Multicomponent Mechanical Characterization of Atherosclerotic Human Coronary Arteries: An Experimental and Computational Hybrid Approach

Integrating Computational and Biological Hemodynamic Approaches to Improve Modeling of Atherosclerotic Arteries

Atherosclerosis is the primary cause of cardiovascular disease, resulting in mortality, elevated healthcare costs, diminished productivity, and reduced quality of life for individuals and their communities. This is exacerbated by the limited understanding of its underlying causes and limitations in current therapeutic interventions, highlighting the need for sophisticated models of atherosclerosis. This review critically evaluates the computational and biological models of atherosclerosis, focusing on the study of hemodynamics in atherosclerotic coronary arteries. Computational models account for the geometrical complexities and hemodynamics of the blood vessels and stenoses, but they fail to capture the complex biological processes involved in atherosclerosis. Different in vitro and in vivo biological models can capture aspects of the biological complexity of healthy and stenosed vessels, but rarely mimic the human anatomy and physiological hemodynamics, and require significantly more time, cost, and resources. Therefore, emerging strategies are examined that integrate computational and biological models, and the potential of advances in imaging, biofabrication, and machine learning is explored in developing more effective models of atherosclerosis.

Intravascular imaging of angioplasty balloon under-expansion during pre-dilation predicts hyperelastic behavior of coronary artery lesions

Introduction: Stent-induced mechanical stimuli cause pathophysiological responses in the coronary artery post-treatment. These stimuli can be minimized through choice of stent, size, and deployment strategy. However, the lack of target lesion material characterization is a barrier to further personalizing treatment. A novel ex-vivo angioplasty-based intravascular imaging technique using optical coherence tomography (OCT) was developed to characterize local stiffness of the target lesion. Methods: After proper institutional oversight, atherosclerotic coronary arteries (n = 9) were dissected from human donor hearts for ex vivo material characterization <48 h post-mortem. Morphology was imaged at the diastolic blood pressure using common intravascular OCT protocols and at subsequent pressures using a specially fabricated perfusion balloon that accommodates the OCT imaging wire. Balloon under-expansion was quantified relative to the nominal balloon size at 8 ATM. Correlation to a constitutive hyperelastic model was empirically investigated (n = 13 plaques) using biaxial extension results fit to a mixed Neo-Hookean and Exponential constitutive model. Results and discussion: The average circumferential Cauchy stress was 66.5, 130.2, and 300.4 kPa for regions with <15, 15-30, and >30% balloon under-expansion at a 1.15 stretch ratio. Similarly, the average longitudinal Cauchy stress was 68.1, 172.6, and 412.7 kPa, respectively. Consequently, strong correlation coefficients >0.89 were observed between balloon under-expansion and stress-like constitutive parameters. These parameters allowed for visualization of stiffness and material heterogeneity for a range of atherosclerotic plaques. Balloon under-expansion is a strong predictor of target lesion stiffness. These findings are promising as stent deployment could now be further personalized via target lesion material characterization obtained pre-operatively.

Local Characterization of Collagen Architecture and Mechanical Failure Properties of Fibrous Plaque Tissue of Atherosclerotic Human Carotid Arteries

Atherosclerotic plaque rupture in carotid arteries is a major cause of cerebrovascular events. Plaque rupture is the mechanical failure of the heterogeneous fibrous plaque tissue. Local characterization of the tissue's failure properties and the collagen architecture are of great importance to have insights in plaque rupture for clinical event prevention. Previous studies were limited to average rupture properties and global structural characterization, and did not provide the necessary local information. In this study, we assessed the local collagen architecture and failure properties of fibrous plaque tissue, by analyzing 30 tissue strips from 18 carotid plaques. Our study framework entailed second harmonic generation imaging for local collagen orientation and dispersion, and uniaxial tensile testing and digital image correlation for local tissue mechanics. The results showed that 87% of the imaged locations had collagen orientation close to the circumferential direction (0°) of the artery, and substantial dispersion locally. All regions combined, median [Q1:Q3] of the predominant angle measurements was -2° [-16°:16°]. The stretch ratio measurements clearly demonstrated a nonuniform stretch ratio distribution in the tissue under uniaxial loading. The rupture initiation regions had significantly higher stretch ratios (1.26 [1.15-1.40]) than the tissue average stretch ratio (1.11 [1.10-1.16]). No significant difference in collagen direction and dispersion was identified between the rupture regions and the rest of the tissue. The presented study forms an initial step towards gaining better insights into the characterization of local structural and mechanical fingerprints of fibrous plaque tissue in order to aid improved assessment of plaque rupture risk. STATEMENT OF SIGNIFICANCE: Plaque rupture risk assessment, critical to prevent cardiovascular events, requires knowledge on local failure properties and structure of collagenous plaque tissue. Our current knowledge is unfortunately limited to tissue's overall ultimate failure properties with scarce information on collagen architecture. In this study, local failure properties and collagen architecture of fibrous plaque tissue were obtained. We found predominant circumferential alignment of collagen fibers with substantial local dispersion. The tissue showed nonuniform stretch distribution under uniaxial tensile loading, with high stretches at rupture spots. This study highlights the significance of local mechanical and structural assessment for better insights into plaque rupture and the potential use of local stretches as risk marker for plaque rupture for patient-specific clinical applications.

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.

The substrate stiffness at physiological range significantly modulates vascular cell behavior

Changes in the stiffness of the cellular microenvironment are involved in many pathological processes of blood vessels. Substrate stiffness has been shown to have extensive effects on vascular endothelial cells (VECs) and vascular smooth muscle cells (VSMCs). However, the material stiffness of most previously reported in-vitro models is ranging from ~100 kPa to the magnitude of MPa, which does not match the mechanical properties of natural vascular tissue (10-100 kPa). Herein, we constructed hydrogel substrates with the stiffness of 18-86 kPa to explore the effect of physiological stiffness on vascular cells. Our findings show that, with the increase of stiffness at the physiological range, the cell adhesion and proliferation behaviors of VECs and VSMCs are significantly enhanced. On the soft substrate, VECs express more nitric oxide (NO), and VSMCs tend to maintain a healthy contraction phenotype. More importantly, we find that the number of differentially expressed genes in cells cultured between 18 kPa and 86 kPa substrates (560 in VECs, 243 in VSMCs) is significantly higher than that between 86 kPa and 333 kPa (137 in VECs, 172 in VSMCs), indicating that a small increase in stiffness within the physiological range have a higher impact on vascular cell behaviors. Overall, our results expanded the exploration of how stiffness affects the behavior of vascular cells at the physiological range.

Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases

Hemodynamic factors, induced by pulsatile blood flow, play a crucial role in vascular health and diseases, such as the initiation and progression of atherosclerosis. Computational fluid dynamics, finite element analysis, and fluid-structure interaction simulations have been widely used to quantify detailed hemodynamic forces based on vascular images commonly obtained from computed tomography angiography, magnetic resonance imaging, ultrasound, and optical coherence tomography. In this review, we focus on methods for obtaining accurate hemodynamic factors that regulate the structure and function of vascular endothelial and smooth muscle cells. We describe the multiple steps and recent advances in a typical patient-specific simulation pipeline, including medical imaging, image processing, spatial discretization to generate computational mesh, setting up boundary conditions and solver parameters, visualization and extraction of hemodynamic factors, and statistical analysis. These steps have not been standardized and thus have unavoidable uncertainties that should be thoroughly evaluated. We also discuss the recent development of combining patient-specific models with machine-learning methods to obtain hemodynamic factors faster and cheaper than conventional methods. These critical advances widen the use of biomechanical simulation tools in the research and potential personalized care of vascular diseases.