Surgical Aortic Valve Replacement: Are We Able to Improve Hemodynamic Outcome?

The dynamics of red blood cells traversing slits of mechanical heart valves under high shear

Hemolysis, including subclinical hemolysis, is a potentially severe complications of mechanical heart valves (MHVs), which leads to shortened red blood cell (RBC) lifespan and hemolytic anemia. Serious hemolysis is usually associated with structural deterioration and regurgitation. However, the shear stress in MHVs' narrow leakage slits is much lower than the shear stress threshold causing hemolysis and the mechanisms in this context remain largely unclear. This study investigated the hemolysis mechanism of RBCs in cell-size slits under high shear rates by establishing in vitro microfluidic devices and a coarse-grained molecular dynamics (CGMD) model, considering both fluid and structural effects simultaneously. Microfluidic experiments and computational simulation revealed six distinct dynamic states of RBC traversal through MHVs' microscale slits under various shear rates and slit sizes. It elucidated that RBC dynamic states were influenced by not only by fluid forces but significantly by the compressive force of slit walls. The variation of the potential energy of the cell membrane indicated its stretching, deformation, and rupture during traversal, corresponding to the six dynamic states. The maximum forces exerted on membrane by water particles and slit walls directly determined membrane rupture, serving as a critical determinant. This analysis helps in understanding the contribution of the slit walls to membrane rupture and identifying the threshold force that leads to membrane rupture. The hemolysis mechanism of traversing microscale slits is revealed to effectively explain the occurrences of hemolysis and subclinical hemolysis.

A hemodynamic study of the relationship between the left and right liver volumes and the blood flow distribution in portal vein branches

BACKGROUND

Cirrhosis patients often exhibit clinical symptoms such as right liver atrophy, portal hypertension, spleen enlargement and increased blood supply, which exhibit considerable variation between the left and right liver sections. These differences are hypothesized to stem from disparities in blood flow within the left and right portal vein (PV) branches. However, rigorous quantitative evidence remains scarce.

PURPOSE

We mainly aim at quantitatively revealing the relationship between the blood flow rates of two PV branches and liver volumes, and providing quantitative evidence and theoretical support for the diagnosis and treatment of cirrhosis from the perspective of hemodynamics.

METHODS

Five cirrhotic patients and two healthy volunteers from Beijing Friendship Hospital are investigated. Their PV blood flow models are established based on computed tomography (CT) images and finite volume simulations. The volume of the left and right liver lobes are measured in 3-matic. The distributions of blood source in the PV branches are tracked by streamline analysis. The blood flow rates are quantitatively counted by integrating the blood source velocities. Linear analysis is performed to build the relationship between liver volumes and PV blood flow distributions.

RESULTS

Streamline analysis reveals significant differences in blood distribution between the left and right PV branches. The majority of blood from the superior mesenteric vein (SMV) flowed into the right portal vein (RPV), while most blood from the splenic vein (SV) entered the left portal vein (LPV). The main PV pressure drop linearly increases with the SV blood velocity for all PV structures of patients and healthy volunteers. The flow rate ratio QRPV/QLPV demonstrates an increase in tandem with the volume ratio VR/VL, exhibiting a linear correlation with the Pearson correlation coefficient being 0.93.

CONCLUSION

The differences in the blood distributions are consistent with the clinicians' knowledge and validate our simulations. We demonstrated a linear increase in PV pressure with elevated SV blood velocity. Additionally, the volumes of the left and right hepatic lobes exhibited a positive correlation with blood flow rates in the corresponding PV branches.

A Fibrin-Thrombin Based In Vitro Perfusion System to Study Flow-Related Prosthetic Heart Valves Thrombosis

Prosthetic heart valve (PHV) replacement has increased the survival rate and quality of life for heart valve-diseased patients. However, PHV thrombosis remains a critical problem associated with these procedures. To better understand the PHV flow-related thrombosis problem, appropriate experimental models need to be developed. In this study, we present an in vitro fibrin clot model that mimics clot accumulation in PHVs under relevant hydrodynamic conditions while allowing real-time imaging. We created 3D-printed mechanical aortic valve models that were inserted into a transparent glass aorta model and connected to a system that simulates human aortic flow pulse and pressures. Thrombin was gradually injected into a circulating fibrinogen solution to induce fibrin clot formation, and clot accumulation was quantified via image analysis. The results of valves positioned in a normal versus a tilted configuration showed that clot accumulation correlated with the local flow features and was mainly present in areas of low shear and high residence time, where recirculating flows are dominant, as supported by computational fluid dynamic simulations. Overall, our work suggests that the developed method may provide data on flow-related clot accumulation in PHVs and may contribute to exploring new approaches and valve designs to reduce valve thrombosis.

