Is There a Relationship Between Stress in Walls of Abdominal Aortic Aneurysm and Symptoms?

Computation for biomechanical analysis of aortic aneurysms: the importance of computational grid

Aortic wall stress is the most common variable of interest in abdominal aortic aneurysm (AAA) rupture risk assessment. Computation of such stress has been dominated by finite element analysis. However, the effects of finite element (FE) formulation, element quality, and methods of FE mesh construction on the efficiency, robustness, and accuracy of such computation have attracted little attention. In this study, we fill this knowledge gap by comparing the results of the calculated aortic wall stress for ten AAA patients using tetrahedral and hexahedral meshes when varying the FE formulation (displacement-based and hybrid), FE shape functions, spatial integration scheme, and number of elements through the wall thickness.

Comparative study between 1-way and 2-way coupled fluid-structure interaction in numerical simulation of aortic arch aneurysms

Hemodynamic forces are related to pathological variations of the cardiovascular system, and numerical simulations for fluid-structure interaction have been systematically used to analyze the behavior of blood flow and the arterial wall in aortic aneurysms. This paper proposes a comparative analysis of 1-way and 2-way coupled fluid-structure interaction for aortic arch aneurysm. The coupling models of fluid-structure interaction were conducted using 3D geometry of the thoracic aorta from computed tomography. Hyperelastic anisotropic properties were estimated for the Holzapfel arterial wall model. The rheological behavior of the blood was modeled by the Carreau-Yasuda model. The results showed that the 1-way approach tends to underestimate von Mises stress, displacement, and strain over the entire cardiac cycle, compared to the 2-way approach. In contrast, the behavior of the variables of flow field, velocity, wall shear stress, and Reynolds number when coupled by the 1-way model was overestimated at the systolic moment and tends to be equal at the diastolic moment. The quantitative differences found, especially during the systole, suggest the use of 2-way coupling in numerical simulations of aortic arch aneurysms due to the hyperelastic nature of the arterial wall, which leads to a strong iteration between the fluid and the arterial wall.

A quarter of a century biomechanical rupture risk assessment of abdominal aortic aneurysms. Achievements, clinical relevance, and ongoing developments

Abdominal aortic aneurysm (AAA) disease, the local enlargement of the infrarenal aorta, is a serious condition that causes many deaths, especially in men exceeding 65 years of age. Over the past quarter of a century, computational biomechanical models have been developed towards the assessment of AAA risk of rupture, technology that is now on the verge of being integrated within the clinical decision-making process. The modeling of AAA requires a holistic understanding of the clinical problem, in order to set appropriate modeling assumptions and to draw sound conclusions from the simulation results. In this article we summarize and critically discuss the proposed modeling approaches and report the outcome of clinical validation studies for a number of biomechanics-based rupture risk indices. Whilst most of the aspects concerning computational mechanics have already been settled, it is the exploration of the failure properties of the AAA wall and the acquisition of robust input data for simulations that has the greatest potential for the further improvement of this technology.

Prediction of Abdominal Aortic Aneurysm Growth Using Geometric Assessment of Computerized Tomography Images Acquired During the Aneurysm Surveillance Period

OBJECTIVE

We investigated the utility of geometric features for future AAA growth prediction.

BACKGROUND

Novel methods for growth prediction of AAA are recognized as a research priority. Geometric feature have been used to predict cerebral aneurysm rupture, but not examined as predictor of AAA growth.

METHODS

Computerized tomography (CT) scans from patients with infra-renal AAAs were analyzed. Aortic volumes were segmented using an automated pipeline to extract AAA diameter (APD), undulation index (UI), and radius of curvature (RC). Using a prospectively recruited cohort, we first examined the relation between these geometric measurements to patients' demographic features (n = 102). A separate 192 AAA patients with serial CT scans during AAA surveillance were identified from an ongoing clinical database. Multinomial logistic and multiple linear regression models were trained and optimized to predict future AAA growth in these patients.

RESULTS

There was no correlation between the geometric measurements and patients' demographic features. APD (Spearman r = 0.25, P < 0.05), UI (Spearman r = 0.38, P < 0.001) and RC (Spearman r =-0.53, P < 0.001) significantly correlated with annual AAA growth. Using APD, UI, and RC as 3 input variables, the area under receiver operating characteristics curve for predicting slow growth (<2.5 mm/yr) or fast growth (>5 mm/yr) at 12 months are 0.80 and 0.79, respectively. The prediction or growth rate is within 2 mm error in 87% of cases.

