The Role of Geometric Parameters in the Prediction of Abdominal Aortic Aneurysm Wall Stress

Aortic tissue stiffness and tensile strength are correlated with density changes following proteolytic treatment

INTRODUCTION

Dissection or rupture of the aorta is accompanied by high mortality rates, and there is a pressing need for better prediction of these events for improved patient management and clinical outcomes. Biomechanically, these events represent a situation wherein the locally acting wall stress exceed the local tissue strength. Based on recent reports for polymers, we hypothesized that aortic tissue failure strength and stiffness are directly associated with tissue mass density. The objective of this work was to test this novel hypothesis for porcine thoracic aorta.

METHODS

Three tissue specimens from freshly harvested porcine thoracic aorta were treated with either collagenase or elastase to selectively degrade structural proteins in the tissue, or with phosphate buffer saline (control). The tissue mass and volume of each specimen were measured before and after treatment to allow for density calculation, then mechanically tested to failure under uniaxial extension.

RESULTS

Protease treatments resulted in statistically significant tissue density reduction (sham vs. collagenase p = 0.02 and sham vs elastase p = 0.003), which in turn was significantly and directly correlated with both ultimate tensile strength (sham vs. collagenase p = 0.02 and sham vs elastase p = 0.03) and tangent modulus (sham vs. collagenase p = 0.007 and sham vs elastase p = 0.03).

CONCLUSIONS

This work demonstrates for the first time that tissue stiffness and tensile strength are directly correlated with tissue density in proteolytically-treated aorta. These findings constitute an important step towards understanding aortic tissue failure mechanisms and could potentially be leveraged for non-invasive aortic strength assessment through density measurements, which could have implications to clinical care.

Narrative Review of Risk Assessment of Abdominal Aortic Aneurysm Rupture Based on Biomechanics-Related Morphology

CLINICAL IMPACT

Studies have shown that the biomechanical indicators based on multi-scale models are more effective in accurately assessing the rupture risk of AAA. To meet the need for clinical monitoring and rapid decision making, the typical morphological parameters associated with AAA rupture and their relationships with the mechanical environment have been summarized, which provide a reference for clinical preoperative risk assessment of AAA.

Uncertainty quantification of the wall thickness and stiffness in an idealized dissected aorta

Personalized treatment informed by computational models has the potential to markedly improve the outcome for patients with a type B aortic dissection. However, existing computational models of dissected walls significantly simplify the characteristic false lumen, tears and/or material behavior. Moreover, the patient-specific wall thickness and stiffness cannot be accurately captured non-invasively in clinical practice, which inevitably leads to assumptions in these wall models. It is important to evaluate the impact of the corresponding uncertainty on the predicted wall deformations and stress, which are both key outcome indicators for treatment optimization. Therefore, a physiology-inspired finite element framework was proposed to model the wall deformation and stress of a type B aortic dissection at diastolic and systolic pressure. Based on this framework, 300 finite element analyses, sampled with a Latin hypercube, were performed to assess the global uncertainty, introduced by 4 uncertain wall thickness and stiffness input parameters, on 4 displacement and stress output parameters. The specific impact of each input parameter was estimated using Gaussian process regression, as surrogate model of the finite element framework, and a δ moment-independent analysis. The global uncertainty analysis indicated minor differences between the uncertainty at diastolic and systolic pressure. For all output parameters, the 4th quartile contained the major fraction of the uncertainty. The parameter-specific uncertainty analysis elucidated that the material stiffness and relative thickness of the dissected membrane were the respective main determinants of the wall deformation and stress. The uncertainty analysis provides insight into the effect of uncertain wall thickness and stiffness parameters on the predicted deformation and stress. Moreover, it emphasizes the need for probabilistic rather than deterministic predictions for clinical decision making in aortic dissections.

An artificial intelligence based abdominal aortic aneurysm prognosis classifier to predict patient outcomes

Abdominal aortic aneurysms (AAA) have been rigorously investigated to understand when their clinically-estimated risk of rupture-an event that is the 13th leading cause of death in the US-exceeds the risk associated with repair. Yet the current clinical guideline remains a one-size-fits-all "maximum diameter criterion" whereby AAA exceeding a threshold diameter is thought to make the risk of rupture high enough to warrant intervention. However, between 7 and 23.4% of smaller-sized AAA have been reported to rupture with diameters below the threshold. In this study, we train and assess machine learning models using clinical, biomechanical, and morphological indices from 381 patients to develop an aneurysm prognosis classifier to predict one of three outcomes for a given AAA patient: their AAA will remain stable, their AAA will require repair based as currently indicated from the maximum diameter criterion, or their AAA will rupture. This study represents the largest cohort of AAA patients that utilizes the first available medical image and clinical data to classify patient outcomes. The APC model therefore represents a potential clinical tool to striate specific patient outcomes using machine learning models and patient-specific image-based (biomechanical and morphological) and clinical data as input. Such a tool could greatly assist clinicians in their management decisions for patients with AAA.

Evaluation of associations between outflow morphology and rupture risk of abdominal aortic aneurysms

PURPOSE

This study aimed to evaluate the association between the outflow morphology and abdominal aortic aneurysm (AAA) rupture risk, to find risk factors for future prediction models.

MATERIALS AND METHODS

We retrospectively analyzed 46 patients with ruptured AAAs and 46 patients with stable AAAs using a 1:1 match for sex, age, and maximum aneurysm diameter. The chi-square test, paired t-test, and Wilcoxon signed-rank test were used to compare variables. Logistic regression was performed to evaluate variables potentially associated with AAA rupture. Receiver operating characteristic curve analysis and the area under the curve (AUC) were used to assess the regression models.

RESULTS

Ruptured AAAs had a shorter proximal aortic neck (median (interquartile range, IQR): 24.0 (9.4-34.2) mm vs. 33.3 (20.0-52.8) mm, p = 0.004), higher tortuosity (median(IQR): 1.35 (1.23-1.49) vs. 1.29 (1.23-1.39), p = 0.036), and smaller minimum luminal area of the right common iliac artery (CIA) (median (IQR): 86.7 (69.9-126.4) mm2 vs. 118.9 (86.3-164.1)mm2, p = 0.001) and left CIA (median(IQR): 92.2 (67.3,125.1) mm2 vs. 110.7 (80.12, 161.1) mm2, p = 0.010) than stable AAA did. Multiple regression analysis demonstrated significant associations of the minimum luminal area of the bilateral CIAs (odds ratio [OR] = 0.996, 95 % confidence interval [CI] 0.991-0.999, p = 0.037), neck length (OR = 0.969, 95 % CI 0.941-0.993, p = 0.017), and aneurysm tortuosity (OR = 1.031, 95 % CI 1.003-1.063, p = 0.038) with ruptured AAAs. The AUC of this regression model was 0.762 (95 % CI 0.664-0.860, p < 0.001).

CONCLUSIONS

The smaller minimum luminal area of the CIA is associated with an increased risk of rupture. This study highlights the potential of utilizing outflow parameters as novel and additional tools in risk assessment. It also provides a compelling rationale to further intensify research in this area.

Geometry and flow in ascending aortic aneurysms are influenced by left ventricular outflow tract orientation: Detecting increased wall shear stress on the outer curve of proximal aortic aneurysms

BACKGROUND

The geometrical characterization of ascending thoracic aortic aneurysms in clinical practice is limited to diameter measurements. Despite growing interest in hemodynamic assessment, its relationship with ascending thoracic aortic aneurysm pathogenesis is poorly understood. This study examines the relationship between geometry of the ventriculo-aortic junction and blood flow patterns in ascending thoracic aortic aneurysm disease.

