In vivo Multi-perspective 3D + t Ultrasound Imaging and Motion Estimation of Abdominal Aortic Aneurysms.

Time-resolved three-dimensional ultrasound (3D + t US) is a promising imaging modality for monitoring abdominal aortic aneurysms (AAAs), providing their 3D geometry and motion. The lateral contrast of US is poor, a well-documented drawback which multi-perspective (MP) imaging could resolve. This study aims to show the feasibility of in vivo multi-perspective 3D + t ultrasound imaging of AAAs for improving the image contrast and displacement accuracy. To achieve this, single-perspective (SP) aortic ultrasound images from three different angles were spatiotemporally registered and fused, and the displacements were compounded. The fused MP had a significantly higher wall-lumen contrast than the SP images, for both patients and volunteers (P < .001). MP radial displacements patterns are smoother than SP patterns in 67% of volunteers and 92% of patients. The MP images from three angles have a decreased tracking error (P < .001 for all participants), and an improved SNRe compared to two out of three SP images (P < .05). This study has shown the added value of MP 3D + t US, improving both image contrast and displacement accuracy in AAA imaging. This is a step toward using multiple or large transducers in the clinic to capture the 3D geometry and strain more accurately, for patient-specific characterization of AAAs.

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.

Toward assessment of rupture risk predictors in abdominal aortic aneurysms including intraluminal thrombus based on 3D+t ultrasound images.

Image-based patient-specific rupture risk analysis for abdominal aortic aneurysms (AAAs) has shown considerable promise. However, clinical translation has been hampered by the use of invasive and costly imaging modalities. Despite being a promising alternative, ultrasound (US) makes a full analysis, including intraluminal thrombus (ILT), not trivial. This study explored the feasibility of assessing AAA rupture risk parameters, e.g., peak wall stress (PWS) and peak wall rupture index (PWRI), using US-based models of the AAA wall, finally including ILT. Three-dimensional US data were segmented from a group of AAA patients whose CT data were available within 30 days. The segmented vessel wall and ILT boundaries were converted into a mesh including and excluding ILT to evaluate the effect of adding ILT on the model output. US-based rupture risk parameters (PWS and PWRI) were compared to CT-based results. The US-based PWS and PWRI, including ILT, showed good agreement with CT-based results, and the model excluding ILT showed no significant bias in wall stress or rupture index. When including ILT, a lower US-based wall stress and rupture index of 7.2% and 3.8% were found, respectively. The intraclass correlation coefficient (ICC) of PWS was 0.60. The highest ICC was found for the PWRI (ICC = 0.86), indicating good absolute agreement. This study showed that PWRI can be estimated with US when including the ILT, yielding comparable results to CT, and good absolute agreement. Future work should focus on improving the contrast of ILT in US, since this will be essential to performing large-scale studies in AAA cohorts.

3D-Ultrasound Based Mechanical and Geometrical Analysis of Abdominal Aortic Aneurysms and Relationship to Growth.

The heterogeneity of progression of abdominal aortic aneurysms (AAAs) is not well understood. This study investigates which geometrical and mechanical factors, determined using time-resolved 3D ultrasound (3D + t US), correlate with increased growth of the aneurysm. The AAA diameter, volume, wall curvature, distensibility, and compliance in the maximal diameter region were determined automatically from 3D + t echograms of 167 patients. Due to limitations in the field-of-view and visibility of aortic pulsation, measurements of the volume, compliance of a 60 mm long region and the distensibility were possible for 78, 67, and 122 patients, respectively. Validation of the geometrical parameters with CT showed high similarity, with a median similarity index of 0.92 and root-mean-square error (RMSE) of diameters of 3.5 mm. Investigation of Spearman correlation between parameters showed that the elasticity of the aneurysms decreases slightly with diameter (p = 0.034) and decreases significantly with mean arterial pressure (p < 0.0001). The growth of a AAA is significantly related to its diameter, volume, compliance, and surface curvature (p < 0.002). Investigation of a linear growth model showed that compliance is the best predictor for upcoming AAA growth (RMSE 1.70 mm/year). To conclude, mechanical and geometrical parameters of the maximally dilated region of AAAs can automatically and accurately be determined from 3D + t echograms. With this, a prediction can be made about the upcoming AAA growth. This is a step towards more patient-specific characterization of AAAs, leading to better predictability of the progression of the disease and, eventually, improved clinical decision making about the treatment of AAAs.

Effect of Collateral Flow on Catheter-Based Assessment of Cardiac Microvascular Obstruction.

