discretized—often as 10-node tetrahedra or structured hexahedral layers—for simulation of internal loading (patient blood pressure) (Catlin et al., 24 Nov 2025, Jamshidian et al., 7 Jul 2024, Alkhatib et al., 2022, Miller et al., 2019). Constitutive wall models include:
- Linear elasticity: Used for rapid, single-phase stress recovery; key finding: peak wall stress is robust to unknown cardiac phase and matches nonlinear analysis within clinical uncertainties (phase bias <9%, stress difference <1.1%) (Catlin et al., 24 Nov 2025).
- Nonlinear hyperelasticity: Polymer-type strain energy forms such as Raghavan–Vorp (W = C₁₀(Ī₁−3)+C₂₀(Ī₁−3)²), Mooney–Rivlin, or GOH for fiber-structured anisotropy, are solved to assess large-deformation mechanics (Catlin et al., 24 Nov 2025, Alkhatib et al., 2022, Friesen et al., 2020).
Boundary/loading conditions: Uniform traction at the lumen, rigid or spring-supported ends, and incorporation of patient-specific pressure waveforms. Residual stress corrections (Fung’s Uniform Stress Hypothesis) reduce artificial through-thickness stress gradients (Miller et al., 2019, Catlin et al., 24 Nov 2025).
Extraction of rupture-relevant metrics:
- 99th-percentile maximum principal stress ( $\sigma_{99}$ ): Preferred over absolute maxima for statistical robustness and reduced sensitivity to geometric uncertainties (Catlin et al., 24 Nov 2025, Jamshidian et al., 7 Jul 2024).
- Circumferential (hoop) strain: Computed from normal displacements to the surface and local radius of curvature ( $\varepsilon = u_n/R$ ) (Jamshidian et al., 22 May 2024).
- Wall tension: Membrane resultant of wall stress, insensitive to thickness approximation (Jamshidian et al., 14 Feb 2025).
- Relative Structural Integrity Index (RSII): Defined as normalized local hoop strain divided by tension, yielding a dimensionless compliance map independent of wall properties, pressure, or thickness (Jamshidian et al., 14 Feb 2025). Low RSII denotes greater wall stiffness; high RSII indicates locally compliant (and potentially vulnerable) tissue.

3. Dynamic and Kinematic Analysis: Growth and Temporal Evolution

Patient-specific progression modeling accounts for both instantaneous kinematics and long-term geometrical growth, directly informing surveillance scheduling and intervention windows.

Kinematic quantification: Time-resolved 4D-CTA or ECG-gated acquisitions enable image-registration-based displacement and circumferential strain analysis throughout the cardiac cycle. Strain estimation relies on robust registration, local normal projection, and surface curvature fitting. The 99th-percentile systolic strain typically ranges 2.62–7.31%, lower than in healthy aortas (Jamshidian et al., 22 May 2024, Jamshidian et al., 23 May 2025).
Growth modeling: Longitudinal, surface-based models track patient-specific AAA deformation across multiple scans. SE(3)-equivariant geometric deep networks operate directly on 3D wall meshes, leveraging local morphological (ILT thickness, radius, geodesics), hemodynamic (TAWSS, OSI), and temporal (historic surface change) features, predicting both diameter trajectory and local surface displacement with median errors on the order of 1 mm (Alblas et al., 10 Jun 2025). Continuous implicit neural representations model the spatiotemporal surface evolution as the zero-level set of a learned signed distance function, enabling sub-voxel interpolation and geometry synthesis between irregularly spaced scans (average surface distances 0.72–2.52 mm) (Alblas et al., 2023).
Assessment of geometric uncertainty: Segmentation error—especially systematic bias in wall localization—is the dominant source of strain/stress estimate uncertainty. Absolute geometric uncertainty should not exceed one wall thickness (∼1.5 mm) to guarantee R $^2$ > 0.8 and NRMSE < 0.05 for strain; the 99th-percentile strain is more robust than peak strain to such errors (Sekhavat et al., 16 Sep 2025).

4. Automated, Reproducible, and Software-Driven Workflows

Clinical translation of patient-specific AAA analysis critically depends on robust automation and integration of imaging, modeling, and analysis pipelines:

Turnkey solutions: Fully automated or "one-click" implementations are available (e.g., MATLAB 7-line code and standalone app) that intake STL geometry, patient systolic pressure, and return wall stress metrics and visualizations in under a minute on consumer hardware (Jamshidian et al., 7 Jul 2024).
Open-source toolchains: Python-based AneuPy supports fully scriptable, modular, and customizable geometry construction, meshing (SALOME, GMSH), model parameterization, and interoperability with standard biomechanics solvers (Abaqus, ANSYS, OpenFOAM) (Lucio et al., 13 Mar 2025).
Mesh strategies: Structured hexahedral meshing yields up to 4× speed-ups over unstructured tetrahedral meshing with equivalent accuracy (<5% variation in $\sigma_1$ with 2 vs. 4 layers) and tractable analyst effort (~30 min per case) (Alkhatib et al., 2022).
Integration with downstream analysis: Outputs from segmentation and geometry-reconstruction modules are compatible with FEA/CFD/FSI software and allow direct extraction of clinically relevant indices (diameter, tortuosity, wall thickness, ILT burden) (Lucio et al., 13 Mar 2025, Friesen et al., 2020).