METHODS

Thirty-three patients with ascending thoracic aortic aneurysms (exclusions: bicuspid aortic valves, connective tissue disease) underwent 4-dimensional flow magnetic resonance imaging. After image segmentation, geometrical parameters were measured, including aortic curvature, tortuosity, length, and diameter. A unique angular measurement made by the trajectory of the left ventricular outflow tract axis and the proximal aorta was also conducted. Velocity profiles were quantitatively and qualitatively analyzed. In addition, 11 patients (33%) underwent wall shear stress mapping of the ascending thoracic aortic aneurysm region using computational fluid dynamics simulation.

RESULTS

Greater left ventricular outflow tract aortic angles were associated with larger aortic diameters at the levels of the sinus (coefficient = 0.387, P = .014) and ascending aorta (coefficient = 0.284, P = .031). Patients with left ventricular outflow tract aortic angles greater than 60° had marked asymmetric flow acceleration on the outer curvature in the proximal aorta, ascertained from 4-dimensional flow analysis. For patients undergoing computational fluid dynamics assessment, regression analysis found that higher left ventricular outflow tract aortic angles were associated with significantly higher wall shear stress values in the outer curve of the aorta (coefficient 0.07, 95% confidence interval 0.04-0.11, P = .002): Angles greater than 50° yielded time-averaged wall shear stress values greater than 2.5 Pa, exhibiting a linear relationship.

CONCLUSIONS

Our findings strengthen the hypothesis of flow-mediated ascending thoracic aortic aneurysm disease progression and that left ventricular outflow tract aortic angle may be a predictor of disease severity.

Effect of Intraluminal Thrombus Burden on the Risk of Abdominal Aortic Aneurysm Rupture

Abdominal aortic aneurysm (AAA) is a critical health disorder, where the abdominal aorta dilates more than 50% of its normal diameter. Enlargement in abdominal aorta alters the hemodynamics and flow-induced forces on the AAA wall. Depending on the flow conditions, the hemodynamic forces on the wall may result in excessive mechanical stresses that lead to AAA rupture. The risk of rupture can be predicted using advanced computational techniques such as computational fluid dynamics (CFD) and fluid-structure interaction (FSI). For a reliable rupture risk assessment, formation of intraluminal thrombus (ILT) and uncertainty in arterial material properties should be taken into account, mainly due to the patient-specific differences and unknowns in AAAs. In this study, AAA models are computationally investigated by performing CFD simulations combined with FSI analysis. Various levels of ILT burdens are artificially generated in a realistic AAA geometry, and the peak effective stresses are evaluated to elucidate the effect of material models and ILT formation. The results indicate that increasing the ILT burden leads to lowered effective stresses on the AAA wall. The material properties of the artery and ILT are also effective on the stresses; however, these effects are limited compared to the effect of ILT volume in the AAA sac.

The Ratio of the Size of the Abdominal Aortic Aneurysm to That of the Unchanged Aorta as a Risk Factor for Its Rupture

Background: A ruptured abdominal aortic aneurysm is a severe condition associated with high mortality. Currently, the most important criterion used to estimate the risk of its rupture is the size of the aneurysm, but due to patients’ anatomical variability, many aneurysms have a high risk of rupture with a small aneurysm size. We asked ourselves whether individual differences in anatomy could be taken into account when assessing the risk of rupture. Methods: Based on the CT scan image, aneurysm and normal aorta diameters were collected from 186 individuals and compared in patients with ruptured and unruptured aneurysms. To take into account anatomical differences between patients, diameter ratios were calculated by dividing the aneurysm diameter by the diameter of the normal aorta at various heights, and then further comparisons were made. Results: It was found that the calculated ratios differ between patients with ruptured and unruptured aneurysms. This observation is also present in patients with small aneurysms, with its maximal size below the level that indicates the need for surgical treatment. For small aneurysms, the ratios help us to estimate the risk of rupture better than the maximum sac size (AUC: 0.783 vs. 0.650). Conclusions: The calculated ratios appear to be a valuable feature to indicate which of the small aneurysms have a high risk of rupture. The obtained results suggest the need for further confirmation of their usefulness in subsequent groups of patients.

Prediction of abdominal aortic aneurysm growth by artificial intelligence taking into account clinical, biologic, morphologic, and biomechanical variables

OBJECTIVES

To develop a prediction model that could risk stratify abdominal aortic aneurysms (AAAs) into high and low growth rate groups, using machine learning algorithms based on variables from different pathophysiological fields.

METHODS

A cohort of 40 patients with small AAAs (maximum diameter 32-53 mm) who had at least an initial and a follow-up CT scan (median follow-up 12 months, range 3-36 months) were included. 29 input variables from clinical, biological, morphometric, and biomechanical pathophysiological aspects extracted for predictive modeling. Collected data were used to build two supervised machine learning models. A gradient boosting (XGboost) and a support vector machines (SVM) algorithm were trained with 60% and tested with 40% of the data to predict which AAA would achieve a growth rate higher than the median of our study cohort. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used for the evaluation of the developed algorithms.

RESULTS

XGboost achieved the highest AUC in predicting high compared to low AAA growth rate with an AUC of 81.2% (95% CI from 61.1 to 100%). SVM achieved the second highest performance with an AUC of 68.8% (95% CI from 46.5 to 91%). Based on the best performing algorithm, variable importance was estimated. Diameter-diameter ratio (maximum diameter/neck diameter), Tortuosity from Renal arteries to aortic bifurcation, and maximum thickness of the intraluminal thrombus were found to be the most important factors for model predictions. Other factors were also found to play a significant but less important role.

CONCLUSIONS

A prediction model that can risk stratify AAAs into high and low growth rate groups could be developed by analyzing several factors implicated in the multifactorial pathophysiology of this disease, with the use of machine learning algorithms. Future studies including larger patient cohorts and implementing additional risk markers may aid in the establishment of such methodology during AAA rupture risk estimation.

Artificial intelligence framework to predict wall stress in abdominal aortic aneurysm

Abdominal aortic aneurysms (AAA) have been rigorously investigated to understand when their risk of rupture - which is the 13th leading cause of death in the US - exceeds the risks associated with repair. Clinical intervention occurs when an aneurysm diameter exceeds 5.5 cm, but this "one-size fits all" criterion is insufficient, as it has been reported thatup to a quarter of AAA smaller than 5.5 cm do rupture. Therefore, there is a need for a more reliable, patient-specific, clinical tool to aide in the management of AAA. Biomechanical assessment of AAA is thought to provide critical physical insights to rupture risk, but clinical translataion of biomechanics-based tools has been limited due to the expertise, time, and computational requirements. It was estimated that through 2015, only 348 individual AAA cases have had biomechanical stress analysis performed, suggesting a deficient sample size to make such analysis relevant in the clinic. Artificial intelligence (AI) algorithms offer the potential to increase the throughput of AAA biomechanical analyses by reducing the overall time required to assess the wall stresses in these complex structures using traditional methods. This can be achieved by automatically segmenting regions of interest from medical images and using machine learning models to predict wall stresses of AAA. In this study, we present an automated AI-based methodology to predict the biomechanical wall stresses for individual AAA. The predictions using this approach were completed in a significantly less amount of time compared to a more traditional approach (~4 hours vs 20 seconds).

