Prediction of Turbulent Shear Stresses through Dysfunctional Bileaflet Mechanical Heart Valves using Computational Fluid Dynamics

There are more than 300,000 heart valves implanted annually worldwide with about 50% of them being mechanical valves. The heart valve replacement is often a common treatment for severe valvular disease. However, valves may dysfunction leading to adverse hemodynamic conditions. The current computational study investigated the flow around a bileaflet mechanical heart valve at different leaflet dysfunction levels of 0%, 50%, and 100%, and documented the relevant flow characteristics such as vortical structures and turbulent shear stresses. Studying the flow characteristics through these valves during their normal operation and dysfunction can lead to better understanding of their performance, possibly improved designs, and help identify conditions that may increase the potential risk of blood cell damage. Results suggested that maximum flow velocities increased with dysfunction from 2.05 to 4.49 ms-1 which were accompanied by growing eddies and velocity fluctuations. These fluctuations led to higher turbulent shear stresses from 90 to 800 N.m-2 as dysfunctionality increased. These stress values exceeded the thresholds corresponding to elevated risk of hemolysis and platelet activation. The regions of elevated stresses were concentrated around and downstream of the functional leaflet where high jet velocity and stronger helical structures existed

Numerical Modeling of Pulse Wave Propagation in a Stenosed Artery using Two-Way Coupled Fluid Structure Interaction (FSI)

As the heart beats, it creates fluctuation in blood pressure leading to a pulse wave that propagates by displacing the arterial wall. These waves travel through the arterial tree and carry information about the medium that they propagate through as well as information of the geometry of the arterial tree. Pulse wave velocity (PWV) can be used as a non-invasive diagnostic tool to study the functioning of cardiovascular system. A stenosis in an artery can dampen the pulse wave leading to changes in the propagating pulse. Hence, PWV analysis can be performed to detect a stenosed region in arteries. This paper presents a numerical study of pulse wave propagation in a stenosed artery by means of two-way coupled fluid structure interaction (FSI). The computational model was validated by the comparison of the simulated PWV results with theoretical values for a healthy artery. Propagation of the pulse waves in the stenosed artery was compared with healthy case using spatiotemporal maps of wall displacements. The analysis for PWV showed significance differences between the healthy and stenosed arteries including damping of propagating waves and generation of high wall displacements downstream the stenosis caused by flow instabilities. This approach can be used to develop patient-specific models that are capable of predicting PWV signatures associated with stenosis changes. The knowledge gained from these models may increase utility of this approach for managing patients at risk of stenosis occurrence

The Influence of the Aortic Root Geometry on Flow Characteristics of a Bileaflet Mechanical Heart Valve

Bileaflet mechanical heart valves have one of the most successful valve designs for more than 30 years. These valves are often used for aortic valve replacement, where the geometry of the aortic root sinuses may vary due to valvular disease and affect valve performance. Common geometrical sinus changes may be due to valve stenosis and insufficiency. In the current study, the effect of these geometrical changes on the mean flow and velocity fluctuations downstream of the valve and aortic sinuses were investigated. The study focused on the fully-open leaflet position where blood velocities are close to their maximum. Simulation results were validated using previous experimental laser Doppler anemometry (LDA) measurements. Results showed that as the stenosis and insufficiency increased there were more flow separation and increased local mean velocity downstream of the leaflets. In addition, the detected elevated velocity fluctuations were associated with higher Reynolds shear stresses levels, which may increase the chances of blood damage and platelet activation and may lead to increased risk of blood clot formation

Finite Element-Based Machine Learning Model for Predicting the Mechanical Properties of Composite Hydrogels

In this study, a finite element (FE)-based machine learning model was developed to predict the mechanical properties of bioglass (BG)-collagen (COL) composite hydrogels. Based on the experimental observation of BG-COL composite hydrogels with scanning electron microscope, 2000 microstructural images with randomly distributed BG particles were created. The BG particles have diameters ranging from 0.5 μm to 1.5 μm and a volume fraction from 17% to 59%. FE simulations of tensile testing were performed for calculating the Young’s modulus and Poisson’s ratio of 2000 microstructures. The microstructural images and the calculated Young’s modulus and Poisson’s ratio by FE simulation were used for training and testing a convolutional neural network regression model. Results showed that the network developed in this work can effectively predict the mechanical properties of the composite hydrogels. The R-squared values were 95% and 83% for Young’s modulus and Poisson’s ratio, respectively. This work provides a surrogate model of finite element analysis to predict mechanical properties of BG-COL hydrogel using microstructure images, which could be further utilized for characterizing heterogeneous materials in big data-driven material designs

