Geometric Predictors of Abdominal Aortic Aneurysm Maximum Wall Stress

Increased tortuosity of ACA might be associated with increased risk of ACoA aneurysm development and less aneurysm dome size: a computer-aided analysis

Objectives

We decided to perform computer-aided analysis of the anterior cerebral artery (ACA) to check for a potential correlation with anterior communicating artery (ACoA) aneurysm presence and growth.

Methods

We retrospectively analyzed the ACA anatomy of 121 patients with ACoA aneurysms along with 121 age, risk factors, and vessel side-matched control patients without an ACoA aneurysm. We obtained their medical history and digital subtraction angiography (DSA) data from their medical records. For each patient’s DSA, we extracted curve representing the course of their ACA and calculated its relative length (RL), sum of angle metrics (SOAM), triangular index (TI), product of angle distance (PAD), and inflection count metrics (ICM).

Results

Patients with ACoA aneurysm had significantly higher RL (0.64 ± 0.23 vs. 0.56 ± 0.22; p < 0.01), SOAM (0.27 ± 0.19 vs. 0.18 ± 0.15; p < 0.01), PAD (0.12 ± 0.13 vs. 0.09 ± 0.11; p = 0.02), and TI (0.57 ± 0.14 vs. 0.44 ± 0.15; p < 0.01). In multivariate logistic regression analysis, after adjustment for possible confounders, SOAM (OR, 1.34; 95% CI, 1.12–1.63; p < 0.01) and TI (OR, 1.84; 95% CI, 1.47–2.35; p < 0.01) remained independently associated with higher risk of ACoA aneurysm. Additionally, we found significant negative correlations between TI and aneurysm dome size (R = − 0.194; p = 0.047).

Conclusions

Increased tortuosity of ACA might increase the risk of ACoA aneurysm development and decrease the risk of aneurysm growth.

Key Points

• Anterior cerebral artery’s sum of angle metrics is associated with hypertension as well as with history of ischemic stroke and myocardial infarction.

• Increased tortuosity of anterior cerebral artery might be associated with anterior communicating artery aneurysm development.

• Tortuosity of anterior cerebral artery is negatively correlated with anterior communicating artery aneurysm dome size.

Wall Stress and Geometry Measures in Electively Repaired Abdominal Aortic Aneurysms

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, 99th 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 parameters. Our findings indicate that wall stress can be predicted by geometric indices with an accuracy of up to 94% when AAA models are generated with uniform wall thickness and up to 67% for patient specific, non-uniform wall thickness AAA. These geometric predictors of wall stress could be used in lieu of complex finite element models as part of a geometry-based protocol for rupture risk assessment.

Gaussian Process Regressions for Inverse Problems and Parameter Searches in Models of Ventricular Mechanics

Patient specific models of ventricular mechanics require the optimization of their many parameters under the uncertainties associated with imaging of cardiac function. We present a strategy to reduce the complexity of parametric searches for 3-D FE models of left ventricular contraction. The study employs automatic image segmentation and analysis of an image database to gain geometric features for several classes of patients. Statistical distributions of geometric parameters are then used to design parametric studies investigating the effects of: (1) passive material properties during ventricular filling, and (2) infarct geometry on ventricular contraction in patients after a heart attack. Gaussian Process regression is used in both cases to build statistical models trained on the results of biophysical FEM simulations. The first statistical model estimates unloaded configurations based on either the intraventricular pressure or the end-diastolic fiber strain. The technique provides an alternative to the standard fixed-point iteration algorithm, which is more computationally expensive when used to unload more than 10 ventricles. The second statistical model captures the effects of varying infarct geometries on cardiac output. For training, we designed high resolution models of non-transmural infarcts including refinements of the border zone around the lesion. This study is a first effort in developing a platform combining HPC models and machine learning to investigate cardiac function in heart failure patients with the goal of assisting clinical diagnostics.