Session Z05: Cardiac and Cardiovascular Mechanics

Impact of body movement on the aortic valve hemodynamics, an ex-vivo experimental study

During intense physical activity, e.g., running, the human body raises cardiac output as a natural physiological response. Research concerning the human body's systemic response to elevated heart rate is well understood while little information exists about how intense movements of the human body physically impact the hemodynamics of the cardiac system. We hypothesize intense movements of the body will alter the hemodynamic interaction between the vessel wall and blood, a phenomenon that needs to be quantitatively evaluated. This study uses an ex-vivo experimental approach to quantify how movements of the human body impact the hemodynamics around the aortic valve beyond the typical physiological response. Utilizing a data collection system the team records the physical movement and cardiac response of a test subject performing physically intensive activities. The test subject data is then used to drive a six-degrees-of-freedom motion simulation platform for ex-vivo lab testing. An aortic valve model is placed on the motion simulation platform where a high-speed PIV camera system is used to measure changes in the hemodynamics when subjected to simulated physical activity. Two independent variables exist for laboratory testing: model inlet flow rate, and simulated movement. In the reference group, we measure the hemodynamics of the system while changing the inlet flow rate with the motion simulation platform at rest. In the test group, we record the system's hemodynamic response over the same variable inlet flow rates while also driving the motion simulation platform to mimic real-world conditions. Finally, we discuss differences in the hemodynamics between these two test groups.   

Evaluating the Effect of Subaortic Stenosis on Blood Fow Downstream of Bileaflet Mechanical Heart Valve Using Particle Image Velocimetry.

Concomitant left ventricular septal myectomy during aortic valve replacement is possible in the presence of congenital or acquired severe subaortic stenosis. However, subaortic stenosis may recur in some patients (recurrence rate up to 37%), so regular clinical evaluation is required. The hemodynamic effects of different types (discrete vs. tunnel) and shapes (symmetric vs. asymmetric) of subaortic stenosis on flow downstream of bileaflet mechanical heart valve were investigated in this study using particle image velocimetry and a cutting-edge cardiac simulator. Different 3D printed subaortic stenosis models were created and tested under physiological flow conditions for this purpose.

The study found that subaortic stenosis and its various types and shapes had a significant impact on flow patterns in terms of velocity, vorticity dynamics, and Reynolds shear stress. Also, leaflets’ kinematics were noticeably affected by the type, shape, and severity of subaortic stenosis.

 

    

Elucidating the effect of fibrosis on left atrial flow using multi-physics, multi-scale simulations

Atrial fibrillation (AFib) affects millions of people worldwide and increases the risk of stroke 5-fold, causing higher mortality and more disabilities than strokes in the normal population. During AFib, atria beat irregularly, creating stagnant flow regions where clots can form, typically in the left atrial (LA) appendage. However, the mechanisms by which fibrotic atria are more prone to thrombosis are yet not understood. Atrial motion impairment is caused by fibrosis through electrical, structural, and contractile effects. We use a multi-physics, multi-scale framework that couples electrophysiology, biomechanics, and hemodynamics to investigate the effect of fibrosis in atrial flow and ultimately in thrombosis. We model a LA contraction against a constant pressure in 4 patient-specific anatomies with different fibrotic burdens. In the fibrotic regions, we increase 5-fold the tissue passive stiffness (iPS) and reduce by half the peak tension (rPT) generated by cardiomyocytes. We found that the combined effect of iPS and rPT leads to smaller kinetic energy (KE) inside the left atrium in all cases due to a reduced LA wall motion. The independent effect of iPS and rPT also decreases KE, but their impact on the total contribution varies among patients. 

    

Inferring left atrial appendage (LAA) hemodynamics from 4D CT contrast dynamics by physics informed neural networks (PINNs)

Atrial fibrillation (AFib) is a common arrhythmia with a lifetime incidence of ~ 1 in 3 people that is associated with a 5X increase in ischemic stroke risk. During AFib, the atrial walls move weakly and irregularly causing blood stasis inside the LAA. Current clinical risk scores for stroke in AFib patients are based on demographic factors and have moderate accuracy. Here we present a computational tool for patient-specific clotting risk assessment based on 4D CT acquisitions of LAA contrast dynamics. We test PINNs with different underlying models (e.g., continuity only vs. Navier-Stokes and continuity), boundary conditions (e.g., temporally periodic flow vs. non-periodic flow), and input data with varying spatio-temporal resolutions. Our ground truth comprises CFD simulations in idealized, fixed-wall geometries as well as patient-specific, moving-wall left atrial meshes. We find that PINNs can accurately infer LAA hemodynamics using each patient's contrast agent concentration fields from CFD as training data with only a continuity underlying model, as long as temporal periodicity is imposed. Finally, we show proof of concept of the application of ROMs to infer LAA residence time using 4D CT data acquired in the clinical setting.   

Study of cardiac fluid dynamics in the right side of the heart with AI PIV

Particle Image Velocimetry (PIV), when used to investigate cardiac flows, has been restricted to the exploration of left heart hemodynamics. Easy phantom modeling, simple geometrical assumptions and the use of modified ultrahigh frequency 3D echocardiographic probes dedicated to the left heart are the main reason for this restriction. However, hemodynamics of the right heart, due to its complex geometry, is still poorly understood. AI PIV based on Deep Learning and Convolutional Neural Networks offers a super-resolution view of the velocity fields. In this paper, we applied this new technique to agitated saline bubble echocardiographic recordings of the right heart. The obtained higher-resolution results showed promising patterns and vortices throughout the cardiac cycle, circumventing the above-mentioned obstacles. For instance, the annular tricuspid excursion that has been used for decades as a marker of systolic function of the right ventricle, seems to be crucial for the formation of two diastolic vortices in the right atrium. A lateral counterclockwise vortex and a medial clockwise vortex that direct flow from the low-pressure right atrium to the middle of the tricuspid valve were noted repeatedly throughout different cardiac cycles within the same patients and between different patients.

