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.

Morphological Analysis of the Right Ventricular Endocardial Wall in Pulmonary Hypertension.

Pulmonary hypertension (PH) is a chronic progressive disease diagnosed when the pressure in the main pulmonary artery, assessed by right heart catheterization (RHC), is greater than 25 mmHg. Changes in the pulmonary vasculature due to the high pressure yield an increase in the right ventricle (RV) afterload. This starts a remodeling process during which the ventricle exhibits changes in shape and eventually fails. RV models were obtained from the segmentation of cardiac magnetic resonance images at baseline and 1-year follow-up for a pilot study that involved 12 PH and 7 control subjects. The models were used to create surface meshes of the geometry and to compute the principal, mean, and Gaussian curvatures. Ten global curvature indices were calculated for each of the RV endocardial wall reconstructions at the end-diastolic volume (EDV) and end-systolic volume (ESV) phases of the cardiac cycle. Statistical analysis of the data was performed to discern if there are significant differences in the curvature indices between controls and the PH group, as well as between the baseline and follow-up phases for the PH subjects. Six curvature indices, namely, the Gaussian curvature at ESV, the mean curvature at EDV and ESV, the L2-norm of the mean curvature at ESV, and the L2-norm of the major principal curvature at EDV and ESV, were found to be significantly different between controls and PH subjects (p < 0.05). We infer that these geometry measures could be used as indicators of RV endocardial wall morphology changes. Two global parameters, the Gaussian and mean curvatures at ESV, showed significant changes at the one-year follow-up for the PH subjects (p < 0.05). The aforementioned geometry measures to assess changes in RV shape could be used as part of a noninvasive computational tool to aid clinicians in PH diagnostic and progression assessment, and to evaluate the effectiveness of treatment.

A Hemodynamic Comparison of Myocardial Bridging and Coronary Atherosclerotic Stenosis: A Computational Model With Experimental Evaluation.

Myocardial bridging (MB) and coronary atherosclerotic stenosis can impair coronary blood flow and may cause myocardial ischemia or even heart attack. It remains unclear how MB and stenosis are similar or different regarding their impacts on coronary hemodynamics. The purpose of this study was to compare the hemodynamic effects of coronary stenosis and MB using experimental and computational fluid dynamics (CFD) approaches. For CFD modeling, three MB patients with different levels of lumen obstruction, mild, moderate, and severe were selected. Patient-specific left anterior descending (LAD) coronary artery models were reconstructed from biplane angiograms. For each MB patient, the virtually healthy and stenotic models were also simulated for comparison. In addition, an in vitro flow-loop was developed, and the pressure drop was measured for comparison. The CFD simulations results demonstrated that the difference between MB and stenosis increased with increasing MB/stenosis severity and flowrate. Experimental results showed that increasing the MB length (by 140%) only had significant impact on the pressure drop in the severe MB (39% increase at the exercise), but increasing the stenosis length dramatically increased the pressure drop in both moderate and severe stenoses at all flow rates (31% and 93% increase at the exercise, respectively). Both CFD and experimental results confirmed that the MB had a higher maximum and a lower mean pressure drop in comparison with the stenosis, regardless of the degree of lumen obstruction. A better understanding of MB and atherosclerotic stenosis may improve the therapeutic strategies in coronary disease patients and prevent acute coronary syndromes.

Design, Development, and Temporal Evaluation of a Magnetic Resonance Imaging-Compatible In Vitro Circulation Model Using a Compliant Abdominal Aortic Aneurysm Phantom.

Biomechanical characterization of abdominal aortic aneurysms (AAAs) has become commonplace in rupture risk assessment studies. However, its translation to the clinic has been greatly limited due to the complexity associated with its tools and their implementation. The unattainability of patient-specific tissue properties leads to the use of generalized population-averaged material models in finite element analyses, which adds a degree of uncertainty to the wall mechanics quantification. In addition, computational fluid dynamics modeling of AAA typically lacks the patient-specific inflow and outflow boundary conditions that should be obtained by nonstandard of care clinical imaging. An alternative approach for analyzing AAA flow and sac volume changes is to conduct in vitro experiments in a controlled laboratory environment. In this study, we designed, built, and characterized quantitatively a benchtop flow loop using a deformable AAA silicone phantom representative of a patient-specific geometry. The impedance modules, which are essential components of the flow loop, were fine-tuned to ensure typical intraluminal pressure conditions within the AAA sac. The phantom was imaged with a magnetic resonance imaging (MRI) scanner to acquire time-resolved images of the moving wall and the velocity field inside the sac. Temporal AAA sac volume changes lead to a corresponding variation in compliance throughout the cardiac cycle. The primary outcome of this work was the design optimization of the impedance elements, the quantitative characterization of the resistive and capacitive attributes of a compliant AAA phantom, and the exemplary use of MRI for flow visualization and quantification of the deformed AAA geometry.