Geometric Features Of Pig Airways Using Computed Tomography

Accurate knowledge of the airway geometry is needed when constructing physical models of the airway tree and for numerical modeling of flow or sound propagation in the airways. Human and animal experiments are conducted to validate these models. Many studies documented the geometric details of the human airways. However, information about the geometry of pig airways is scarcer. Earlier studies suggested that the morphology of animal airways can be significantly different from that of humans. The objective of this study is to measure the airway diameter, length and bifurcation angles in domestic pigs using computed tomography. In this study, lungs of six pigs were imaged, then segmentation software tools were used to extract the geometry of the airway lumen. The airway dimensions were measured from the resulting 3-D models for the first 24 airway generations. Results showed that the size and morphology of the airways of the six pigs were similar. The trachea diameters were found to be comparable to the typical human adult, but the diameter, length and branching angles of other airways were noticeably different from that of humans. For example, pig airways consistently had an early branching from the trachea that feeds the top right lung lobe and precedes the main carina. This branch is absent in the human airways. The results suggested that the pig airways geometry may not be accurately approximated by human airways and this approximation may contribute to increasing the errors in computational models of the pig chest

Adverse Hemodynamic Conditions Associated with Mechanical Heart Valve Leaflet Immobility

Artificial heart valves may dysfunction, leading to thrombus and/or pannus formations. Computational fluid dynamics is a promising tool for improved understanding of heart valve hemodynamics that quantify detailed flow velocities and turbulent stresses to complement Doppler measurements. This combined information can assist in choosing optimal prosthesis for individual patients, aiding in the development of improved valve designs, and illuminating subtle changes to help guide more timely early intervention of valve dysfunction. In this computational study, flow characteristics around a bileaflet mechanical heart valve were investigated. The study focused on the hemodynamic effects of leaflet immobility, specifically, where one leaflet does not fully open. Results showed that leaflet immobility increased the principal turbulent stresses (up to 400%), and increased forces and moments on both leaflets (up to 600% and 4000%, respectively). These unfavorable conditions elevate the risk of blood cell damage and platelet activation, which are known to cascade to more severe leaflet dysfunction. Leaflet immobility appeared to cause maximal velocity within the lateral orifices. This points to the possible importance of measuring maximal velocity at the lateral orifices by Doppler ultrasound (in addition to the central orifice, which is current practice) to determine accurate pressure gradients as markers of valve dysfunction

Upper Airway Flow Dynamics in Obstructive Sleep Apnea Patients with Various Apnea-Hypopnea Index

Background and aim: This study evaluates the upper airway flow characteristics, anatomical features and analyzes their correlations with AHI in patients with varied degrees of OSA severity seeking for discernments of the underlying pathophysiological profile. Materials and Methods: Patient-specific computational fluid dynamics models were reconstructed from high-resolution cone-beam computed tomography images for 4 OSA patients classified as minimal, mild, moderate, and severe according to AHI. Results: The parameters, minimal cross-sectional area (MCA), and the pharyngeal airway volume did not show clear correlations with the OSA severity defined according to AHI. No correlations were found between the classically defined resistance of the airway in terms of pressure drop and AHI. The flow analysis further showed that the fluid mechanisms likely to cause airway collapse are associated with the degree of narrowing in the pharyngeal airway rather than AHI. Results also suggested that some patients classified as severe OSA according to the AHI can show less susceptibility to airway collapse than patients with relatively lower AHI values and vice versa. Conclusions: The relative contribution of anatomical and non-anatomical causes to the OSA severity can significantly vary between patients. AHI alone is inadequate to be used as a marker of the pathophysiological profile of OSA. Combining airflow analysis with AHI in diagnosing OSA severity may provide additional details about the underlying pathophysiology, subsequently improving the individualized clinical outcomes

Finite Element-Based Machine Learning Model for Predicting the Mechanical Properties of Composite Hydrogels

In this study, a finite element (FE)-based machine learning model was developed to predict the mechanical properties of bioglass (BG)-collagen (COL) composite hydrogels. Based on the experimental observation of BG-COL composite hydrogels with scanning electron microscope, 2000 microstructural images with randomly distributed BG particles were created. The BG particles have diameters ranging from 0.5 µm to 1.5 µm and a volume fraction from 17% to 59%. FE simulations of tensile testing were performed for calculating the Young’s modulus and Poisson’s ratio of 2000 microstructures. The microstructural images and the calculated Young’s modulus and Poisson’s ratio by FE simulation were used for training and testing a convolutional neural network regression model. Results showed that the network developed in this work can effectively predict the mechanical properties of the composite hydrogels. The R-squared values were 95% and 83% for Young’s modulus and Poisson’s ratio, respectively. This work provides a surrogate model of finite element analysis to predict mechanical properties of BG-COL hydrogel using microstructure images, which could be further utilized for characterizing heterogeneous materials in big data-driven material designs