Biomechanical assessment predicts aneurysm-related events in patients with abdominal aortic aneurysm

Objective To test whether aneurysm biomechanical ratio (ABR; a dimensionless ratio of wall stress and wall strength) can predict aneurysm related events. Methods In a prospective multicentre clinical study of 295 patients with an abdominal aortic aneurysm (AAA; diameter ≥ 40 mm), three dimensional reconstruction and computational biomechanical analyses were used to compute ABR at baseline. Participants were followed for at least two years and the primary end point was the composite of aneurysm rupture or repair. Results The majority were male (87%), current or former smokers (86%), most (72%) had hypertension (mean ± standard deviation [SD] systolic blood pressure 140 ± 22 mmHg), and mean ± SD baseline diameter was 49.0 ± 6.9 mm. Mean ± SD ABR was 0.49 ± 0.27. Participants were followed up for a mean ± SD of 848 ± 379 days and rupture (n = 13) or repair (n = 102) occurred in 115 (39%) cases. The number of repairs increased across tertiles of ABR: low (n = 24), medium (n = 34), and high ABR (n = 44) (p = .010). Rupture or repair occurred more frequently in those with higher ABR (log rank p = .009) and ABR was independently predictive of this outcome after adjusting for diameter and other clinical risk factors, including sex and smoking (hazard ratio 1.41; 95% confidence interval 1.09–1.83 [p = .010]). Conclusion It has been shown that biomechanical ABR is a strong independent predictor of AAA rupture or repair in a model incorporating known risk factors, including diameter. Determining ABR at baseline could help guide the management of patients with AAA

Numerical Simulation of Anisotropic Tissue Growth Using a Total Lagrangian Formulation

This paper describes a new method for simulating tissue growth which can handle anisotropic changes in volume. The method takes advantage of the total Lagrangian formulation which allows the computation of nodal forces for each element in a finite element mesh based on a theoretical stress-free configuration, obtained by considering the unconstrained anisotropic growth of the considered element. The method allows the modelling of shrinking (atrophy), swelling, or tissue growth and the computation of the resulting mechanical stresses in the surrounding tissue. The steady-state solution is obtained using an explicit integration method and dynamic relaxation. The proposed method allows the coupling of continuum mechanical simulations with underlying growth mechanisms, offering a tool for the multiscale study of tissue growth

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.status: publishe