EVALUATION OF AORTIC TORTUOSITY AS A NEGATIVE PREDICTOR OF ABDOMINAL AORTIC ANEURYSM RUPTURE

OBJECTIVE

Maximal aortic diameter has been used as a key indication to repair an abdominal aortic aneurysm (AAA). Aortic tortuosity has been proposed as another consideration. This study compared the degree of aortic tortuosity in ruptured aneurysms with those who underwent elective repair using CT imaging.

METHODS

A retrospective review of a prospectively maintained database of patients that underwent AAA repair from Dec 2014 to Dec 2019 was undertaken. Patients with ruptured aneurysm (rAAA) were matched to a group of non-ruptured AAA (nrAAA) patients with the same maximal aneurysm diameter and age. The degree of aortic tortuosity, defined as the maximum lateral deviation (mm) from aortic centerline, was measured on preoperative CT scans on coronal views.

RESULTS

Over a 5-year period, 572 AAA cases were identified. The aortic tortuosity of 25 rAAA cases were compared with a matched control group of 31 nrAAA that were selected based on same mean maximum diameter of 8.4cm and similar age. In the rAAA group, the mean age was 74.8 years (84% males). For the nrAAA group, the mean age was 76.3 years (88%) males. The mean aortic tortuosity for the rAAA cases and nrAAA groups were 9.3±7.9 mm and 18.0±11.2 mm, respectively (p<0.01).

CONCLUSIONS

Greater aortic tortuosity was seen in nrAAA cases compared with rAAA cases at the same matched aneurysm size. This suggests that aortic tortuosity may confer a reduced rupture risk. Further studies with larger cohorts are needed to verify this observation.

Combined Curvature and Wall Shear Stress Analysis of Abdominal Aortic Aneurysm: An Analysis of Rupture Risk Factors

PURPOSE

To discuss the risk factors for abdominal aortic aneurysm rupture based on geometric and hemodynamic parameters.

METHODS

We retrospectively reviewed the clinical data of those who were diagnosed with an abdominal aortic aneurysm by computed tomography angiography at our hospital between October 2019 and December 2020. Thirty-five patients were included in the ruptured group (13 patients) and the unruptured group (22 patients). We analyzed the differences and correlations of anatomical factors and hemodynamic parameters between the two groups using computational fluid dynamics based on computed tomography angiography.

RESULTS

There were significant differences in the maximum diameter [(79.847 ± 10.067) mm vs. (52.320 ± 14.682) mm, P < 0.001], curvature [(0.139 ± 0.050) vs. 0.080 (0.123 - 0.068), P = 0.021], and wall shear stress at the site of maximal blood flow impact [0.549(0.839 - 0.492) Pa vs. (1.378 ± 0.255) Pa, P < 0.001] between the ruptured and unruptured groups, respectively. And in the ruptured group, wall shear stress at the rupture site was significantly different from that at the site of maximal blood flow impact [0.025 (0.049 - 0.018) Pa vs. 0.549 (0.839 - 0.492) Pa, P = 0.001]. Then, the maximum diameter and curvature were associated with rupture (maximum diameter: OR: 1.095, P = 0.003; curvature: OR: 1.142E + 10, P = 0.012). Most importantly, curvature is negatively correlated with wall shear stress (r = - 0.366, P = 0.033).

CONCLUSIONS

Both curvature and wall shear stress can evaluate the rupture risk of aneurysm. Also, curvature can be used as the geometric substitution of wall shear stress.

Local aortic aneurysm wall expansion measured with automated image analysis

Background

Assessment of regional aortic wall deformation (RAWD) might better predict for abdominal aortic aneurysm (AAA) rupture than the maximal aortic diameter or growth rate. Using sequential computed tomography angiograms (CTAs), we developed a streamlined, semiautomated method of computing RAWD using deformable image registration (dirRAWD).

Methods

Paired sequential CTAs performed 1 to 2 years apart of 15 patients with AAAs of various shapes and sizes were selected. Using each patient’s initial CTA, the luminal and aortic wall surfaces were segmented both manually and semiautomatically. Next, the same patient’s follow-up CTA was aligned with the first using automated rigid image registration. Deformable image registration was then used to calculate the local aneurysm wall expansion between the sequential scans (dirRAWD). To measure technique accuracy, the deformable registration results were compared with the local displacement of anatomic landmarks (fiducial markers), such as the origin of the inferior mesenteric artery and/or aortic wall calcifications. Additionally, for each patient, the maximal RAWD was manually measured for each aneurysm and was compared with the dirRAWD at the same location.

Results

The technique was successful in all patients. The mean landmark displacement error was 0.59 ± 0.93 mm with no difference between true landmark displacement and deformable registration landmark displacement by Wilcoxon rank sum test (P = .39). The absolute difference between the manually measured maximal RAWD and dirRAWD was 0.27 ± 0.23 mm, with a relative difference of 7.9% and no difference using the Wilcoxon rank sum test (P = .69). No differences were found in the maximal dirRAWD when derived using a purely manual AAA segmentation compared with using semiautomated AAA segmentation (P = .55).

Conclusions

We found accurate and automated RAWD measurements were feasible with clinically insignificant errors. Using semiautomated AAA segmentations for deformable image registration methods did not alter maximal dirRAWD accuracy compared with using manual AAA segmentations. Future work will compare dirRAWD with finite element analysis–derived regional wall stress and determine whether dirRAWD might serve as an independent predictor of rupture risk.

Current abdominal aortic aneurysm (AAA) surveillance methods are limited to assessments of the maximal diameter, which cannot accurately predict for AAA expansion and rupture risk. Automated assessment of AAA expansion across the entire three-dimensional geometry of the aneurysm could better describe aneurysm growth and could substantially inform management decisions, including the indications for repair. We have developed an accurate and streamlined approach to assessing local three-dimensional AAA expansion with submillimeter accuracy using computed tomography imaging obtained during routine aneurysm surveillance. This novel process does not require significant user expertise nor computer processing power and can be performed using open-source software readily accessible to both scientists and clinicians.

A Predictive Analysis of Wall Stress in Abdominal Aortic Aneurysms Using a Neural Network Model

Rupture risk assessment of abdominal aortic aneurysms (AAAs) by means of quantifying wall stress is a common biomechanical strategy. However, the clinical translation of this approach has been greatly limited due to the complexity associated with the computational tools required for its implementation. Thus, being able to estimate wall stress using nonbiomechanical markers that can be quantified as a direct outcome of clinical image segmentation would be advantageous in improving the potential implementation of said strategy. In the present work, we investigated the use of geometric indices to predict patient-specific AAA wall stress by means of a novel neural network (NN) modeling approach. We conducted a retrospective review of existing clinical images of two patient groups: 98 asymptomatic and 50 symptomatic AAAs. The images were subject to a protocol consisting of image segmentation, processing, volume meshing, finite element modeling, and geometry quantification, from which 53 geometric indices and the spatially averaged wall stress (SAWS) were calculated. SAWS estimated from finite element analysis was considered the gold standard for the predictions. We developed feed-forward NN models composed of an input layer, two dense layers, and an output layer using Keras, a deep learning library in python. The NN models were trained, tested, and validated independently for both AAA groups using all geometric indices, as well as a reduced set of indices resulting from a variable reduction procedure. We compared the performance of the NN models with two standard machine learning algorithms (MARS: multivariate adaptive regression splines and GAM: generalized additive model) and a linear regression model (GLM: generalized linear model). With the reduced sets of indices, the NN-based approach exhibited the highest mean goodness-of-fit (for the symptomatic group 0.71 and for the asymptomatic group 0.79) and lowest mean relative error (17% for both groups). In contrast, MARS yielded a mean goodness-of-fit of 0.59 for the symptomatic group and 0.77 for the asymptomatic group, with relative errors of 17% for the symptomatic group and 22% for the asymptomatic group. GAM had a mean goodness-of-fit of 0.70 for the symptomatic group and 0.80 for the asymptomatic group, with relative errors of 16% for the symptomatic group and 20% for the asymptomatic group. GLM did not perform as well as the other algorithms, with a mean goodness-of-fit of 0.53 for the symptomatic group and 0.70 for the asymptomatic group, with relative errors of 19% for the symptomatic group and 23% for the asymptomatic group. Nevertheless, the NN models required a reduced set of 15 and 13 geometric indices to predict SAWS for the symptomatic and asymptomatic AAA groups, respectively. This was in contrast to the reduced set of nine and eight geometric indices required to predict SAWS with the MARS and GAM algorithms for each AAA group, respectively. The use of NN modeling represents a promising alternative methodology for the estimation of AAA wall stress using geometric indices as surrogates, in lieu of finite element modeling. The performance metrics of NN models are expected to improve with significantly larger group sizes, given the suitability of NN modeling for "big data" applications.