A Comparative Study of Biomechanical and Geometrical Attributes of Abdominal Aortic Aneurysms in the Asian and Caucasian Populations.

In this work, we provide a quantitative assessment of the biomechanical and geometric features that characterize abdominal aortic aneurysm (AAA) models generated from 19 Asian and 19 Caucasian diameter-matched AAA patients. 3D patient-specific finite element models were generated and used to compute peak wall stress (PWS), 99th percentile wall stress (99th WS), and spatially averaged wall stress (AWS) for each AAA. In addition, 51 global geometric indices were calculated, which quantify the wall thickness, shape, and curvature of each AAA. The indices were correlated with 99th WS (the only biomechanical metric that exhibited significant association with geometric indices) using Spearman's correlation and subsequently with multivariate linear regression using backward elimination. For the Asian AAA group, 99th WS was highly correlated (R2 = 0.77) with three geometric indices, namely tortuosity, intraluminal thrombus volume, and area-averaged Gaussian curvature. Similarly, 99th WS in the Caucasian AAA group was highly correlated (R2 = 0.87) with six geometric indices, namely maximum AAA diameter, distal neck diameter, diameter-height ratio, minimum wall thickness variance, mode of the wall thickness variance, and area-averaged Gaussian curvature. Significant differences were found between the two groups for ten geometric indices; however, no differences were found for any of their respective biomechanical attributes. Assuming maximum AAA diameter as the most predictive metric for wall stress was found to be imprecise: 24% and 28% accuracy for the Asian and Caucasian groups, respectively. This investigation reveals that geometric indices other than maximum AAA diameter can serve as predictors of wall stress, and potentially for assessment of aneurysm rupture risk, in the Asian and Caucasian AAA populations.

Computational Modeling of Human Left Ventricle to Assess the Effects of Trabeculae Carneae on the Diastolic and Systolic Functions.

Trabeculae carneae are irregular structures that cover the endocardial surfaces of both ventricles and account for a significant portion of human ventricular mass. The role of trabeculae carneae in diastolic and systolic functions of the left ventricle (LV) is not well understood. Thus, the objective of this study was to investigate the functional role of trabeculae carneae in the LV. Finite element (FE) analyses of ventricular functions were conducted for three different models of human LV derived from high-resolution magnetic resonance imaging (MRI). The first model comprised trabeculae carneae and papillary muscles, while the second model had papillary muscles and partial trabeculae carneae, and the third model had a smooth endocardial surface. We customized these patient-specific models with myofiber architecture generated with a rule-based algorithm, diastolic material parameters of Fung strain energy function derived from biaxial tests and adjusted with the empirical Klotz relationship, and myocardial contractility constants optimized for average normal ejection fraction (EF) of the human LV. Results showed that the partial trabeculae cutting model had enlarged end-diastolic volume (EDV), reduced wall stiffness, and even increased end-systolic function, indicating that the absence of trabeculae carneae increased the compliance of the LV during diastole, while maintaining systolic function.

On the Use of Geometric Modeling to Predict Aortic Aneurysm Rupture.

BACKGROUND: Currently, the risk of abdominal aortic aneurysm (AAA) rupture is determined using the maximum diameter (Dmax) of the aorta. We sought in this study to identify a set of computed tomography (CT)-based geometric parameters that would better predict the risk of rupture than Dmax. METHODS: We obtained CT scans from 180 patients (90 ruptured AAA and 90 elective AAA repair) and then used automated software to calculate 1- , 2- , and 3-dimensional geometric parameters for each AAA. Linear regression was used to identify univariate correlates of membership in the rupture group. We then used stepwise backward elimination to generate a logistic regression model for prediction of rupture. RESULTS: Linear regression identified 40 correlates of rupture. Following stepwise backward elimination, we developed a multivariate logistic regression model containing 15 geometric parameters, including Dmax. This model was compared with a model containing Dmax alone. The multivariate model correctly classified 98% of all cases, whereas the Dmax-only model correctly classified 72% of cases. Receiver operating characteristic analysis showed that the multivariate model had an area under the curve of 0.995, as compared with 0.770 for the Dmax-only model. This difference was highly significant (P < 0.0001). CONCLUSIONS: This study demonstrates that a multivariable model using geometric factors entirely measurable from CT scanning can be a better predictor of AAA rupture than maximum diameter alone.