Cardiac microvascular obstruction (MVO) associated with acute myocardial infarction (heart attack) is characterized by partial or complete elimination of perfusion in the myocardial microcirculation. A new catheter-based method (CoFI, Controlled Flow Infusion) has recently been developed to diagnose MVO in the catheterization laboratory during acute therapy of the heart attack. A porcine MVO model demonstrates that CoFI can accurately identify the increased hydraulic resistance of the affected microvascular bed. A benchtop microcirculation model was developed and tuned to reproduce in vivo MVO characteristics. The tuned benchtop model was then used to systematically study the effect of different levels of collateral flow. These experiments showed that measurements obtained in the catheter-based method were adversely affected such that collateral flow may be misinterpreted as MVO. Based on further analysis of the measured data, concepts to mitigate the adverse effects were formulated which allow discrimination between collateral flow and MVO.

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.

Correction to: Decision Tree Based Classification of Abdominal Aortic Aneurysms Using Geometry Quantification Measures.

This erratum is to correct the variable name on the left hand side of Eq. (2). The correct variable name is "Diameter" rather than the stated "Area."

Decision Tree Based Classification of Abdominal Aortic Aneurysms Using Geometry Quantification Measures.

Abdominal aortic aneurysm (AAA) is an asymptomatic aortic disease with a survival rate of 20% after rupture. It is a vascular degenerative condition different from occlusive arterial diseases. The size of the aneurysm is the most important determining factor in its clinical management. However, other measures of the AAA geometry that are currently not used clinically may also influence its rupture risk. With this in mind, the objectives of this work are to develop an algorithm to calculate the AAA wall thickness and abdominal aortic diameter at planes orthogonal to the vessel centerline, and to quantify the effect of geometric indices derived from this algorithm on the overall classification accuracy of AAA based on whether they were electively or emergently repaired. Such quantification was performed based on a retrospective review of existing medical records of 150 AAA patients (75 electively repaired and 75 emergently repaired). Using an algorithm implemented within the MATLAB computing environment, 10 diameter- and wall thickness-related indices had a significant difference in their means when calculated relative to the AAA centerline compared to calculating them relative to the medial axis. Of these 10 indices, nine were wall thickness-related while the remaining one was the maximum diameter (Dmax). Dmax calculated with respect to the medial axis is over-estimated for both electively and emergently repaired AAA compared to its counterpart with respect to the centerline. C5.0 decision trees, a machine learning classification algorithm implemented in the R environment, were used to construct a statistical classifier. The decision trees were built by splitting the data into 70% for training and 30% for testing, and the properties of the classifier were estimated based on 1000 random combinations of the 70/30 data split. The ensuing model had average and maximum classification accuracies of 81.0 and 95.6%, respectively, and revealed that the three most significant indices in classifying AAA are, in order of importance: AAA centerline length, L2-norm of the Gaussian curvature, and AAA wall surface area. Therefore, we infer that the aforementioned three geometric indices could be used in a clinical setting to assess the risk of AAA rupture by means of a decision tree classifier. This work provides support for calculating cross-sectional diameters and wall thicknesses relative to the AAA centerline and using size and surface curvature based indices in classification studies of AAA.

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.

The role of wall shear stress in the assessment of right ventricle hydraulic workload.

Pulmonary hypertension (PH) is a devastating disease affecting approximately 15-50 people per million, with a higher incidence in women. PH mortality is mostly attributed to right ventricle (RV) failure, which results from RV hypotrophy due to an overburdened hydraulic workload. The objective of this study is to correlate wall shear stress (WSS) with hemodynamic metrics that are generally accepted as clinical indicators of RV workload and are well correlated with disease outcome. Retrospective right heart catheterization data for 20 PH patients were analyzed to derive pulmonary vascular resistance (PVR), arterial compliance (C), and an index of wave reflections (Γ). Patient-specific contrast-enhanced computed tomography chest images were used to reconstruct the individual pulmonary arterial trees up to the seventh generation. Computational fluid dynamics analyses simulating blood flow at peak systole were conducted for each vascular model to calculate WSS distributions on the endothelial surface of the pulmonary arteries. WSS was found to be decreased proportionally with elevated PVR and reduced C. Spatially averaged WSS (SAWSS) was positively correlated with PVR (R (2) = 0.66), C (R (2) = 0.73), and Γ (R (2) = 0.5) and also showed promising preliminary correlations with RV geometric characteristics. Evaluating WSS at random cross sections in the proximal vasculature (main, right, and left pulmonary arteries), the type of data that can be acquired from phase-contrast magnetic resonance imaging, did not reveal the same correlations. In conclusion, we found that WSS has the potential to be a viable and clinically useful noninvasive metric of PH disease progression and RV health. Future work should be focused on evaluating whether SAWSS has prognostic value in the management of PH and whether it can be used as a rapid reactivity assessment tool, which would aid in selection of appropriate therapies.