On the importance of tunica intima in the aging aorta: a three-layered in silico model for computing wall stresses in abdominal aortic aneurysms

Layer-specific experimental data for human aortic tissue suggest that, in aged arteries and arteries with non-atherosclerotic intimal thickening, the innermost layer of the aorta increases significantly its stiffness and thickness, becoming load-bearing. However, there are very few computational studies of abdominal aortic aneurysms (AAAs) that take into account the mechanical contribution of the three layers that comprise the aneurysmal tissue. In this paper, a three-layered finite element model is proposed from the simplest uniaxial stress state to geometrically parametrized models of AAAs with different asymmetry values. Comparisons are made between a three-layered artery wall and a mono-layered intact artery, which represents the complex behavior of the aggregate adventitia-media-intima in a single layer with averaged mechanical properties. Likewise, the response of our idealized geometries is compared with similar experimental and numerical models. Finally, the mechanical contributions of adventitia, media and intima are analyzed for the three-layered aneurysms through the evaluation of the mean stress absorption percentage. Results show the relevance and necessity of considering the inclusion of tunica intima in multi-layered models of AAAs for getting accurate results in terms of peak wall stresses and displacements.

Combining Volumetric and Wall Shear Stress Analysis from CT to Assess Risk of Abdominal Aortic Aneurysm Progression

Background Despite known limitations, the decision to operate on abdominal aortic aneurysm (AAA) is primarily on the basis of measurement of maximal aneurysm diameter. Purpose To identify volumetric and computational fluid dynamics parameters to predict AAAs that are likely to progress in size. Materials and Methods This study, part of a multicenter prospective registry (NCT01599533), included 126 patients with AAA. Patients were sorted into stable (≤10-mL increase in aneurysm volume) and progression (>10-mL increase in aneurysm volume) groups. Initial AAA characteristics of the derivation cohort were analyzed (maximal diameter and surface, thrombus and lumen volumes, maximal wall pressure, and wall shear stress [WSS]) to identify relevant parameters for a logistic regression model. Model and maximal diameter diagnostic performances were assessed in both cohorts and for AAAs smaller than 50 mm by using area under the receiver operating characteristic curve (AUC). Results Eighty-one patients were included (mean age, 73 years ± 7 years [standard deviation]; 78 men). The derivation and validation cohorts included, respectively, 50 and 31 participants. In the derivation cohort, there was higher mean lumen volume and lower mean WSS in the progression group compared with the stable group (60 mL ± 14 vs 46 mL ± 18 [P = .005] and 66% ± 6 vs 53% ± 9 [P = .02], respectively). Mean lumen volume and mean WSS at baseline were correlated to total volume growth (r = 0.47 [P = .002] and -0.42 [P = .006], respectively). In the derivation cohort, a regression model including lumen volume and WSS to predict aneurysm enlargement was superior to maximal diameter alone (AUC, 0.78 vs 0.52, respectively; P = .003); although no difference was found in the validation cohort (AUC, 0.79 vs 0.71, respectively; P = .51). For AAAs smaller than 50 mm, a regression model that included both baseline WSS and lumen volume performed better than maximal diameter (AUC, 0.79 vs 0.53, respectively; P = .01). Conclusion Combined analysis of lumen volume and wall shear stress was associated with enlargement of abdominal aortic aneurysms at 1 year, particularly in aneurysms smaller than 50 mm in diameter. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Mitsouras and Leach in this issue.

A novel method for describing biomechanical properties of the aortic wall based on the three-dimensional fluid-structure interaction model

OBJECTIVES

Our goal was to present a novel non-invasive approach for assessment of aortic wall displacement to describe its biomechanical properties during the cardiac cycle.

METHODS

The fluid-structure interaction (FSI) technique was used to reconstruct aortic wall displacement based on computed tomography angiography and 2-dimensional speckle-tracking technique (2DSTT) data collected from 20 patients [10 with healthy aortas (AA) and 10 with abdominal aortic aneurysms (AAAs)]. The mechanical properties of the wall of the aorta were described by the Yeoh hyperelastic materials model with α and β parameters, and wall displacement was determined with 2DSTT. The mechanical parameters of the wall of the aorta in the FSI model were automatically updated in the calculation loop until the calculated and clinically measured wall movements were the same.

RESULTS

Results showed 98% accuracy of FSI compared to 2DSTT for AA and AAA (P > 0.05). The mean wall deformation for AA was 2.45 ± 0.12 mm and 2.49 ± 0.10 mm for FSI and 2DSTT, respectively (P = 0.40), whereas that for AAA was 2.84 ± 0.44 mm and 2.88 ± 0.45 mm, respectively (P = 0.83). The FSI analysis indicated that the α and β parameters for AA were equal to 14.35 ± 1.30 N⋅cm-2 and 9.33 ± 1.08 N⋅cm-2, respectively; and for AAA, α was 11.00 ± 0.49 N⋅cm-2 and β was 79.46 ± 4.32 N⋅cm-2.

CONCLUSIONS

The FSI technique may be successfully applied to assess the mechanical parameters of patient-specific aortic walls using computed tomography angiographic and 2DSTT measurements.

(PEAK) WALL STRESS AS AN INDICATOR OF ABDOMINAL AORTIC ANEURYSM SEVERITY

Abdominal aortic aneurysms, which consist of dilatations of the infra-renal aorta by at least 1.5 times of its normal diameter, are becoming a leading cause of death worldwide. Rupture often occurs unexpectedly, before a repair procedure is conducted. The AAA maximum diameter has been used as a clinical criterion to monitor AAA severity. However, assessment of AAA rupture risk requires knowledge of wall stress and wall strength at the potential rupture location. We conducted a study on 37 patient specific CT datasets to investigate the benefits of using peak wall stress instead of D_max for AAA rupture severity. Correlation between PWS and 24 geometric indices and biomechanical factors was studied where eleven of them showed a statistically significant correlation with PWS. A Finite Element Analysis Rupture Index was used to conclude that the use of D _max as a single predictor of AAA behavior and severity may be insufficient based on our patient population with a D_max smaller than the 5.5 cm, clinically recommended repair threshold.

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.