The Relationship Between Surface Curvature and Abdominal Aortic Aneurysm Wall Stress.

The maximum diameter (MD) criterion is the most important factor when predicting risk of rupture of abdominal aortic aneurysms (AAAs). An elevated wall stress has also been linked to a high risk of aneurysm rupture, yet is an uncommon clinical practice to compute AAA wall stress. The purpose of this study is to assess whether other characteristics of the AAA geometry are statistically correlated with wall stress. Using in-house segmentation and meshing algorithms, 30 patient-specific AAA models were generated for finite element analysis (FEA). These models were subsequently used to estimate wall stress and maximum diameter and to evaluate the spatial distributions of wall thickness, cross-sectional diameter, mean curvature, and Gaussian curvature. Data analysis consisted of statistical correlations of the aforementioned geometry metrics with wall stress for the 30 AAA inner and outer wall surfaces. In addition, a linear regression analysis was performed with all the AAA wall surfaces to quantify the relationship of the geometric indices with wall stress. These analyses indicated that while all the geometry metrics have statistically significant correlations with wall stress, the local mean curvature (LMC) exhibits the highest average Pearson's correlation coefficient for both inner and outer wall surfaces. The linear regression analysis revealed coefficients of determination for the outer and inner wall surfaces of 0.712 and 0.516, respectively, with LMC having the largest effect on the linear regression equation with wall stress. This work underscores the importance of evaluating AAA mean wall curvature as a potential surrogate for wall stress.

A Methodology for Verifying Abdominal Aortic Aneurysm Wall Stress.

An abdominal aortic aneurysm (AAA) is a permanent focal dilatation of the abdominal aorta of at least 1.5 times its normal diameter. Although the criterion of maximum diameter is still used in clinical practice to decide on a timely intervention, numerical studies have demonstrated the importance of other geometric factors. However, the major drawback of numerical studies is that they must be validated experimentally before clinical implementation. This work presents a new methodology to verify wall stress predicted from the numerical studies against the experimental testing. To this end, four AAA phantoms were manufactured using vacuum casting. The geometry of each phantom was subject to microcomputed tomography (µCT) scanning at zero and three other intraluminal pressures: 80, 100, and 120 mm Hg. A zero-pressure geometry algorithm was used to calculate the wall stress in the phantom, while the numerical wall stress was calculated with a finite-element analysis (FEA) solver based on the actual zero-pressure geometry subjected to 80, 100, and 120 mm Hg intraluminal pressure loading. Results demonstrate the moderate accuracy of this methodology with small relative differences in the average wall stress (1.14%). Additionally, the contribution of geometric factors to the wall stress distribution was statistically analyzed for the four phantoms. The results showed a significant correlation between wall thickness and mean curvature (MC) with wall stress.

A Methodology for the Derivation of Unloaded Abdominal Aortic Aneurysm Geometry With Experimental Validation.

In this work, we present a novel method for the derivation of the unloaded geometry of an abdominal aortic aneurysm (AAA) from a pressurized geometry in turn obtained by 3D reconstruction of computed tomography (CT) images. The approach was experimentally validated with an aneurysm phantom loaded with gauge pressures of 80, 120, and 140 mm Hg. The unloaded phantom geometries estimated from these pressurized states were compared to the actual unloaded phantom geometry, resulting in mean nodal surface distances of up to 3.9% of the maximum aneurysm diameter. An in-silico verification was also performed using a patient-specific AAA mesh, resulting in maximum nodal surface distances of 8 µm after running the algorithm for eight iterations. The methodology was then applied to 12 patient-specific AAA for which their corresponding unloaded geometries were generated in 5-8 iterations. The wall mechanics resulting from finite element analysis of the pressurized (CT image-based) and unloaded geometries were compared to quantify the relative importance of using an unloaded geometry for AAA biomechanics. The pressurized AAA models underestimate peak wall stress (quantified by the first principal stress component) on average by 15% compared to the unloaded AAA models. The validation and application of the method, readily compatible with any finite element solver, underscores the importance of generating the unloaded AAA volume mesh prior to using wall stress as a biomechanical marker for rupture risk assessment.

