Design, Development and Temporal Evaluation of an MRI-Compatible In-Vitro Circulation Model Using a Compliant AAA Phantom

Biomechanical characterization of abdominal aortic aneurysms (AAA) 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 non-standard 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. 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 intra-sac pressure conditions. 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

Studies on the assessment of abdominal aortic aneurysm rupture risk

Abdominal Aortic Aneurysms (AAA) are a type of cardiovascular disease that affect the infra renal aorta, where the normal aorta dilates to more than two times its original diameter. Progressive dilation leads to weakening of the aortic wall and eventually, rupture. Pathophysiological, biomechanical and genetic factors contribute to the genesis, growth and rupture of the AAA. Current clinical management of AAA is based on the maximum transverse diameter of the dilated lumen to recommend surgical intervention. However, several additional factors play a role in the progression to rupture. Therefore, a patient specific assessment based on biomechanical factors and antecedents can lead to the development of a predictive tool that can be implemented at the bedside. This thesis has investigated a biomechanical as well as machine learning/statistical approach to developing an accurate patient specific predictive tool for rupture assessment in Asian AAA patients. This was deemed necessary due to the differences in morphology between Asian and Caucasian patients. These differences have caused difficulties with the graft delivery during Endovascular Aneurysm Repair (EVAR) and subsequent follow up of AAA Asian patients. The clinical significance of these differences can be brought out through a comparison of the geometric parameters in each cohort along with the mechanical stresses developed in these aneurysms. A comparison of 19 patients each from the Asian and Caucasian patient groups was conducted to establish the differences in morphology and hence, the biomechanics of rupture in each cohort. This was done using a combination of in-house segmentation codes and commercial finite element analysis (FEA) software. Subsequently, statistical analysis was also applied to generate geometric surrogates to predict the wall stresses in the patient groups. The study resulted in significant differences in peak wall stress among Asian patients with high maximum diameters when modeled with non-linear constitutive material models. The means of the biomechanical stresses between the two patient groups were not significant between the two patient groups. It was seen that the geometric indices that were significantly correlated to the spatially averaged wall stress in the Asian patient group were maximum diameter, proximal neck diameter, L2 norm of the mean curvature (MLN), square root sum of the Gaussian curvature (KM), and the L2 norm of the minor principal curvature (K2LN) while in the Caucasian patient group it was only distal neck diameter. The resulting six geometric indices can be used as geometric surrogates to quantify wall stresses in the patients. The five indices in addition to maximum diameter can predict the spatially averaged wall stresses with an accuracy of 72.87% in the Asian patient group and 74.74% in the Caucasian patient group. Machine learning algorithms (MLA) as a possible approach to hasten the diagnostic process was explored using multiple classifiers such as decision trees, support vector machines (SVM), Naïve Bayes and logistic regression. The most suitable classifier analyzing a cohort of 312 patients (155 AAA and 157 normal) with geometric and patient antecedent attributes, was determined as the Naïve Bayes algorithm. Further, a feature selection algorithm was applied to obtain the attributes that are significantly correlated to rupture. These included the proximal neck diameter, the neck length and the right iliac artery diameter in addition to the maximum diameter of the AAA. A further investigation involving 155 patients’ data was done to estimate whether only antecedent attributes can accurately predict rupture in the cohort. Using only attributes based on patient history, it was observed that the MLA can accurately extract the most significant clinical features that are correlated to AAA rupture. It was determined that machine learning algorithms can be used in synergy with the biomechanics to estimate rupture risk in the cohort. A fluid structure interaction (FSI) based model is developed on patient specific geometry data originating in Singapore to establish the biomechanics of rupture in Asian AAA. A comparative analysis of two patient models with differing maximum diameters and literature based inlet and outlet boundary conditions was carried out by extracting biomechanical parameters such as principal stresses and wall shear stresses from the simulation. The patient model with the larger maximum transverse diameter developed lower principal and wall shear stresses than the one with a smaller value of the diameter. In order to validate computational results, experimental methods need to be developed to accurately construct a physical model of the system being investigated. A patient specific silicone AAA phantom was used in a benchtop flow loop to mimic the fluid structure interaction between the aortic wall and blood. Realistic boundary conditions were imposed using a physical Windkessel model that mimics the impedance in the abdominal aortic system. Resistance and capacitance modules were built to establish the impedance values that generate realistic pressure values at the outlet. Future work will involve analysis of a larger cohort of patients to compare the Asian and Caucasian cohorts, to develop a more robust machine learning model that can predict rupture and incorporation of patient specific boundary conditions in FSI simulations. Experiments using in vivo impedances will also be done to establish realistic boundary conditions in the AAA.Doctor of Philosophy (MAE