Fluid-Structure Interaction in Abdominal Aortic Aneurysms: Effect of Haematocrit

The Abdominal Aortic Aneurysm (AAA) is a local dilation of the abdominal aorta and it is a cause for serious concern because of the high mortality associated with its rupture. Consequently, the understanding of the phenomena related to the creation and the progression of an AAA is of crucial importance. In this work, the complicated interaction between the blood flow and the AAA wall is numerically examined using a fully coupled Fluid-Structure Interaction (FSI) method. The study investigates the possible link between the dynamic behavior of an AAA and the blood viscosity variations attributed to the haematocrit value, while it also incorporates the pulsatile blood flow, the non-Newtonian behavior of blood and the hyperelasticity of the arterial wall. It was found that blood viscosity has no significant effect on von Mises stress magnitude and distribution, whereas there is a close relation between the haematocrit value and the Wall Shear Stress (WSS) magnitude in AAAs. This WSS variation can possibly alter the mechanical properties of the arterial wall and increase its growth rate or even its rupture possibility. The relationship between haematocrit and dynamic behavior of an AAA can be helpful in designing a patient specific treatment.

Computational Fluid Dynamic Accuracy in Mimicking Changes in Blood Hemodynamics in Patients with Acute Type IIIb Aortic Dissection Treated with TEVAR

Background: We aimed to verify the accuracy of the Computational Fluid Dynamics (CFD) algorithm for blood flow reconstruction for type IIIb aortic dissection (TBAD) before and after thoracic endovascular aortic repair (TEVAR). Methods: We made 3D models of the aorta and its branches using pre- and post-operative CT data from five patients treated for TBAD. The CFD technique was used to quantify the displacement forces acting on the aortic wall in the areas of endograft, mass flow rate/velocity and wall shear stress (WSS). Calculated results were verified with ultrasonography (USG-Doppler) data. Results: CFD results indicated that the TEVAR procedure caused a 7-fold improvement in overall blood flow through the aorta (p = 0.0001), which is in line with USG-Doppler data. A comparison of CFD results and USG-Doppler data indicated no significant change in blood flow through the analysed arteries. CFD also showed a significant increase in flow rate for thoracic trunk and renal arteries, which was in accordance with USG-Doppler data (accuracy 90% and 99.9%). Moreover, we observed a significant decrease in WSS values within the whole aorta after TEVAR compared to pre-TEVAR (1.34 ± 0.20 Pa vs. 3.80 ± 0.59 Pa, respectively, p = 0.0001). This decrease was shown by a significant reduction in WSS and WSS contours in the thoracic aorta (from 3.10 ± 0.27 Pa to 1.34 ± 0.11Pa, p = 0.043) and renal arteries (from 4.40 ± 0.25 Pa to 1.50 ± 0.22 Pa p = 0.043). Conclusions: Post-operative remodelling of the aorta after TEVAR for TBAD improved hemodynamic patterns reflected by flow, velocity and WSS with an accuracy of 99%.

Geometric surrogates of abdominal aortic aneurysm wall mechanics

The maximum diameter criterion is the most important factor in the clinical management of abdominal aortic aneurysms (AAA). Consequently, interventional repair is recommended when an aneurysm reaches a critical diameter, typically 5.0 cm in the United States. Nevertheless, biomechanical measures of the aneurysmal abdominal aorta have long been implicated in AAA risk of rupture. The purpose of this study is to assess whether other geometric characteristics, in addition to maximum diameter, may be highly correlated with the AAA peak wall stress (PWS). Using in-house segmentation and meshing algorithms, 30 patient-specific AAA models were generated for finite element analysis using an isotropic constitutive material for the AAA wall. PWS, evaluated as the spatial maximum of the first principal stress, was calculated at a systolic pressure of 120 mmHg. The models were also used to calculate 47 geometric indices characteristic of the aneurysm geometry. Statistical analyses were conducted using a feature reduction algorithm in which the 47 indices were reduced to 11 based on their statistical significance in differentiating the models in the population (p < 0.05). A subsequent discriminant analysis was performed and 7 of these indices were identified as having no error in discriminating the AAA models with a significant nonlinear regression correlation with PWS. These indices were: D_max (maximum diameter), T (tortuosity), DDr (maximum diameter to neck diameter ratio), S (wall surface area), K_median (median of the Gaussian surface curvature), C_max (maximum lumen compactness), and M_mode (mode of the Mean surface curvature). Therefore, these characteristics of an individual AAA geometry are the highest correlated with the most clinically relevant biomechanical parameter for rupture risk assessment. We conclude that the indices can serve as surrogates of PWS in lieu of a finite element modeling approach for AAA biomechanical evaluation.

Fluid-structure interaction modeling of aneurysmal arteries under steady-state and pulsatile blood flow: a stability analysis

Tortuous aneurysmal arteries are often associated with a higher risk of rupture but the mechanism remains unclear. The goal of this study was to analyze the buckling and post-buckling behaviors of aneurysmal arteries under pulsatile flow. To accomplish this goal, we analyzed the buckling behavior of model carotid and abdominal aorta with aneurysms by utilizing fluid-structure interaction (FSI) method with realistic waveforms boundary conditions. FSI simulations were done under steady-state and pulsatile flow for normal (1.5) and reduced (1.3) axial stretch ratios to investigate the influence of aneurysm, pulsatile lumen pressure and axial tension on stability. Our results indicated that aneurysmal artery buckled at the critical buckling pressure and its deflection nonlinearly increased with increasing lumen pressure. Buckling elevates the peak stress (up to 118%). The maximum aneurysm wall stress at pulsatile FSI flow was (29%) higher than under static pressure at the peak lumen pressure of 130 mmHg. Buckling results show an increase in lumen shear stress at the inner side of the maximum deflection. Vortex flow was dramatically enlarged with increasing lumen pressure and artery diameter. Aneurysmal arteries are more susceptible than normal arteries to mechanical instability which causes high stresses in the aneurysm wall that could lead to aneurysm rupture.

Mechanical and geometrical determinants of wall stress in abdominal aortic aneurysms: A computational study

An aortic aneurysm (AA) is a focal dilatation of the aortic wall. Occurrence of AA rupture is an all too common event that is associated with high levels of patient morbidity and mortality. The decision to surgically intervene prior to AA rupture is made with recognition of significant procedural risks, and is primarily based on the maximal diameter and/or growth rate of the AA. Despite established thresholds for intervention, rupture occurs in a notable subset of patients exhibiting sub-critical maximal diameters and/or growth rates. Therefore, a pressing need remains to identify better predictors of rupture risk and ultimately integrate their measurement into clinical decision making. In this study, we use a series of finite element-based computational models that represent a range of plausible AA scenarios, and evaluate the relative sensitivity of wall stress to geometrical and mechanical properties of the aneurysmal tissue. Taken together, our findings encourage an expansion of geometrical parameters considered for rupture risk assessment, and provide perspective on the degree to which tissue mechanical properties may modulate peak stress values within aneurysmal tissue.

Toward modeling the effects of regional material properties on the wall stress distribution of abdominal aortic aneurysms

The overall geometry and different biomechanical parameters of an abdominal aortic aneurysm (AAA), contribute to its severity and risk of rupture, therefore they could be used to track its progression. Previous and ongoing research efforts have resorted to using uniform material properties to model the behavior of AAA. However, it has been recently illustrated that different regions of the AAA wall exhibit different behavior due to the effect of the biological activities in the metalloproteinase matrix that makes up the wall at the aneurysm site. In this work, we introduce a non-invasive patient-specific regional material property model to help us better understand and investigate the AAA wall stress distribution, peak wall stress (PWS) severity, and potential rupture risk. Our results indicate that the PWS and the overall wall stress distribution predicted using the proposed regional material property model, are higher than those predicted using the traditional homogeneous, hyper-elastic model (p <1.43E-07). Our results also show that to investigate AAA, the overall geometry, presence of intra-luminal thrombus (ILT), and loading condition in a patient specific manner may be critical for capturing the biomechanical complexity of AAAs.