Monocytes mediate metastatic breast tumor cell adhesion to endothelium under flow.

Endothelial adhesion is necessary for the hematogenous dissemination of tumor cells. However, the metastatic breast tumor cell MDA-MB-231 does not bind to the endothelium under physiological flow conditions, suggesting alternate mechanisms of adhesion. Since monocytes are highly represented in the tumor microenvironment, and also bind to endothelium during inflammation, we hypothesized that the monocytes assist in the arrest of MDA-MB-231 on the endothelium. Using in vitro models of the dynamic shear environment of the vasculature, we show that TNF-α-activated THP1/primary human monocytes and MDA-MB-231 cells form stable aggregates, and that the monocytes in these aggregates mediate the adhesion of otherwise nonadherent MDA-MB-231 cells to inflamed endothelium under flow (55±2.4 vs. 1.7±0.82 at a shear stress of 0.5 dyn/cm(2), P<0.01). We also show that the hydrodynamic forces determine the size and orientation of aggregates adhered to the endothelium, and strongly favor the attachment of small aggregates with tumor cells downstream of flow (74-86% doublets at 0.5-2 dyn/cm(2), P<0.01). The 5-fold up-regulation of ICAM-1 on TNF-α-activated MDA-MB-231 cells through the Nf-κB pathway was found to be critical in MDA-MB-231-monocyte aggregation and endothelial adhesion. Our results demonstrate that, under inflammatory conditions, monocytes may serve to disseminate tumor cells through circulation, and the tumor-monocyte-endothelial axis may represent a new therapeutic target to reduce cancer metastasis.

Numerical simulation of particle transport and deposition in the pulmonary vasculature.

To quantify the transport and adhesion of drug particles in a complex vascular environment, computational fluid particle dynamics (CFPD) simulations of blood flow and drug particulate were conducted in three different geometries representing the human lung vasculature for steady and pulsatile flow conditions. A fully developed flow profile was assumed as the inlet velocity, and a lumped mathematical model was used for the calculation of the outlet pressure boundary condition. A receptor-ligand model was used to simulate the particle binding probability. The results indicate that bigger particles have lower deposition fraction due to less chance of successful binding. Realistic unsteady flow significantly accelerates the binding activity over a wide range of particle sizes and also improves the particle deposition fraction in bifurcation regions when comparing with steady flow condition. Furthermore, surface imperfections and geometrical complexity coupled with the pulsatility effect can enhance fluid mixing and accordingly particle binding efficiency. The particle binding density at bifurcation regions increases with generation order and drug carriers are washed away faster in steady flow. Thus, when studying drug delivery mechanism in vitro and in vivo, it is important to take into account blood flow pulsatility in realistic geometry. Moreover, tissues close to bifurcations are more susceptible to deterioration due to higher uptake.

Abdominal aortic aneurysm: from clinical imaging to realistic replicas.

The goal of this work is to develop a framework for manufacturing nonuniform wall thickness replicas of abdominal aortic aneurysms (AAAs). The methodology was based on the use of computed tomography (CT) images for virtual modeling, additive manufacturing for the initial physical replica, and a vacuum casting process and range of polyurethane resins for the final rubberlike phantom. The average wall thickness of the resulting AAA phantom was compared with the average thickness of the corresponding patient-specific virtual model, obtaining an average dimensional mismatch of 180 µm (11.14%). The material characterization of the artery was determined from uniaxial tensile tests as various combinations of polyurethane resins were chosen due to their similarity with ex vivo AAA mechanical behavior in the physiological stress configuration. The proposed methodology yields AAA phantoms with nonuniform wall thickness using a fast and low-cost process. These replicas may be used in benchtop experiments to validate deformations obtained with numerical simulations using finite element analysis, or to validate optical methods developed to image ex vivo arterial deformations during pressure-inflation testing.

A protocol for measuring pull-off stress of wound-treatment polymers.