CONCLUSIONS

Geometric features of an AAA can predict its future growth. This method can be applied to routine clinical CT scans acquired from patients during their AAA surveillance pathway.

On stress in abdominal aortic aneurysm: Linear versus non-linear analysis and aneurysm rupture risk

We present comprehensive biomechanical analyses of abdominal aortic aneurysms (AAA) for 43 patients. We compare stress magnitudes and stress distributions within arterial walls of abdominal aortic aneurysms (AAA) obtained using two simulation and modelling methods: (a) Fully automated and computationally very efficient linear method embedded in the software platform Biomechanics based Prediction of Aneurysm Rupture Risk (BioPARR), freely available from https://bioparr.mech.uwa.edu.au/; (b) More complex and much more computationally demanding Non-Linear Iterative Stress Analysis (Non-LISA) that uses a non-linear inverse iterative approach and strongly non-linear material model. Both methods predicted localised high stress zones with over 90% of AAA model volume fraction subjected to stress below 20% of the 99th percentile maximum principal stress. However, for the non-linear iterative method, the peak maximum principal stress (and 99th percentile maximum principal stress) was higher and the stress magnitude in the low stress area lower than for the automated linear method embedded in BioPARR. Differences between the stress distributions obtained using the two methods tended to be particularly pronounced in the areas where the AAA curvature was large. Performance of the selected characteristic features of the stress fields (we used 99th percentile maximum principal stress) obtained using BioPARR and Non-LISA in distinguishing between the AAAs that would rupture and remain intact was for practical purposes the same for both methods.

Effect of Vessel Tortuosity on Stress Concentration at the Distal Stent-Vessel Interface: Possible Link With New Entry Formation Through Biomechanical Simulation

A computational approach is used to investigate potential risk factors for distal stent graft-induced new entry (dSINE) in aortic dissection (AD) patients. Patient-specific simulations were performed based on computed tomography images acquired from six AD patients (three dSINE and three non-dSINE) to analyze the correlation between anatomical characteristics and stress/strain distributions. Sensitivity analysis was carried out using idealized models to independently assess the effect of stent graft length, stent tortuosity and wedge apposition angle at the landing zone on key biomechanical variables. Mismatch of biomechanical properties between the stented and nonstented regions led to high stress at the distal stent graft-vessel interface in all patients, as well as shear strain in the neighboring region, which coincides with the location of tear formation. Stress was observed to increase with the increase of stent tortuosity (from 263 kPa at a tortuosity angle of 50 deg to 313 kPa at 30 deg). It was further amplified by stent graft landing at the inflection point of a curve. Malapposition of the stent graft led to an asymmetrical segment within the aorta, therefore changing the location and magnitude of the maximum von Mises stress substantially (up to +25.9% with a +25 deg change in the distal wedge apposition angle). In conclusion, stent tortuosity and wedge apposition angle serve as important risk predictors for dSINE formation in AD patients.

Image, geometry and finite element mesh datasets for analysis of relationship between abdominal aortic aneurysm symptoms and stress in walls of abdominal aortic aneurysm

These datasets contain Computed Tomography (CT) images of 19 patients with Abdominal Aortic Aneurysm (AAA) together with 19 patient-specific geometry data and computational grids (finite element meshes) created from these images applied in the research reported in Journal of Surgical Research article “Is There A Relationship Between Stress in Walls of Abdominal Aortic Aneurysm and Symptoms?”[1].

The images were randomly selected from the retrospective database of University Hospitals Leuven (Leuven, Belgium) and provided to The University of Western Australia's Intelligent Systems for Medicine Laboratory. The analysis was conducted using our freely-available open-source software BioPARR (Joldes et al., 2017) created at The University of Western Australia. The analysis steps include image segmentation to obtain the patient-specific AAA geometry, construction of computational grids (finite element meshes), and AAA stress computation. We use well-established and widely used data file formats (Nearly Raw Raster Data or NRRD for the images, Stereolitography or STL format for geometry, and Abaqus finite element code keyword format for the finite element meshes). This facilitates re-use of our datasets in practically unlimited range of studies that rely on medical image analysis and computational biomechanics to investigate and formulate indicators and predictors of AAA symptoms.