A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm

Geometric features of the aorta are linked to patient risk of rupture in the clinical decision to electively repair an ascending aortic aneurysm (AsAA). Previous approaches have focused on relationship between intuitive geometric features (e.g., diameter and curvature) and wall stress. This work investigates the feasibility of a machine learning approach to establish the linkages between shape features and FEA-predicted AsAA rupture risk, and it may serve as a faster surrogate for FEA associated with long simulation time and numerical convergence issues. This method consists of four main steps: (1) constructing a statistical shape model (SSM) from clinical 3D CT images of AsAA patients; (2) generating a dataset of representative aneurysm shapes and obtaining FEA-predicted risk scores defined as systolic pressure divided by rupture pressure (rupture is determined by a threshold criterion); (3) establishing relationship between shape features and risk by using classifiers and regressors; and (4) evaluating such relationship in cross-validation. The results show that SSM parameters can be used as strong shape features to make predictions of risk scores consistent with FEA, which lead to an average risk classification accuracy of 95.58% by using support vector machine and an average regression error of 0.0332 by using support vector regression, while intuitive geometric features have relatively weak performance. Compared to FEA, this machine learning approach is magnitudes faster. In our future studies, material properties and inhomogeneous thickness will be incorporated into the models and learning algorithms, which may lead to a practical system for clinical applications.

Effects of anatomical characteristics as factors in abdominal aortic aneurysm rupture: CT aortography analysis

The aim of this study was to analyze the anatomical characteristics of patients with ruptured abdominal aortic aneurysms (AAAs) using computed tomography (CT) aortography in order to determine the risk factors for rupture.We retrospectively reviewed the CT aortography findings and medical records of patients with ruptured AAAs who underwent CT aortography between February 2002 and December 2014. Age, sex, blood pressure at the time of rupture, treatment methods used for the ruptured AAAs, and treatment outcomes were analyzed. Statistical analyses were performed to determine the association between the maximum aneurysm diameter, which is considered the standard predictor of aneurysm rupture, and anatomical characteristics such as proximal neck diameter, angle between the suprarenal aorta and the aneurysm neck (α angle), angle between the aneurysm neck and aneurysm sac (β angle), and angles between the abdominal aorta and both iliac arteries.Data were reviewed for a total of 36 patients. The mean maximum diameter of AAAs was 76.84 ± 21.08 mm. Multivariate analysis adjusted for age and sex indicated statistical correlations between the α and β angles and maximum aneurysm diameter and between the β angle and iliac artery involvement.Our results suggest that the tortuosity of the aorta tends to be associated with the diameter of AAAs and iliac artery involvement. Investigation of the anatomical characteristics of individual patients using CT aortography is expected to aid in predicting the risk of AAA rupture.

Hemodynamic Profile of Two Aortic Endografts Accounting for Their Postimplantation Position

Endovascular aneurysm repair (EVAR) is a clinically effective technique for treating anatomically eligible abdominal aortic aneurysms (AAAs), involving the deployment of an endograft (EG) that is designed to prevent blood leakage in the aneurysmal sac. While most EGs have equivalent operating principles, the hemodynamic environment established by different EGs is not necessarily the same. So, to unveil the post-EVAR hemodynamic properties, we need an EG-specific computational approach that currently lacks from the literature. Endurant and Excluder are two EGs with similar pre-installation designs. We assumed that the flow conditions in the particular EGs do not vary significantly. The hypothesis was tested combining image reconstructions, computational fluid dynamics (CFD), and statistics, taking into account the postimplantation position of the EGs. Ten patients with Endurant EGs and ten patients with Excluder EGs were included in this study. The two groups were matched with respect to the preoperative morphological characteristics of the AAAs. The EG models are derived from image reconstructions of postoperative computed tomography scans. Wall shear stress (WSS), displacement force, velocity, and helicity were calculated in regions of interest within the EG structures, i.e., the main body, the upper and lower part of the limbs. Excluder generated higher WSS compared to Endurant, especially on the lower part of the limbs (p = 0.001). Spatial fluctuations of WSS were observed on the upper part of the Excluder limbs. Higher blood velocity was induced by Excluder in all the regions of interest (p = 0.04, p = 0.01, and p = 0.004). Focal points of secondary flow were detected in the main body of Endurant and the limbs of Excluder. The displacement force acting on the lower part of the Excluder limbs was stronger compared to the Endurant one (p = 0.03). The results showed that two similar EGs implanted in similar AAAs can induce significantly different flow properties. The delineation of the hemodynamic features associated with the various commercially available EGs could further promote the personalization of treatment offered to aneurysmal patients and inspire ideas for the improvement of EG designs in the future.

Geometric Predictors of Abdominal Aortic Aneurysm Maximum Wall Stress

Abdominal aortic aneurysm (AAA) is a dilation of the abdominal aorta (above 50 % of its original diameter), which can cause death upon rupturing. It usually grows asymptomatically leading to late clinical intervention. The medical criteria to indicate surgery are based on measuring the diameter and growth rate, but in many cases aneurysms fail at uncharacterized critical values. In search of a more efficient technique in predicting AAA failure, there is consensus on the importance of studying its geometric characteristics and estimation of the wall stress, but no fully successful correlation has been found between the two yet. This work examines the relationship between a parameterized geometry (18 input variables and 10 dependent indices) and 1 output variable: the maximum wall stress. Design of Experiments (DOE) techniques are used to generate 183 geometric configurations, for which Finite Element Analyses are performed using ANSYS™ state-of-the-art solver with a hyperelastic, isotropic and homogeneous arterial model for the wall, considering systolic internal pressure (steady state) and the restriction of longitudinal movement at the blood vessel end-sections. Due to the large number of independent parameters to consider, a preliminary Parameters Correlation analysis was performed to determine if a correlation between all input parameters and the maximum stress existed. The correlations between input parameters and the output variable were determined using the Spearman Rank correlation. Correlations with the maximum wall stress for: maximum diameter (ρ = 0.46), wall thickness (ρ = 0.35), d_c parameter (ρ = 0.21) and tortuosity (ρ = 0.55) were found. The response surface function between geometry and maximum wall stress was estimated by three models: Universal Kriging geostatistical regression (18 parameters), multiple linear regression (4 parameters) and multiple exponential regression (4 parameters). The models accounted for the stress variance by 99 %, 61 % and 66 %, respectively, with average percentage errors of 0.12 %, 16 % and 17 %, respectively. The solution spaces obtained from this study might provide physicians with a better estimation of the AAA rupture potential and thus, facilitate safer and anticipated treatments of the condition.

