Wall Stress and Geometry Measures in Electively Repaired Abdominal Aortic Aneurysms

Abstract

Abdominal aortic aneurysm (AAA) is a vascular disease characterized by the enlargement of the infrarenal segment of the aorta. A ruptured AAA can cause internal bleeding and carries a high mortality rate, which is why the clinical management of the disease is focused on preventing aneurysm rupture. AAA rupture risk is estimated by the change in maximum diameter over time (i.e., growth rate) or if the diameter reaches a prescribed threshold. The latter is typically 5.5 cm in most clinical centers, at which time surgical intervention is recommended. While a size-based criterion is suitable for most patients who are diagnosed at an early stage of the disease, it is well known that some small AAA rupture or patients become symptomatic prior to a maximum diameter of 5.5 cm. Consequently, the mechanical stress in the aortic wall can also be used as an integral component of a biomechanics-based rupture risk assessment strategy. In this work, we seek to identify geometric characteristics that correlate strongly with wall stress using a sample space of 100 asymptomatic, unruptured, electively repaired AAA models. The segmentation of the clinical images, volume meshing, and quantification of up to 45 geometric measures of each AAA were done using in-house Matlab scripts. Finite element analysis was performed to compute the first principal stress distributions from which three global biomechanical parameters were calculated: peak wall stress, 99 th -percentile wall stress and spatially averaged wall stress. Following a feature reduction approach consisting of Pearson's correlation matrices with Bonferroni correction and linear regressions, a multivariate stepwise regression analysis was conducted to find the geometric measures most highly correlated with each of the biomechanical $

An Objective and Repeatable Sac Isolation Technique for Comparing Biomechanical Metrics in Abdominal Aortic Aneurysms

“…This is a significant reduction in computational time and power than using the smallest rotational increment of 0.5° (processing time of 218 min). Several reports that conducted FEA of AAA used MWS as a biomechanics metric since it provides guidance on whether an aneurysm, in a general sense, is at an elevated stress-state [ 12 , 14 , 15 , 16 , 17 , 19 , 25 ]. The 3D geometric reconstruction needed for computational analysis of AAAs can vary considerably.…”

“…DICOM data was performed using a semi-automated approach with a U-NET convolutional neural network and custom MATLAB code (Mathworks, Natwick, MA, USA) [ 18 , 19 ] ( Figure 1 ). The U-NET was trained using Amazon Web Service’s (Amazon, Seattle, WA, USA) Elastic Compute Cloud and local workstations using multiple graphics processing units [ 20 , 21 ].…”

“…The primary and significant source of variability in AAA biomechanical metrics is the definition of the spatial region over which the metrics are considered. For example, some reports of AAA biomechanics defined their metrics over a region that included the iliac arteries up to and including the superior mesenteric arteries [ 6 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], while some have used only the region from the aortic bifurcation point up to the most distal renal artery. These different definitions do not allow for meaningful comparisons of some biomechanical and morphological metrics between AAA and across studies.…”

A machine leaning approach for abdominal aortic aneurysm severity assessment using geometric, biomechanical, and patient-specific historical clinical features

“…Moreover, it has been shown that in addition to geometric indices such as D max , a variety of biomechanical parameters and material properties affect AAA morphology and behavior [3,[7][8][9]. Wall stress distribution and mainly the peak wall stress -which is directly related to the failure site on the AAA surface -computed using finite element analysis (FEA) have been helpful to comprehensively consider the effects of all mentioned parameters [10][11][12]. Additionally, it has been illustrated that patient-specific historical data, such as the presence of clinical pre-existing conditions or risk factors, such as smoking, hypertension, or chronic kidney disease (CKD) may increase AAA severity and risk of rupture [6,13].…”