A Review of Computational Methods to Predict the Risk of Rupture of Abdominal Aortic Aneurysms

Computational methods have played an important role in health care in recent years, as determining parameters that affect a certain medical condition is not possible in experimental conditions in many cases. Computational fluid dynamics (CFD) methods have been used to accurately determine the nature of blood flow in the cardiovascular and nervous systems and air flow in the respiratory system, thereby giving the surgeon a diagnostic tool to plan treatment accordingly. Machine learning or data mining (MLD) methods are currently used to develop models that learn from retrospective data to make a prediction regarding factors affecting the progression of a disease. These models have also been successful in incorporating factors such as patient history and occupation. MLD models can be used as a predictive tool to determine rupture potential in patients with abdominal aortic aneurysms (AAA) along with CFD-based prediction of parameters like wall shear stress and pressure distributions. A combination of these computer methods can be pivotal in bridging the gap between translational and outcomes research in medicine. This paper reviews the use of computational methods in the diagnosis and treatment of AAA.Published versio

Numerical Simulation of Unsteady Aerodynamics in Insect Flight using Generic Planform Shapes

The aim of this work is to provide a better understanding of the aerodynamic performance of various planform shapes undergoing various kinematics for flapping wing flight. There have been extensive analyses and experiments of 2D airfoils undergoing flapping motion. These studies reveal various mechanisms for unsteady aerodynamic lift and thrust generation such as the importance of the control of leading edge vortices. However, 3D analysis of flapping wing flight with various planform shapes is required to study 3D flow structures and their role in determining the lift and thrust for the design of a future flapping wing micro air vehicle.Firstly, CFD analysis has been carried out on 5 rigid simple planform shapes (rectangle, reverse ellipse, ellipse, triangle and four ellipse) at a Reynolds number of 13500 to determine the effect of planform shape on the aerodynamic performance of a flapping wing undergoing hover kinematics. The kinematics is described in terms of only 2 angles, sweep and pitch. The performance is compared through the use of force histories, pressure distribution plots, flow structure images, power utilized and lift generated. It is found that the area distribution near the wing tip contributes to the difference in lift generation between the five planforms. The rectangular planform is found to have the best performance with a suitable balance of power consumed and lift generated as compared to the reverse ellipse which has the highest lift generation.Secondly, the kinematics from a honeybee and thrips are used on the same 5 planform shapes to determine the effect of kinematics on the performance of each planform. These kinematics are more realistic as the variations of all the 3 angles (sweep, pitch and elevation) are considered. It is found from the analysis that the reverse ellipse planform performs the best in terms of lift generation but the ellipse performs better overall when power economy (ratio of average vertical force generated to the average power consumed) is considered. In thrips kinematics, the reverse ellipse is again seen to perform the best in terms of the power economy. For a given planforms, there is a reduction in lift generation in thrips kinematics compared to the honeybee kinematics except in the case of the rectangle where there is an increase.These results have been explained in terms of the flow structures and associated pressure distributions generated on the wings

On the assessment of abdominal aortic aneurysm rupture risk in the Asian population based on geometric attributes