The Association Between Geometry and Wall Stress in Emergently Repaired Abdominal Aortic Aneurysms

Abdominal aortic aneurysm (AAA) is a prevalent cardiovascular disease characterized by the focal dilation of the aorta, which supplies blood to all the organs and tissues in the systemic circulation. With the AAA increasing in diameter over time, the risk of aneurysm rupture is generally associated with the size of the aneurysm. If diagnosed on time, intervention is recommended to prevent AAA rupture. The criterion to decide on surgical intervention is determined by measuring the maximum diameter of the aneurysm relative to the critical value of 5.5 cm. However, a more reliable approach could be based on understanding the biomechanical behavior of the aneurysmal wall. In addition, geometric features that are proven to be significant predictors of the AAA wall mechanics could be used as surrogates of the AAA biomechanical behavior and, subsequently, of the aneurysm's risk of rupture. The aim of this work is to identify those geometric indices that have a high correlation with AAA wall stress in the population of patients who received an emergent repair of their aneurysm. In-house segmentation and meshing algorithms were used to model 75 AAAs followed by estimation of the spatially distributed wall stress by performing finite element analysis. Fifty-two shape and size geometric indices were calculated for the same models using MATLAB scripting. Hypotheses testing were carried out to identify the indices significantly correlated with wall stress by constructing a Pearson's correlation coefficient matrix. The analyses revealed that 12 indices displayed high correlation with the wall stress, amongst which wall thickness and curvature-based indices exhibited the highest correlations. Stepwise regression analysis of these correlated indices indicated that wall stress can be predicted by the following four indices with an accuracy of 76%: maximum aneurysm diameter, aneurysm sac length, average wall thickness at the maximum diameter cross-section, and the median of the wall thickness variance. The primary outcome of this work emphasizes the use of global measures of size and wall thickness as geometric surrogates of wall stress for emergently repaired AAAs.

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.

Continuous measurement of aortic dimensions in Turner syndrome: a cardiovascular magnetic resonance study

BACKGROUND

Severity of thoracic aortic disease in Turner syndrome (TS) patients is currently described through measures of aorta size and geometry at discrete locations. The objective of this study is to develop an improved measurement tool that quantifies changes in size and geometry over time, continuously along the length of the thoracic aorta.

METHODS

Cardiovascular magnetic resonance (CMR) scans for 15 TS patients [41 ± 9 years (mean age ± standard deviation (SD))] were acquired over a 10-year period and compared with ten healthy gender and age-matched controls. Three-dimensional aortic geometries were reconstructed, smoothed and clipped, which was followed by identification of centerlines and planes normal to the centerlines. Geometric variables, including maximum diameter and cross-sectional area, were evaluated continuously along the thoracic aorta. Distance maps were computed for TS and compared to the corresponding maps for controls, to highlight any asymmetry and dimensional differences between diseased and normal aortae. Furthermore, a registration scheme was proposed to estimate localized changes in aorta geometry between visits. The estimated maximum diameter from the continuous method was then compared with corresponding manual measurements at 7 discrete locations for each visit and for changes between visits.

RESULTS

Manual measures at the seven positions and the corresponding continuous measurements of maximum diameter for all visits considered, correlated highly (R-value = 0.77, P < 0.01). There was good agreement between manual and continuous measurement methods for visit-to-visit changes in maximum diameter. The continuous method was less sensitive to inter-user variability [0.2 ± 2.3 mm (mean difference in diameters ± SD)] and choice of smoothing software [0.3 ± 1.3 mm]. Aortic diameters were larger in TS than controls in the ascending [TS: 13.4 ± 2.1 mm (mean distance ± SD), Controls: 12.6 ± 1 mm] and descending [TS: 10.2 ± 1.3 mm (mean distance ± SD), Controls: 9.5 ± 0.9 mm] thoracic aorta as observed from the distance maps.

CONCLUSIONS

An automated methodology is presented that enables rapid and precise three-dimensional measurement of thoracic aortic geometry, which can serve as an improved tool to define disease severity and monitor disease progression.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier - NCT01678274 . Registered - 08.30.2012.

The - Not So - Solid 5.5 cm Threshold for Abdominal Aortic Aneurysm Repair: Facts, Misinterpretations, and Future Directions

Abdominal aortic aneurysms (AAAs) represent a focal dilation of the aorta exceeding 1.5 times its normal diameter. It is reported that 4-8% of men and 0.5-1% of women above 50 years of age bear an AAA. Rupture represents the most disastrous complication of aneurysmal disease that is accompanied by an overall mortality of 80%. Autopsy data have shown that nearly 13% of AAAs with a maximum diameter ≤5 cm were ruptured and 60% of the AAAs >5 cm in diameter never ruptured. It is therefore obvious that the "maximum diameter criterion," as a single parameter that fits all patients, is obsolete. Investigators have begun a search for more reliable rupture risk markers for AAA expansion, such as the level and change of peak wall stress or AAA geometry. Furthermore, it is becoming more and more evident that intraluminal thrombus (ILT), which is present in 75% of all AAAs, affects AAA features and promotes their expansion. Though these hemodynamic properties of AAAs are significant and seem to better describe rupture risk, they are in need of specialized equipment and software and demand time for processing making them difficult in use and unattractive to clinicians in everyday practice. In the search for the addition of other risk factors or user-friendly tools, which may predict AAA expansion and rupture, the use of the asymmetrical ILT deposition index seems appealing since it has been reported to identify AAAs that may have an increased or decreased growth rate.

Detection of aortic wall instability with the new dissectometer: Correlation with histological findings

OBJECTIVES

Although risk stratification for aortic dissection or rupture based on aortic diameter is quite suboptimal, alternative methods for the assessment of the aortic wall stability are rare. We assessed the mechanical properties of the aortic wall by a new custom-made device mimicking transversal aortic wall shear stress during open heart surgery in comparison with histological examination.

MATERIAL AND METHODS

One-hundred and five aortic walls were tested by the 'dissectometer' (seven different measured and two calculated values) as well as histological examination was performed.

RESULTS

Histological examination classified the aortic wall as normal in 54 (51.4%) patients and pathologic in 51 (48.6%) patients. Six out of nine parameters assessed by the dissectometer showed a significant correlation to histological findings. Using ROC-analysis, the most reliable parameter (P9) showed a sensitivity of 93.3% and a specificity of 80.4% with an area under the curve of 0.89 when using a cut-off value of 3.4. In the logistic regression analysis, P9 was an independent predictor for aortic wall instability (OR 28.983, 95% CI 11.507-72.993, p < 0.0001).

CONCLUSION

The dissectometer is suitable for discriminating between stable and unstable aortic walls with a good correlation to histological examination holding promise for direct and quick intraoperative identification of aortic walls at risk for dissection.

Investigation of material modeling in fluid-structure interaction analysis of an idealized three-layered abdominal aorta: aneurysm initiation and fully developed aneurysms

Different material models for an idealized three-layered abdominal aorta are compared using computational techniques to study aneurysm initiation and fully developed aneurysms. The computational model includes fluid-structure interaction (FSI) between the blood vessel and the blood. In order to model aneurysm initiation, the medial region was degenerated to mimic the medial loss occurring in the inception of an aneurysm. Various cases are considered in order to understand their effects on the initiation of an abdominal aortic aneurysm. The layers of the blood vessel were modeled using either linear elastic materials or Mooney-Rivlin (otherwise known as hyperelastic) type materials. The degenerated medial region was also modeled in either linear elastic or hyperelastic-type materials and assumed to be in the shape of an arc with a thin width or a circular ring with different widths. The blood viscosity effect was also considered in the initiation mechanism. In addition, dynamic analysis of the blood vessel was performed without interaction with the blood flow by applying time-dependent pressure inside the lumen in a three-layered abdominal aorta. The stresses, strains, and displacements were compared for a healthy aorta, an initiated aneurysm and a fully developed aneurysm. The study shows that the material modeling of the vessel has a sizable effect on aneurysm initiation and fully developed aneurysms. Different material modeling of degeneration regions also affects the stress-strain response of aneurysm initiation. Additionally, the structural analysis without considering FSI (called noFSI) overestimates the peak von Mises stress by 52% at the interfaces of the layers.