Skin wounds and burns compromise the body's natural barrier to bacteria and other pathogens. While many forms of wound dressings are available, polymeric films are advantageous for various reasons, ranging from the ease of application to durability. One common drawback of using polymeric films for a wound bandage is that the films tend to adhere to common inanimate objects. Patients spend hours in contact with soft and hard materials pressed against their skin, which, if the skin was dressed with a polymeric film, would inflict further wound damage upon body movement. In this work, we present a novel technique that allowed for measuring polymeric tackiness, after a long incubation period, with materials regularly encountered in a hospital or home setting, and soft fabrics. The polymers were exposed to an environment intended to simulate daily conditions and the technique is designed to perform multiple experiments simultaneously with ease. Four commercially available polymers (new-skin, no-sting skin-prep, skin shield, and Silesse) were tested as proof-of-concept to gather preliminary data for an overall assessment of wound treatment efficacy, resulting in the estimation of pull-off stress of the polymers from a specimen of porcine skin. Silesse did not reveal a measurable tackiness, no-sting skin-prep had the highest mean tackiness (13.8 kPa), while the mean tackiness between new-skin and skin shield was approximately equal (9.8 kPa vs. 10.1 kPa, respectively), p = 0.05. Future work on polymeric fluids for wound dressing applications should include tensile stress and dynamic viscosity estimations.

Effects of intraluminal thrombus on patient-specific abdominal aortic aneurysm hemodynamics via stereoscopic particle image velocity and computational fluid dynamics modeling.

The pathology of the human abdominal aortic aneurysm (AAA) and its relationship to the later complication of intraluminal thrombus (ILT) formation remains unclear. The hemodynamics in the diseased abdominal aorta are hypothesized to be a key contributor to the formation and growth of ILT. The objective of this investigation is to establish a reliable 3D flow visualization method with corresponding validation tests with high confidence in order to provide insight into the basic hemodynamic features for a better understanding of hemodynamics in AAA pathology and seek potential treatment for AAA diseases. A stereoscopic particle image velocity (PIV) experiment was conducted using transparent patient-specific experimental AAA models (with and without ILT) at three axial planes. Results show that before ILT formation, a 3D vortex was generated in the AAA phantom. This geometry-related vortex was not observed after the formation of ILT, indicating its possible role in the subsequent appearance of ILT in this patient. It may indicate that a longer residence time of recirculated blood flow in the aortic lumen due to this vortex caused sufficient shear-induced platelet activation to develop ILT and maintain uniform flow conditions. Additionally, two computational fluid dynamics (CFD) modeling codes (Fluent and an in-house cardiovascular CFD code) were compared with the two-dimensional, three-component velocity stereoscopic PIV data. Results showed that correlation coefficients of the out-of-plane velocity data between PIV and both CFD methods are greater than 0.85, demonstrating good quantitative agreement. The stereoscopic PIV study can be utilized as test case templates for ongoing efforts in cardiovascular CFD solver development. Likewise, it is envisaged that the patient-specific data may provide a benchmark for further studying hemodynamics of actual AAA, ILT, and their convolution effects under physiological conditions for clinical applications.

On the Relative Effects of Wall and Intraluminal Thrombus Constitutive Material Properties in Abdominal Aortic Aneurysm Wall Stress.

INTRODUCTION: An abdominal aortic aneurysm (AAA) is a dilation localized in the infrarenal segment of the abdominal aorta that can expand continuously and rupture if left untreated. Computational methods such as finite element analysis (FEA) are widely used with in silico models to calculate biomechanical predictors of rupture risk while choosing constitutive material properties for the AAA wall and intraluminal thrombus (ILT). METHODS: In the present work, we investigated the effect of different constitutive material properties for the wall and ILT on 21 idealized and 10 unruptured patient-specific AAA geometries. Three material properties were used to characterize the wall and two for the ILT, leading to six material model combinations for each AAA geometry subject to appropriate boundary conditions. RESULTS: The results of the FEA simulations indicate significant differences in the average peak wall stress (PWS), 99th percentile wall stress (99th WS), and spatially averaged wall stress (SAWS) for all AAA geometries subject to the choice of a material model combination. Specifically, using a material model combination with a compliant ILT yielded statistically higher wall stresses compared to using a stiff ILT, irrespective of the constitutive equation used to model the AAA wall. DISCUSSION: This work provides quantitative insight into the variability of the wall stress distributions ensuing from AAA FEA modeling due to its strong dependency on population-averaged soft tissue material characterizations. This dependency leads to uncertainty about the true biomechanical state of stress of an individual AAA and the subsequent assessment of its rupture risk.

