A simple, effective and clinically applicable method to compute abdominal aortic aneurysm wall stress

A new inverse method for estimation of in vivo mechanical properties of the aortic wall

The aortic wall is always loaded in vivo, which makes it challenging to estimate the material parameters of its nonlinear, anisotropic constitutive equation from in vivo image data. Previous approaches largely relied on either computationally expensive finite element models or simplifications of the geometry or material models. In this study, we investigated a new inverse method based on aortic wall stress computation. This approach consists of the following two steps: (1) computing an "almost true" stress field from the in vivo geometries and loading conditions, (2) building an objective function based on the "almost true" stress fields, constitutive equations and deformation relations, and estimating the material parameters by minimizing the objective function. The method was validated through numerical experiments by using the in vivo data from four ascending aortic aneurysm (AsAA) patients. The results demonstrated that the method is computationally efficient. This novel approach may facilitate the personalized biomechanical analysis of aortic tissues in clinical applications, such as in the rupture risk analysis of ascending aortic aneurysms.

Correlation between transversal and orthogonal maximal diameters of abdominal aortic aneurysms and alternative rupture risk predictors

PURPOSE

There is no standard for measuring maximal diameter (Dmax) of abdominal aortic aneurysm (AAA) from computer tomography (CT) images although differences between Dmax evaluated from transversal (axialDmax) or orthogonal (orthoDmax) planes can be large especially for angulated AAAs. Therefore we investigated their correlations with alternative rupture risk indicators as peak wall stress (PWS) and peak wall rupture risk (PWRR) to decide which Dmax is more relevant in AAA rupture risk assessment.

MATERIAL AND METHODS

The Dmax values were measured by a trained radiologist from 70 collected CT scans, and the corresponding PWS and PWRR were evaluated using Finite Element Analysis (FEA). The cohort was ordered according to the difference between axialDmax and orthoDmax (D_a-o) quantifying the aneurysm angulation, and Spearman's correlation coefficients between PWS/PWRR - orthoDmax/axialDmax were calculated.

RESULTS

The calculated correlations PWS/PWRR vs. orthoDmax were substantially higher for angulated AAAs (with D_a-o≥3mm). Under this limit, the correlations were almost the same for both Dmax values. Analysis of AAAs divided into two groups of angulated (n=38) and straight (n=32) cases revealed that both groups are similar in all parameters (orthoDmax, PWS, PWRR) with the exception of axialDmax (p=0.024).

CONCLUSIONS

It was confirmed that orthoDmax is better correlated with the alternative rupture risk predictors PWS and PWRR for angulated AAAs (D_A-O≥3mm) while there is no difference between orthoDmax and axialDmax for straight AAAs (D_A-O<3mm). As angulated AAAs represent a significant portion of cases it can be recommended to use orthoDmax as the only Dmax parameter for AAA rupture risk assessment.

From Finite Element Meshes to Clouds of Points: A Review of Methods for Generation of Computational Biomechanics Models for Patient-Specific Applications

It has been envisaged that advances in computing and engineering technologies could extend surgeons' ability to plan and carry out surgical interventions more accurately and with less trauma. The progress in this area depends crucially on the ability to create robustly and rapidly patient-specific biomechanical models. We focus on methods for generation of patient-specific computational grids used for solving partial differential equations governing the mechanics of the body organs. We review state-of-the-art in this area and provide suggestions for future research. To provide a complete picture of the field of patient-specific model generation, we also discuss methods for identifying and assigning patient-specific material properties of tissues and boundary conditions.