    

Author not Attending

This research entails the study of the transfer and transport of a passive scalar around the aortic valve to aid in understanding Calcific Aortic Valve Disease (CAVD). Simulations were conducted using a novel interpolation-free sharp-interface immersed boundary method. The method is generic in nature, enabling imposing boundary conditions for scalar concentration to investigate CAVD. In this study, the 3D geometry of the native tricuspid AV including the cusps, commissures, and sinuses will be reconstructed based on the parametric model developed by Haj-Ali, et al. (2012) based on the AV anatomy and measurements reported in the literature. We will solve advection-diffusion transport equations to find the scalar transport, albeit in a Fluid-Structure Interaction (FSI) setting. The FSI framework will be based on the developed immersed boundary coupled with a solid solver (Calculix) using PRECICE. The results will be employed to evaluate the distribution of scalar concentration on leaflets as well as to understand the correlation between the level of concentration and valve movements. The correlation between the predicted scalar concentration and several WSS-based parameters (WSS, WSSG, OSI, GON, RRT) will be also investigated. 

    

Aortic Replacement valves Hemodynamics in regards to Blood Stasis and Residence time

The aortic valve hemodynamics are heavily influenced by several factors. We have previously showed how replacement valve sizing can play a large role in controlling residence time and blood stasis inside the sinus bulge of the aorta. This year we will present a continuation of this work and describe how other surgical factors can affect the residence time around the aortic valves. We will focus on the ratio of valve and sinus size, valve mounting geometry and valve eccentricity and on how these factors can increase blood stasis. These findings present a clinically relevant framework for understanding the flow caused by surgically implanted aortic valves.

    

Hemodynamics of infarcted hearts

Myocardial infarction occurs when a branch of a coronary artery is occluded by a thrombus (blood clot) and a certain area becomes ischemic. This results in a progressive deterioration of electrical and muscular activity of the injuried area leading to cell death and local failure of muscle contraction [1]. In this work, we employ a high-fidelity numerical heart model to study the hemodynamics after an ischemic event (heart attack). We aim at understanding why after apparently similar infarction events, some patients have a poor prognosis while others perfectly recover [2]. The analysis is based on the fluid-structure-electrophysiology interaction (FSEI2), which can cope with the electrophysiology of the myocardium, including the fibers orientation, its active contraction and passive relaxation, the dynamics of the valves and the hemodynamics within the heart chambers and arteries. All these models are three–way coupled with each other, thus providing a predictive framework for capturing both the healthy and pathologic heart functioning. In particular, the ischemic region perturbs the elastic and electrophysiology properties of the myocardium which, in turn, loses contractility. Importantly, multiple ischemic events will be considered by varying the position and size of the impaired myocardium, depending on which coronary branch gets blocked (e.g anterior, anterior–septal, lateral and inferior). For each case, the hemodynamics effectiveness in terms of transvalvular pressure drops, cardiac output, ventricle washout and wall shear stress will be measured in order to determine what are the sensitive parameters affecting the diseases evolution and detection.   

Optimal cardiac resynchronization therapy: an in-silico investigation using a whole-heart model

Cardiac resynchronization therapy (CRT) is indicated in patients with heart failure and consists of the implantation of a pacemaker to restore the coordination of ventricles contraction. However, in one-third of the patients CRT fails to improve clinical parameters owing to the sub-optimal position of the pacing leads, which can be improved by running dedicated clinical trials. However, the recruitment of patients is a bottleneck for clinical trials and digital twins of the human heart have recently been proposed as a viable alternative.  In this study, we rely on an unprecedented cardiovascular numerical model which is GPU–accelerated to replicate the full cardiac dynamics within a few hours. Specifically, cardiac dyssynchrony is induced by blocking the left bundle branch in the electrophysiology system, which yields a delay in the electrical activation of the ventricular myocardium and a reduced ejection fraction. A CRT is then implanted in-silico by mounting three leads: one in the right atrium, one in the right ventricle and another in the left ventricle. The optimal positioning of the last lead is determined by considering several pacing sites across the coronary venous system. The numerical results confirm the reliability of the method, thus moving towards an effective use of multiphysics modelling in digital medicine.    

Modeling High Residence Time Platelets in LVAD patients. Intraventricular Flow and Lagrangian Statistics in a Patient-Specific Model of a Deformable Ventricle.

A numerical model of LVAD-supported intraventricular flow is used to study the trajectory of platelets in the left ventricle(LV) of an advanced Heart Failure patient implanted with an LVAD. The anatomy and motion of the LV walls are measured from 4D Computed Tomography (CT). Ventricular motion is enforced to the computational model by assigned mesh deformation. Inflow and outflow through valves and LVAD cannula are applied as boundary conditions, balanced with the ventricular volume change. After transients have been eliminated (discarding 3 cycles), flow tracers were injected during multiple mitral fillings and tracked for more than 15 cardiac cycles. Lagrangian statistics reveal the dynamics of platelets in the failing LV with remaining contractability, assisted by an LVAD. The study identifies a small but crucial portion of extremely high residence time platelets in the left ventricle, despite continuous flow from LVAD (5 L/min), and the characteristics of the trajectories of these anomalous “pockets” of platelets that do not mix with the incoming mitral flow and do not evacuate the ventricle. These trajectories could shed light on the persistence of thrombogenic conditions in LVAD patients despite recent improvements in LVAD performance and reduction of in-pump thrombosis.