Assessing the impact of distal protection filter design characteristics on 30-day outcomes of carotid artery stenting procedures.

OBJECTIVE: This study aims to review retrospectively the records of patients treated with carotid artery stenting (CAS) to investigate the potential correlations between clinical variables, distal protection filter (DPF) type and characteristics, and 30-day peri-/postprocedural outcomes. METHODS: This is a multicenter, single-arm, nonrandomized retrospective study of patients who underwent filter-protected CAS in the Pittsburgh, Pennsylvania, region between July 2000 and May 2011. Analysis of peri-/postprocedural complications included myocardial infarction, transient ischemic attacks (TIA), stroke, death, and a composition of all adverse events (AEs). Filter characteristics for Accunet (Abbott Vascular, Santa Clara, Calif; n = 429 [58.8%]), Angioguard (Cordis Endovascular, Miami Lakes, Fla; n = 114 [15.6%]), FilterWire (Boston Scientific, Natick, Mass; n = 113 [15.5%]), Spider (ev3 Endovascular, Plymouth, Minn; n = 45 [6.2%]), and Emboshield (Abbott Vascular; n = 24 [3.3%]) were previously determined in vitro and were used to find correlations with CAS procedural outcomes. Both univariate and multivariate analyses were performed, as well as goodness-of-fit tests to find multivariate correlations with procedural outcomes. RESULTS: In total, 731 CAS procedures using six different DPFs were analyzed. Peri-/postprocedural AEs included 19 TIAs (2.6%), 38 strokes (5.2%), one myocardial infarction (0.1%), 19 deaths (3.6%), and a total of 61 patients with complications (8.3%). Univariate analysis for filter design characteristics showed that the composite of AE was negatively associated with both vascular resistance (P = .01) and eccentricity (P = .02) and was positively associated with porosity (P = .0007), number of pores (P = .005), and pore density (P = .001). Multivariate analysis and the goodness-of-fit test revealed that patients with a history of congestive heart failure, stroke, and TIA (each with odds ratio >1) led to a good-fit model P value of .72 for peri-/postprocedural AEs. Multivariate analysis was inconclusive for all filter design characteristics. CONCLUSIONS: The following filter design characteristics are independently significant for minimizing peri-/postprocedural AEs: higher vascular resistance, concentric in shape, greater capture efficiency, lower porosity, lower number of pores, and lower pore density. Lower porosity and smaller wall apposition were also found to be independently significant for minimization of peri-/postprocedural TIAs. This information can be used when considering the desirable design characteristics of future DPFs.).

The importance of patient-specific regionally varying wall thickness in abdominal aortic aneurysm biomechanics.

Abdominal aortic aneurysm (AAA) is a vascular condition where the use of a biomechanics-based assessment for patient-specific risk assessment is a promising approach for clinical management of the disease. Among various factors that affect such assessment, AAA wall thickness is expected to be an important factor. However, regionally varying patient-specific wall thickness has not been incorporated as a modeling feature in AAA biomechanics. To the best our knowledge, the present work is the first to incorporate patient-specific variable wall thickness without an underlying empirical assumption on its distribution for AAA wall mechanics estimation. In this work, we present a novel method for incorporating regionally varying wall thickness (the "PSNUT" modeling strategy) in AAA finite element modeling and the application of this method to a diameter-matched cohort of 28 AAA geometries to assess differences in wall mechanics originating from the conventional assumption of a uniform wall thickness. For the latter, we used both a literature-derived population average wall thickness (1.5 mm; the "UT" strategy) as well as the spatial average of our patient-specific variable wall thickness (the "PSUT" strategy). For the three different wall thickness modeling strategies, wall mechanics were assessed by four biomechanical parameters: the spatial maxima of the first principal stress, strain, strain-energy density, and displacement. A statistical analysis was performed to address the hypothesis that the use of any uniform wall thickness model resulted in significantly different biomechanical parameters compared to a patient-specific regionally varying wall thickness model. Statistically significant differences were obtained with the UT modeling strategy compared to the PSNUT strategy for the spatial maxima of the first principal stress (p = 0.002), strain (p = 0.0005), and strain-energy density (p = 7.83 e-5) but not for displacement (p = 0.773). Likewise, significant differences were obtained comparing the PSUT modeling strategy with the PSNUT strategy for the spatial maxima of the first principal stress (p = 9.68 e-7), strain (p = 1.03 e-8), strain-energy density (p = 9.94 e-8), and displacement (p = 0.0059). No significant differences were obtained comparing the UT and PSUT strategies for the spatial maxima of the first principal stress (p = 0.285), strain (p = 0.152), strain-energy density (p = 0.222), and displacement (p = 0.0981). This work strongly recommends the use of patient-specific regionally varying wall thickness derived from the segmentation of abdominal computed tomography (CT) scans if the AAA finite element analysis is focused on estimating peak biomechanical parameters, such as stress, strain, and strain-energy density.