A systematic review of cardiac in-silico clinical trials

Computational models of the heart are now being used to assess the effectiveness and feasibility of interventions through in-silico clinical trials (ISCTs). As the adoption and acceptance of ISCTs increases, best practices for reporting the methodology and analysing the results will emerge. Focusing in the area of cardiology, we aim to evaluate the types of ISCTs, their analysis methods and their reporting standards. To this end, we conducted a systematic review of cardiac ISCTs over the period of 1 January 2012-1 January 2022, following the preferred reporting items for systematic reviews and meta-analysis (PRISMA). We considered cardiac ISCTs of human patient cohorts, and excluded studies of single individuals and those in which models were used to guide a procedure without comparing against a control group. We identified 36 publications that described cardiac ISCTs, with most of the studies coming from the US and the UK. In 75% of the studies, a validation step was performed, although the specific type of validation varied between the studies. ANSYS FLUENT was the most commonly used software in 19% of ISCTs. The specific software used was not reported in 14% of the studies. Unlike clinical trials, we found a lack of consistent reporting of patient demographics, with 28% of the studies not reporting them. Uncertainty quantification was limited, with sensitivity analysis performed in only 19% of the studies. In 97% of the ISCTs, no link was provided to provide easy access to the data or models used in the study. There was no consistent naming of study types with a wide range of studies that could potentially be considered ISCTs. There is a clear need for community agreement on minimal reporting standards on patient demographics, accepted standards for ISCT cohort quality control, uncertainty quantification, and increased model and data sharing.

Modelling blood flow in patients with heart valve disease using deep learning: A computationally efficient method to expand diagnostic capabilities in clinical routine

Introduction

The computational modelling of blood flow is known to provide vital hemodynamic parameters for diagnosis and treatment-support for patients with valvular heart disease. However, most diagnosis/treatment-support solutions based on flow modelling proposed utilize time- and resource-intensive computational fluid dynamics (CFD) and are therefore difficult to implement into clinical practice. In contrast, deep learning (DL) algorithms provide results quickly with little need for computational power. Thus, modelling blood flow with DL instead of CFD may substantially enhances the usability of flow modelling-based diagnosis/treatment support in clinical routine. In this study, we propose a DL-based approach to compute pressure and wall-shear-stress (WSS) in the aorta and aortic valve of patients with aortic stenosis (AS).

Methods

A total of 103 individual surface models of the aorta and aortic valve were constructed from computed tomography data of AS patients. Based on these surface models, a total of 267 patient-specific, steady-state CFD simulations of aortic flow under various flow rates were performed. Using this simulation data, an artificial neural network (ANN) was trained to compute spatially resolved pressure and WSS using a centerline-based representation. An unseen test subset of 23 cases was used to compare both methods.

Results

ANN and CFD-based computations agreed well with a median relative difference between both methods of 6.0% for pressure and 4.9% for wall-shear-stress. Demonstrating the ability of DL to compute clinically relevant hemodynamic parameters for AS patients, this work presents a possible solution to facilitate the introduction of modelling-based treatment support into clinical practice.

Numerical simulation of hemodynamics in patient-specific pulmonary artery stenosis

BACKGROUND

The incidence rate of pulmonary artery stenosis is increasing year by year and its numerical simulation has become a key project of biomedical engineering.

OBJECTIVE

The purpose of this work is to study the changes of hemodynamic parameters in patient-specific pulmonary artery stenosis.

METHODS

A pulmonary artery stenosis model is established based on patient-specific computed tomography (CT) images. According to the actual anatomy of patient-specific pulmonary artery stenosis, the stenosis area is simulated using a porous medium to study its hemodynamic changes. The computational fluid dynamics (CFD) method is used to simulate the hemodynamic changes of pulmonary artery stenosis, and to explore the mechanical characteristics between blood flow and vessel wall.

RESULTS

The results suggest that the blood pressures of arterial branches increase and the pressure drop at both ends of the stenosis is higher. There is a high flow rate and wall shear stress at the stenosis.

CONCLUSION

This study shows that the hemodynamic model of pulmonary artery stenosis can be accurately reconstructed by achieving numerical simulation of the local stenosis through CT images, and this work has important implications for improving the confidence of clinical diagnosis and treatment of pulmonary artery diseases.