5. Machine Learning, Growth Prediction, and Hybrid Deterministic–Stochastic Models

Data-driven models are increasingly leveraged for segmentation, growth forecasting, and risk stratification:

Deep learning for segmentation and synthesis: Unified multitask conditional diffusion models (AortaDiff) simultaneously synthesize contrast-enhanced CT from non-contrast input and segment the aortic lumen, achieving higher clinical measurement precision (lumen diameter MAE 4.19 mm; Dice score 0.89) than traditional pipelines (Ou et al., 1 Oct 2025). Compact 3D CNNs deliver mean diameter errors <3.5 mm, enabling reliable diameter/growth quantification (López-Linares et al., 2019).
Hybrid predictive modeling: Stochastic pipelines integrate features from deterministic (FEA/CFD/FSI) simulation (e.g., peak wall stress, WSS) with morphological, demographic, and clinical variables in machine learning classifiers (logistic regression, random forests, SVMs). Such hybrid models have achieved area-under-curve (AUC) >0.90 for rupture classification in some studies (Friesen et al., 2020).
Dimensionality reduction and risk prediction: Large cohort modeling using penalized logistic regression and principal-component-based models extends beyond diameter-only rules. Combinations of size, shape, and wall-thickness features optimize prospective rupture risk stratification; theoretical cost-based thresholds (e.g., P(Y=1|X)>0.306) are derived for risk-based surgical decision support (Izbicki et al., 2011).

6. Clinical Implementation, Guidelines, and Controversies

Patient-specific AAA analysis raises important questions regarding clinical impact, standardization, and adoption:

Stress as a rupture marker: Many protocols focus on the 99th-percentile maximum principal stress as an individualized rupture predictor. However, direct correlation between computed stress distributions and symptomatology is lacking: a paper of 19 patient cases showed no significant association between high wall stress and the presence of symptoms (Miller et al., 2019). This suggests that wall stress alone is insufficient; integration with demographic, morphological, and possibly in vivo wall strength indicators is needed.
Relative importance of uncertainty sources: The phase-bias in stress estimates due to single-phase imaging is minor compared to geometric/segmentation uncertainty. Sensitivity analysis demonstrates that phase variations and even linearized-material modeling introduce <10% bias, well within segmentation-induced errors (Catlin et al., 24 Nov 2025, Sekhavat et al., 16 Sep 2025).
Monitoring and intervention criteria: Personalized analysis enables: (1) tuning of surveillance intervals via predicted local surface growth and probability of crossing intervention diameters within set horizons, (2) identification of focal wall-weakness regions (high RSII) for targeted endovascular repair, and (3) sub-voxel monitoring of geometric and mechanical progression leveraging continuous neural shape models (Jamshidian et al., 14 Feb 2025, Alblas et al., 10 Jun 2025, Alblas et al., 2023).
Efficiency and workflow integration: Full deterministic pipelines from raw image to rupture-relevant metrics can be completed in same-day timeframes (typically <2 hours end-to-end) (Alkhatib et al., 2022, Jamshidian et al., 7 Jul 2024).

7. Future Directions

Increasing focus lies on:

Multiphysics coupling: Integration of FSI (partitioned or monolithic solvers) and individualized hemodynamic simulations to resolve wall–flow interactions and their impact on wall weakening (Friesen et al., 2020).
Prospective validation and generalization: Rigorous validation in unselected, diverse patient cohorts with longitudinal data, as well as external-blinded testing of machine learning and geometric deep learning predictors (Alblas et al., 10 Jun 2025).
Unified clinical platforms: Toward automated hybrid pipelines that combine high-fidelity FEA/CFD feature extraction, geometric/morphometric indices, advanced ML risk modeling, and seamless reporting for surgeons and radiologists (Friesen et al., 2020, Jamshidian et al., 7 Jul 2024).
*Translation of dynamic wall integrity indices (RSII, kinematic strain, growth maps) into practical clinical decision algorithms, supplementing or replacing maximal diameter as the dominant selection criterion for AAA repair (Jamshidian et al., 14 Feb 2025, Jamshidian et al., 22 May 2024).