This study aims to review retrospectively the records of Asian patients diagnosed with abdominal aortic aneurysm to investigate the potential correlations between clinical and morphological parameters within the context of whether the aneurysms were ruptured or unruptured. A machine-learning-based approach is proposed to predict the rupture status of Asian abdominal aortic aneurysm by comparing four different classifiers trained with clinical and geometrical parameters obtained from computed tomography images. The classifiers were applied on 312 patient data sets obtained from a regulatory-approved database. The data sets included 17 attributes under three classes: unruptured abdominal aortic aneurysm, ruptured abdominal aortic aneurysm, and normal aorta without aneurysm. Four different classification models, namely, Decision trees, Naïve Bayes, logistic regression, and support vector machines were applied to the patient data set. The models were evaluated by 10-fold cross-validation and the classifier performances were assessed with classification accuracy, area under the curve of receiver operator characteristic, and F-measures. Data analysis and evaluation were performed using the Weka machine learning application. The results indicated that Naïve Bayes achieved the best performance among the classifiers with a classification accuracy of 95.2%, an area under the curve of 0.974, and an F-measure of 0.952. The clinical implications of this work can be addressed in two ways. The best classifier can be applied to prospectively acquired data to predict the likelihood of aneurysm rupture. Next, it would be necessary to estimate the attributes implicated in rupture risk beyond just maximum aneurysm diameter.Accepted versio

A method to produce high contrast vein visualization in active dynamic thermography (ADT)

In this study, a method that will aid in the visualization of vein topology on a target area on the body of a human subject is demonstrated. An external cooling means is configured to cool the left forearm of fourteen study participants, effecting an active thermal change or recovery in the target area upon removal of cooling. An infrared (IR) thermal camera was used to capture a series of transient thermal images. These images were then processed to extract Dynamic synthetic images (SI) throughout the active thermal change or recovery process. Dynamic SI was calculated using a quantitative parameter called tissue activity ratio (TAR), which is defined by the rate of rewarming to the rate of cooling at each pixel of interest. A fixed step size of rewarming temperature (0.5 °C) was used to progressively extract multiple synthetic images throughout the whole recovery process. Compared to a Static SI extraction method, where only a single SI results from the whole active dynamic thermography (ADT) sequence, this study demonstrates a live feed of high contrast vein visualizations by using the Dynamic SI method. Furthermore, the dependency of Dynamic SI contrast on the temperature of the external cooling stimulation was investigated. Three cooling stimulation temperatures (5 °C, 8 °C, and 11 °C) were tested, where no statistically significant difference in the resulting SI contrast was found. Lastly, a discussion is put forth on assisting venipuncture or cannulation-based clinical applications, through the incorporation of the proposed method with a projection system.Nanyang Technological UniversitySubmitted/Accepted versionThis study was supported by the SingHealth-NTU collaborative research grant (Grant number: SHS-NTU/014/2016)

Application of fluid–structure interaction methods to estimate the mechanics of rupture in Asian abdominal aortic aneurysms

Abdominal aortic aneurysms (AAAs) occur because of dilation of the infra-renal aorta to more than 150% of its initial diameter. Progression to rupture is aided by several pathophysiological and biomechanical factors. Surgical intervention is recommended when the aneurysm maximum transverse diameter (DAAA) exceeds 55 mm. A system model that incorporates biomechanical parameters will improve prognosis and establish a relationship between AAA geometry and rupture risk. Two Asian patient-specific AAA geometries were obtained from an IRB-approved vascular database. A biomechanical model based on the fluid–structure interaction (FSI) method was developed for a small aneurysm with DAAA of 35 mm and a large aneurysm with a corresponding diameter of 75 mm. The small aneurysm (patient 1) developed a maximum principal stress (PS1) of 3.16e5 Pa and the large aneurysm (patient 2) developed a PS1 of 2.32e5 Pa. Maximum deformation of arterial wall was 0.0020 m and 0.0022 m for patients 1 and 2 respectively. Location of maximum integral wall shear stress (WSS) (fluid) was different from that of PS1. Induced WSS was also higher in patient 1 (18.74 Pa vs 12.88 Pa). An FSI model incorporating the effect of both the structural and fluid domains aids in better understanding of the mechanics of AAA rupture. Patient 1, having a lower DAAA than patient 2, developed a larger PS1 and WSS. It may be concluded that DAAA may not be the sole determinant of AAA rupture risk.Accepted versio

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.Nanyang Technological UniversityThis work was funded by a Research Student Scholarship from the Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore to Tejas Canchi, and a U.S. National Institutes of Health award (R01HL121293) to Ender A. Finol