Fluid-structure interaction modeling of abdominal aortic aneurysms: the impact of patient-specific inflow conditions and fluid/solid coupling.

Rupture risk assessment of abdominal aortic aneurysms (AAA) by means of biomechanical analysis is a viable alternative to the traditional clinical practice of using a critical diameter for recommending elective repair. However, an accurate prediction of biomechanical parameters, such as mechanical stress, strain, and shear stress, is possible if the AAA models and boundary conditions are truly patient specific. In this work, we present a complete fluid-structure interaction (FSI) framework for patient-specific AAA passive mechanics assessment that utilizes individualized inflow and outflow boundary conditions. The purpose of the study is two-fold: (1) to develop a novel semiautomated methodology that derives velocity components from phase-contrast magnetic resonance images (PC-MRI) in the infrarenal aorta and successfully apply it as an inflow boundary condition for a patient-specific fully coupled FSI analysis and (2) to apply a one-way-coupled FSI analysis and test its efficiency compared to transient computational solid stress and fully coupled FSI analyses for the estimation of AAA biomechanical parameters. For a fully coupled FSI simulation, our results indicate that an inlet velocity profile modeled with three patient-specific velocity components and a velocity profile modeled with only the axial velocity component yield nearly identical maximum principal stress (σ1), maximum principal strain (ε1), and wall shear stress (WSS) distributions. An inlet Womersley velocity profile leads to a 5% difference in peak σ1, 3% in peak ε1, and 14% in peak WSS compared to the three-component inlet velocity profile in the fully coupled FSI analysis. The peak wall stress and strain were found to be in phase with the systolic inlet flow rate, therefore indicating the necessity to capture the patient-specific hemodynamics by means of FSI modeling. The proposed one-way-coupled FSI approach showed potential for reasonably accurate biomechanical assessment with less computational effort, leading to differences in peak σ1, ε1, and WSS of 14%, 4%, and 18%, respectively, compared to the axial component inlet velocity profile in the fully coupled FSI analysis. The transient computational solid stress approach yielded significantly higher differences in these parameters and is not recommended for accurate assessment of AAA wall passive mechanics. This work demonstrates the influence of the flow dynamics resulting from patient-specific inflow boundary conditions on AAA biomechanical assessment and describes methods to evaluate it through fully coupled and one-way-coupled fluid-structure interaction analysis.

Variability of computational fluid dynamics solutions for pressure and flow in a giant aneurysm: the ASME 2012 Summer Bioengineering Conference CFD Challenge.

Stimulated by a recent controversy regarding pressure drops predicted in a giant aneurysm with a proximal stenosis, the present study sought to assess variability in the prediction of pressures and flow by a wide variety of research groups. In phase I, lumen geometry, flow rates, and fluid properties were specified, leaving each research group to choose their solver, discretization, and solution strategies. Variability was assessed by having each group interpolate their results onto a standardized mesh and centerline. For phase II, a physical model of the geometry was constructed, from which pressure and flow rates were measured. Groups repeated their simulations using a geometry reconstructed from a micro-computed tomography (CT) scan of the physical model with the measured flow rates and fluid properties. Phase I results from 25 groups demonstrated remarkable consistency in the pressure patterns, with the majority predicting peak systolic pressure drops within 8% of each other. Aneurysm sac flow patterns were more variable with only a few groups reporting peak systolic flow instabilities owing to their use of high temporal resolutions. Variability for phase II was comparable, and the median predicted pressure drops were within a few millimeters of mercury of the measured values but only after accounting for submillimeter errors in the reconstruction of the life-sized flow model from micro-CT. In summary, pressure can be predicted with consistency by CFD across a wide range of solvers and solution strategies, but this may not hold true for specific flow patterns or derived quantities. Future challenges are needed and should focus on hemodynamic quantities thought to be of clinical interest.