Patient-specific computational evaluation of stiffness distribution in ascending thoracic aortic aneurysm

Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization

Majority of modern techniques for creating and optimizing the geometry of medical devices are based on a combination of computer-aided designs and the utility of the finite element method This approach, however, is limited by the number of geometries that can be investigated and by the time required for design optimization. To address this issue, we propose a generative design approach that combines machine learning (ML) methods and optimization algorithms. We evaluate eight different machine learning methods, including decision tree-based and boosting algorithms, neural networks, and ensembles. For optimal design, we investigate six state-of-the-art optimization algorithms, including Random Search, Tree-structured Parzen Estimator, CMA-ES-based algorithm, Nondominated Sorting Genetic Algorithm, Multiobjective Tree-structured Parzen Estimator, and Quasi-Monte Carlo Algorithm. In our study, we apply the proposed approach to study the generative design of a prosthetic heart valve (PHV). The design constraints of the prosthetic heart valve, including spatial requirements, materials, and manufacturing methods, are used as inputs, and the proposed approach produces a final design and a corresponding score to determine if the design is effective. Extensive testing leads to the conclusion that utilizing a combination of ensemble methods in conjunction with a Tree-structured Parzen Estimator or a Nondominated Sorting Genetic Algorithm is the most effective method in generating new designs with a relatively low error rate. Specifically, the Mean Absolute Percentage Error was found to be 11.8% and 10.2% for lumen and peak stress prediction respectively. Furthermore, it was observed that both optimization techniques result in design scores of approximately 95%. From both a scientific and applied perspective, this approach aims to select the most efficient geometry with given input parameters, which can then be prototyped and used for subsequent in vitro experiments. By proposing this approach, we believe it will replace or complement CAD-FEM-based modeling, thereby accelerating the design process and finding better designs within given constraints. The repository, which contains the essential components of the study, including curated source code, dataset, and trained models, is publicly available at https://github.com/ViacheslavDanilov/generative_design.

Longitudinal versus circumferential biomechanical behavior of the aneurysmal ascending aorta

OBJECTIVES

We evaluate the independent effects of patient and aortic tissue characteristics on biaxial physiologic mechanical metrics in aneurysmal and nonaneurysmal tissues, and uniaxial failure metrics in aneurysmal tissue, comparing longitudinal and circumferential behavior.

METHODS

From February 2017 to October 2022, 382 aortic specimens were collected from 134 patients; 268 specimens underwent biaxial testing, and 114 specimens underwent uniaxial testing. Biaxial testing evaluated Green-Lagrange transition strain and low and high tangent moduli. Uniaxial testing evaluated failure stretch, Cauchy stress, and low and high tangent moduli. Longitudinal gradient boosting models were implemented to estimate mechanical metrics and covariates of importance.

RESULTS

On biaxial testing, nonaneurysmal tissue was less deformable and exhibited a lower transition strain than aneurysmal tissue in the longitudinal (0.18 vs 0.30, P < .001) and circumferential (0.25 vs 0.30, P = .01) directions. Older age and increasing ascending aortic length contributed most to predicting transition strain. On uniaxial testing, longitudinal specimens failed at lower stretch (1.4 vs 1.5, P = .003) and Cauchy stress (1.0 vs 1.9 kPa, P < .001) than circumferential specimens. Failure stretch and Cauchy stress were most strongly associated with tissue orientation and decreased sharply with older age. Age, ascending aortic length, and tissue thickness were the most frequent covariates predicting mechanical metrics across 10 prediction models.

CONCLUSIONS

Age was the strongest predictor of mechanical behavior. After adjusting for age, nonaneurysmal tissue was less deformable than aneurysmal tissue. Differences in longitudinal and circumferential mechanics contribute to tissue dysfunction and failure in ascending aneurysms. This highlights the need to better understand the effects of age, ascending aortic length, and thickness on clinical aortic behavior.

On vasa vasorum: A history of advances in understanding the vessels of vessels

The vasa vasorum are a vital microvascular network supporting the outer wall of larger blood vessels. Although these dynamic microvessels have been studied for centuries, the importance and impact of their functions in vascular health and disease are not yet fully realized. There is now rich knowledge regarding what local progenitor cell populations comprise and cohabitate with the vasa vasorum and how they might contribute to physiological and pathological changes in the network or its expansion via angiogenesis or vasculogenesis. Evidence of whether vasa vasorum remodeling incites or governs disease progression or is a consequence of cardiovascular pathologies remains limited. Recent advances in vasa vasorum imaging for understanding cardiovascular disease severity and pathophysiology open the door for theranostic opportunities. Approaches that strive to control angiogenesis and vasculogenesis potentiate mitigation of vasa vasorum-mediated contributions to cardiovascular diseases and emerging diseases involving the microcirculation.

Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications

Inverse modeling approaches in cardiovascular medicine are a collection of methodologies that can provide non-invasive patient-specific estimations of tissue properties, mechanical loads, and other mechanics-based risk factors using medical imaging as inputs. Its incorporation into clinical practice has the potential to improve diagnosis and treatment planning with low associated risks and costs. These methods have become available for medical applications mainly due to the continuing development of image-based kinematic techniques, the maturity of the associated theories describing cardiovascular function, and recent progress in computer science, modeling, and simulation engineering. Inverse method applications are multidisciplinary, requiring tailored solutions to the available clinical data, pathology of interest, and available computational resources. Herein, we review biomechanical modeling and simulation principles, methods of solving inverse problems, and techniques for image-based kinematic analysis. In the final section, the major advances in inverse modeling of human cardiovascular mechanics since its early development in the early 2000s are reviewed with emphasis on method-specific descriptions, results, and conclusions. We draw selected studies on healthy and diseased hearts, aortas, and pulmonary arteries achieved through the incorporation of tissue mechanics, hemodynamics, and fluid-structure interaction methods paired with patient-specific data acquired with medical imaging in inverse modeling approaches.

Fully-Coupled FSI Computational Analyses in the Ascending Thoracic Aorta Using Patient-Specific Conditions and Anisotropic Material Properties

Computational hemodynamics has become increasingly important within the context of precision medicine, providing major insight in cardiovascular pathologies. However, finding appropriate compromise between speed and accuracy remains challenging in computational hemodynamics for an extensive use in decision making. For example, in the ascending thoracic aorta, interactions between the blood and the aortic wall must be taken into account for the sake of accuracy, but these fluid structure interactions (FSI) induce significant computational costs, especially when the tissue exhibits a hyperelastic and anisotropic response. The objective of the current study is to use the Small On Large (SOL) theory to linearize the anisotropic hyperelastic behavior in order to propose a reduced-order model for FSI simulations of the aorta. The SOL method is tested for fully-coupled FSI simulations in a patient-specific aortic geometry presenting an Ascending Thoracic Aortic Aneurysm (aTAA). The same model is also simulated with a fully-coupled FSI with non-linear material behavior, without SOL linearization. Eventually, the results and computational times with and without the SOL are compared. The SOL approach is demonstrated to provide a significant reduction of computational costs for FSI analysis in the aTAA, and the results in terms of stress state distribution are comparable. The method is implemented in ANSYS and will be further evaluated for clinical applications.

Interpretation of Experimental Data is Substantial for Constitutive Characterization of Arterial Tissue

The paper aims at evaluation of mechanical tests of soft tissues and creation of their representative stress-strain responses and respective constitutive models. Interpretation of sets of experimental results depends highly on the approach to the data analysis. Their common representation through mean and standard deviation may be misleading and give nonrealistic results. In the paper, raw data of seven studies consisting of 11 experimental data sets (concerning carotid wall and atheroma tissues) are re-analyzed to show the importance of their rigorous analysis. The sets of individual uniaxial stress-stretch curves are evaluated using three different protocols: stress-based, stretch-based, and constant-based, and the population-representative response is created by their mean or median values. Except for nearly linear responses, there are substantial differences between the resulting curves, being mostly the highest for constant-based evaluation. But also the stretch-based evaluation may change the character of the response significantly. Finally, medians of the stress-based responses are recommended as the most rigorous approach for arterial and other soft tissues with significant strain stiffening.

Toward precision medicine in vascular connective tissue disorders

Tremendous progress has been made in understanding the etiology, pathogenesis, and treatment of inherited vascular connective tissue disorders. While new insights regarding disease etiology and pathogenesis have informed patient counseling and care, there are numerous obstacles that need to be overcome in order to achieve the full promise of precision medicine. In this review, these issues will be discussed in the context of Marfan syndrome and Loeys-Dietz syndrome, with additional emphasis on the pioneering contributions made by Victor McKusick.

Aortic aneurysms and dissections: Unmet needs from physicians and engineers perspectives

The treatment of aortic disease is complex, requiring cardiothoracic and vascular surgeons to make pre-, post- and intraoperative decisions directly influencing patient survival and well-being. Despite tremendous advancement in vascular surgery and endovascular techniques in the last two decades, along with the abundance of research in the field, many unmet needs and unanswered questions remain. Tight collaboration between engineers and physicians is a keystone in translating new tools, techniques, and devices into practice. Here, we have gathered our perspective, as physicians and engineers, in several pressing issues associated with the diagnosis and treatment of aortic aneurysms and dissection, referring to the current knowledge and practice, signifying unmet needs as well as future directions.