Biomechanical studies on abdominal aortic aneurysms (AAA) seek to provide for better decision criteria to undergo surgical intervention for AAA repair. More accurate results can be obtained by using appropriate material models for the tissues along with accurate geometric models and more realistic boundary conditions for the lesion. However, patient-specific AAA models are generated from gated medical images in which the artery is under pressure. Therefore, identification of the AAA zero pressure geometry would allow for a more realistic estimate of the aneurysmal wall mechanics. This study proposes a novel iterative algorithm to find the zero pressure geometry of patient-specific AAA models. The methodology allows considering the anisotropic hyperelastic behavior of the aortic wall, its thickness and accounts for the presence of the intraluminal thrombus. Results on 12 patient-specific AAA geometric models indicate that the procedure is computational tractable and efficient, and preserves the global volume of the model. In addition, a comparison of the peak wall stress computed with the zero pressure and CT-based geometries during systole indicates that computations using CT-based geometric models underestimate the peak wall stress by 59 ± 64 and 47 ± 64 kPa for the isotropic and anisotropic material models of the arterial wall, respectively.

Intraluminal thrombus (ILT) is a pseudo-tissue that develops from coagulated blood, and is found in most abdominal aortic aneurysms (AAAs) of clinically relevant size. A number of studies have suggested that ILT mechanical characteristics may be related to AAA risk of rupture, even though there is still great controversy in this regard. ILT is isotropic and inhomogeneous and may appear as a soft (single-layered) or stiff (multilayered fibrotic) tissue. This paper aims to investigate how ILT constitution and topology influence the magnitude and location of peak wall stress (PWS). In total 21 patient-specific AAAs (diameter 4.2-5.4 cm) were reconstructed from computer tomography images and biomechanically analyzed using state-of-the-art modeling assumptions. Results indicated that PWS correlated stronger with ILT volume (ρ = 0.44, p = 0.05) and minimum thickness of ILT layer (ρ = 0.73, p = 0.001) than with maximum AAA diameter (ρ = 0.05, p = 0.82). On average PWS was 20% (SD 12%) higher for FE models that used soft instead of stiff ILT models (p < 0.001). PWS location strongly correlated with sites of minimum ILT thickness in the section of maximum AAA diameter and was independent from ILT stiffness. In addition, ILT heterogeneity, i.e., the spatial composition of soft and stiff thrombus tissue, can considerably influence stress in the AAA wall. The present study is limited to identification of influential biomechanical factors, and how its findings translate to an AAA rupture risk assessment remains to be explored by clinical studies.

In this work, we present a novel method for the derivation of the unloaded geometry of an abdominal aortic aneurysm (AAA) from a pressurized geometry in turn obtained by 3D reconstruction of computed tomography (CT) images. The approach was experimentally validated with an aneurysm phantom loaded with gauge pressures of 80, 120, and 140 mm Hg. The unloaded phantom geometries estimated from these pressurized states were compared to the actual unloaded phantom geometry, resulting in mean nodal surface distances of up to 3.9% of the maximum aneurysm diameter. An in-silico verification was also performed using a patient-specific AAA mesh, resulting in maximum nodal surface distances of 8 lm after running the algorithm for eight iterations. The methodology was then applied to 12 patient-specific AAA for which their corresponding unloaded geometries were generated in 5-8 iterations. The wall mechanics resulting from finite element analysis of the pressurized (CT image-based) and unloaded geometries were compared to quantify the relative importance of using an unloaded geometry for AAA biomechanics. The pressurized AAA models underestimate peak wall stress (quantified by the first principal stress component) on average by 15% compared to the unloaded AAA models. The validation and application of the method, readily compatible with any finite element solver, underscores the importance of generating the unloaded AAA volume mesh prior to using wall stress as a biomechanical marker for rupture risk assessment.