Hemodynamic Parameters for Cardiovascular System in 4D Flow MRI: Mathematical Definition and Clinical Applications

Blood flow imaging becomes an emerging trend in cardiology with the recent progress in computer technology. It not only visualizes colorful flow velocity streamlines but also quantifies the mechanical stress on cardiovascular structures; thus, it can provide the detailed inspections of the pathophysiology of diseases and predict the prognosis of cardiovascular functions. Clinical applications include the comprehensive assessment of hemodynamics and cardiac functions in echocardiography vector flow mapping (VFM), 4D flow MRI, and surgical planning as a simulation medicine in computational fluid dynamics (CFD).For evaluation of the hemodynamics, novel mathematically derived parameters obtained using measured velocity distributions are essential. Among them, the traditional and typical parameters are wall shear stress (WSS) and its related parameters. These parameters indicate the mechanical damages to endothelial cells, resulting in degenerative intimal change in vascular diseases. Apart from WSS, there are abundant parameters that describe the strength of the vortical and/or helical flow patterns. For instance, vorticity, enstrophy, and circulation indicate the rotating flow strength or power of 2D vortical flows. In addition, helicity, which is defined as the cross-linking number of the vortex filaments, indicates the 3D helical flow strength and adequately describes the turbulent flow in the aortic root in cases with complicated anatomies. For the description of turbulence caused by the diseased flow, there exist two types of parameters based on completely different concepts, namely: energy loss (EL) and turbulent kinetic energy (TKE). EL is the dissipated energy with blood viscosity and evaluates the cardiac workload related to the prognosis of heart failure. TKE describes the fluctuation in kinetic energy during turbulence, which describes the severity of the diseases that cause jet flow. These parameters are based on intuitive and clear physiological concepts, and are suitable for in vivo flow measurements using inner velocity profiles.

Deep Learning Based Centerline-Aggregated Aortic Hemodynamics: An Efficient Alternative to Numerical Modelling of Hemodynamics

Image-based patient-specific modelling of hemodynamics are gaining increased popularity as a diagnosis and outcome prediction solution for a variety of cardiovascular diseases. While their potential to improve diagnostic capabilities and thereby clinical outcome is widely recognized, these methods require considerable computational resources since they are mostly based on conventional numerical methods such as computational fluid dynamics (CFD). As an alternative to the numerical methods, we propose a machine learning (ML) based approach to calculate patient-specific hemodynamic parameters. Compared to CFD based methods, our approach holds the benefit of being able to calculate a patient-specific hemodynamic outcome instantly with little need for computational power. In this proof-of-concept study, we present a deep artificial neural network (ANN) capable of computing hemodynamics for patients with aortic coarctation in a centerline aggregated (i.e. locally averaged) form. Considering the complex relation between vessels shape and hemodynamics on the one hand and the limited availability of suitable clinical data on the other, a sufficient accuracy of the ANN may however not be achieved with available data only. Another key aspect of this study is therefore the successful augmentation of available clinical data. Using a statistical shape model, additional training data was generated which substantially increased the ANNs accuracy, showcasing the ability of ML based methods to perform in-silico modelling tasks previously requiring resource intensive CFD simulations.

Patient-specific fluid-structure interaction model of bile flow: comparison between 1-way and 2-way algorithms

Gallbladder disease is one of the most spread pathologies in the world. Despite the number of operations dealing with biliary surgery increases, the number of postoperative complications is also high. The aim of this study is to show the influence of the biliary system pathology on bile flow character and to numerically assess the effect of surgical operation (cholecystectomy) on the fluid dynamics in the extrahepatic biliary tree, and also to reveal the difference between 1-way and 2-way FSI algorithms on the results. Moreover, the bile viscosity and biliary tree geometry influence on the choledynamics were evaluated. Bile velocity, pressure, wall shear stress (WSS), displacements and von Mises stress distributions in the extrahepatic biliary tree are presented, and comparison is made between a healthy and a lithogenic bile. The patient-specific biliary tree model is created using magnetic resonance imaging (MRI) and imported in a commercial finite element analysis software. It is found that in the case of lithogenic bile, velocities have lower magnitude while pressures are higher. Furthermore, stress analysis of the bile ducts shows that the WSS distribution is found mostly prevailing in the common hepatic duct and common bile duct areas. It is shown that when it is necessary to evaluate the bile flow dynamics in urgent medical situations, 1-way analysis is acceptable. Nevertheless, 2-way FSI provides more accurate data, if necessary to evaluate the stress-strain state of bile ducts. The proposed model can be applied to medical practice to reduce the number of post-operative complications.

Using position-based dynamics to simulate deformation in aortic valve replacement procedure

We present a novel approach to simulate deformation in aortic valve replacement scenarios with applications in operation planning and batch domain creation for large computational fluid dynamics studies of the aortic arch.