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.

Relation Between Neck Skin Temperature Measurement and Carotid Artery Stenosis: In-Vitro Evaluation.

Carotid artery stenosis is a form of atherosclerosis, where thrombus formation restricts the passage of blood through the carotid artery leading to irreversible damage in the brain tissue. The presence of stenosis in the carotid artery results in abnormal temperature maps on the external skin surface, which can be captured and quantified using noncontact/noninvasive infrared (IR) thermal imaging/thermography. In this study, a thermally charged in vitro carotid artery flow loop, using 0% and 75% stenosis models, was designed to study the thermal effect on the external skin surface. The carotid artery flow was encapsulated with polydimethylsiloxane (PDMS) resembling neck tissue, of which the external surface temperature maps were studied using IR thermography. Using the mean temperature as a threshold value, the resultant thermal image was processed and normalized. Between the two stenosis models, disruption in the thermal features corresponding to the presence of stenosis was observed. The method described in this study paves the path to experimentally study the thermal effect of the presence of stenosis in the carotid artery.

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.

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.

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.

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.