Mechanical instability of normal and aneurysmal arteries

Tortuous arteries associated with aneurysms have been observed in aged patients with atherosclerosis and hypertension. However, the underlying mechanism is poorly understood. The objective of this study was to determine the effect of aneurysms on arterial buckling instability and the effect of buckling on aneurysm wall stress. We investigated the mechanical buckling and post-buckling behavior of normal and aneurysmal carotid arteries and aorta's using computational simulations and experimental measurements to elucidate the interrelationship between artery buckling and aneurysms. Buckling tests were done in porcine carotid arteries with small aneurysms created using elastase treatment. Parametric studies were done for model aneurysms with orthotropic nonlinear elastic walls using finite element simulations. Our results demonstrated that arteries buckled at a critical buckling pressure and the post-buckling deflection increased nonlinearly with increasing pressure. The presence of an aneurysm can reduce the critical buckling pressure of arteries, although the effect depends on the aneurysm's dimensions. Buckled aneurysms demonstrated a higher peak wall stress compared to unbuckled aneurysms under the same lumen pressure. We conclude that aneurysmal arteries are vulnerable to mechanical buckling and mechanical buckling could lead to high stresses in the aneurysm wall. Buckling could be a possible mechanism for the development of tortuous aneurysmal arteries such as in the Loeys-Dietz syndrome.

Advancements in identifying biomechanical determinants for abdominal aortic aneurysm rupture

Abdominal aortic aneurysms are a common health problem and currently the need for surgical intervention is determined based on maximum diameter and growth rate criteria. Since these universal variables often fail to predict accurately every abdominal aortic aneurysms evolution, there is a considerable effort in the literature for other markers to be identified towards individualized rupture risk estimations and growth rate predictions. To this effort, biomechanical tools have been extensively used since abdominal aortic aneurysm rupture is in fact a material failure of the diseased arterial wall to compensate the stress acting on it. The peak wall stress, the role of the unique geometry of every individual abdominal aortic aneurysm as well as the mechanical properties and the local strength of the degenerated aneurysmal wall, all confer to rupture risk. In this review article, the assessment of these variables through mechanical testing, advanced imaging and computational modeling is reviewed and the clinical perspective is discussed.

Biochemomechanics of intraluminal thrombus in abdominal aortic aneurysms

Most computational models of abdominal aortic aneurysms address either the hemodynamics within the lesion or the mechanics of the wall. More recently, however, some models have appropriately begun to account for the evolving mechanics of the wall in response to the changing hemodynamic loads. Collectively, this large body of work has provided tremendous insight into this life-threatening condition and has provided important guidance for current research. Nevertheless, there has yet to be a comprehensive model that addresses the mechanobiology, biochemistry, and biomechanics of thrombus-laden abdominal aortic aneurysms. That is, there is a pressing need to include effects of the hemodynamics on both the development of the nearly ubiquitous intraluminal thrombus and the evolving mechanics of the wall, which depends in part on biochemical effects of the adjacent thrombus. Indeed, there is increasing evidence that intraluminal thrombus in abdominal aortic aneurysms is biologically active and should not be treated as homogeneous inert material. In this review paper, we bring together diverse findings from the literature to encourage next generation models that account for the biochemomechanics of growth and remodeling in patient-specific, thrombus-laden abdominal aortic aneurysms.

The role of geometric and biomechanical factors in abdominal aortic aneurysm rupture risk assessment

The current clinical management of abdominal aortic aneurysm (AAA) disease is based to a great extent on measuring the aneurysm maximum diameter to decide when timely intervention is required. Decades of clinical evidence show that aneurysm diameter is positively associated with the risk of rupture, but other parameters may also play a role in causing or predisposing the AAA to rupture. Geometric factors such as vessel tortuosity, intraluminal thrombus volume, and wall surface area are implicated in the differentiation of ruptured and unruptured AAAs. Biomechanical factors identified by means of computational modeling techniques, such as peak wall stress, have been positively correlated with rupture risk with a higher accuracy and sensitivity than maximum diameter alone. The objective of this review is to examine these factors, which are found to influence AAA disease progression, clinical management and rupture potential, as well as to highlight on-going research by our group in aneurysm modeling and rupture risk assessment.

Robust infrarenal aortic aneurysm lumen centerline detection for rupture status classification

The objective of this work is to develop a robust method for human abdominal aortic aneurysm (AAA) centerline detection that can contribute to the accurate computation of features for the prediction of AAA rupture risk. A semiautomatic algorithm is proposed for detecting the lumen centerline in contrast-enhanced abdominal computed tomography images based on online adaboost classifiers, which does not require prior image segmentation. The algorithm was developed and applied to thirty ruptured and thirty unruptured AAA image data sets and the tortuosities of the detected centerline were measured to assess the correlation between AAA tortuosity and the binary ruptured and unruptured labels. The lumen of each data set was segmented manually by a trained radiologist and the resulting centerlines of each data set were defined as the gold standard to evaluate the accuracy of the algorithm and to compare it against two widely used segmentation techniques. The average mean relative accuracy of the offline adaboost classifier is 91.9% with a standard deviation of 1.6%; for the online adaboost classifier it is 93.6% with a standard deviation of 1.9% (p<0.05). The online adaboost classifier outperforms the offline adaboost classifier while their computational costs are similar. Aneurysm tortuosity computed from an accurately derived lumen centerline using online adaboost is statistically higher for ruptured aneurysms compared to unruptured aneurysms, indicating that tortuosity can be used to assess rupture risk in the vascular clinic.

A literature review of the numerical analysis of abdominal aortic aneurysms treated with endovascular stent grafts

The purpose of this paper is to present the basic principles and relevant advances in the computational modeling of abdominal aortic aneurysms and endovascular aneurysm repair, providing the community with up-to-date state of the art in terms of numerical analysis and biomechanics. Frameworks describing the mechanical behavior of the aortic wall already exist. However, intraluminal thrombus nonhomogeneous structure and porosity still need to be well characterized. Also, although the morphology and mechanical properties of calcifications have been investigated, their effects on wall stresses remain controversial. Computational fluid dynamics usually assumes a rigid artery wall, whereas fluid-structure interaction accounts for artery compliance but is still challenging since arteries and blood have similar densities. We discuss alternatives to fluid-structure interaction based on dynamic medical images that address patient-specific hemodynamics and geometries. We describe initial stresses, elastic boundary conditions, and statistical strength for rupture risk assessment. Special emphasis is accorded to workflow development, from the conversion of medical images into finite element models, to the simulation of catheter-aorta interactions and stent-graft deployment. Our purpose is also to elaborate the key ingredients leading to virtual stenting and endovascular repair planning that could improve the procedure and stent-grafts.

Geometrical factors as predictors of increased growth rate or increased rupture risk in small aortic aneurysms

Abdominal Aortic Aneurysms (AAAs) are focal dilation of the aorta that can lead to excessive enlargement and rupture over time. Current practice suggests intervention when the maximum diameter exceeds 5.5 cm, since in this diameter range the annual rupture risk outweighs the operative mortality. However, small AAA (<5.5 cm), though infrequently, may rupture or produce symptoms. Evidence from large randomized studies of small AAAs support the heterogeneity in patterns of growth and rupture potential among small AAAs. Elevated wall stress values have been implicated in AAAs rupture and rapid enlargement. Additionally, many studies have identified a strong correlation between certain geometric factors and elevated stress values. In this article we discuss the possibility that geometrical factors may have a predictive value to identify those small AAAs that have an increased risk of rupture or growth rate either during initial examination or during follow-up, making them amenable for early repair.