Heart blood flow simulation: a perspective review

A numerical study on the siphonic effect of enhanced external counterpulsation at lower extremities with a coupled 0D-1D closed-loop personalized hemodynamics model

Enhanced external counterpulsation (EECP) is a treatment and rehabilitation approach for ischemic diseases, including coronary artery disease. Its therapeutic benefits are primarily attributed to the improved blood circulation achieved through sequential mechanical compression of the lower extremities. However, despite the crucial role that hemodynamic effects in the lower extremity arteries play in determining the effectiveness of EECP treatment, most studies have focused on the diastole phase and ignored the systolic phase. In the present study, a novel siphon model (SM) was developed to investigate the interdependence of several hemodynamic parameters, including pulse wave velocity, femoral flow rate, the operation pressure of cuffs, and the mean blood flow changes in the femoral artery throughout EECP therapy. To verify the accuracy of the SM, we coupled the predicted afterload in the lower extremity arteries during deflation using SM with the 0D-1D patient-specific model. Finally, the simulation results were compared with clinical measurements obtained during EECP therapy to verify the applicability and accuracy of the SM, as well as the coupling method. The precision and reliability of the previously developed personalized approach were further affirmed in this study. The average waveform similarity coefficient between the simulation results and the clinical measurements during the rest state exceeded 90%. This work has the potential to enhance our understanding of the hemodynamic mechanisms involved in EECP treatment and provide valuable insights for clinical decision-making.

Current progress toward isogeometric modeling of the heart biophysics

In this paper, we review a powerful methodology to solve complex numerical simulations, known as isogeometric analysis, with a focus on applications to the biophysical modeling of the heart. We focus on the hemodynamics, modeling of the valves, cardiac tissue mechanics, and on the simulation of medical devices and treatments. For every topic, we provide an overview of the methods employed to solve the specific numerical issue entailed by the simulation. We try to cover the complete process, starting from the creation of the geometrical model up to the analysis and post-processing, highlighting the advantages and disadvantages of the methodology.

A review of feto-placental vasculature flow modelling

The placenta provides the vital nutrients and removal of waste products required for fetal growth and development. Understanding and quantifying the differences in structure and function between a normally functioning placenta compared to an abnormal placenta is vital to provide insights into the aetiology and treatment options for fetal growth restriction and other placental disorders. Computational modelling of blood flow in the placenta allows a new understanding of the placental circulation to be obtained. This structured review discusses multiple recent methods for placental vascular model development including analysis of the appearance of the placental vasculature and how placental haemodynamics may be simulated at multiple length scales.

Energetics of Cardiac Blood Flow in Hypertrophic Cardiomyopathy through Individualized Computational Modeling

Hypertrophic cardiomyopathy (HCM) is a congenital heart disease characterized by thickening of the heart's left ventricle (LV) wall that can lead to cardiac dysfunction and heart failure. Ventricular wall thickening affects the motion of cardiac walls and blood flow within the heart. Because abnormal cardiac blood flow in turn could lead to detrimental remodeling of heart walls, aberrant ventricular flow patterns could exacerbate HCM progression. How blood flow patterns are affected by hypertrophy and inter-patient variability is not known. To address this gap in knowledge, we present here strategies to generate personalized computational fluid dynamics (CFD) models of the heart LV from patient cardiac magnetic resonance (cMR) images. We performed simulations of CFD LV models from three cases (one normal, two HCM). CFD computations solved for blood flow velocities, from which flow patterns and the energetics of flow within the LV were quantified. We found that, compared to a normal heart, HCM hearts exhibit anomalous flow patterns and a mismatch in the timing of energy transfer from the LV wall to blood flow, as well as changes in kinetic energy flow patterns. While our results are preliminary, our presented methodology holds promise for in-depth analysis of HCM patient hemodynamics in clinical practice.

Intra-Cardiac Flow from Geometry Prescribed Computational Fluid Dynamics: Comparison with Ultrasound Vector Flow Imaging

PURPOSE

This paper investigates the accuracy of blood flow velocities simulated from a geometry prescribed computational fluid dynamics (CFD) pipeline by applying it to a dynamic heart phantom. The CFD flow patterns are compared to a direct flow measurement by ultrasound vector flow imaging (VFI). The hypothesis is that the simulated velocity magnitudes are within one standard deviation of the measured velocities.

METHODS

The CFD pipeline uses computed tomography angiography (CTA) images with 20 volumes per cardiac cycle as geometry input. Fluid domain movement is prescribed from volumetric image registration using the CTA image data. Inlet and outlet conditions are defined by the experimental setup. VFI is systematically measured in parallel planes, and compared to the corresponding planes in the simulated time dependent three dimensional fluid velocity field.

RESULTS

The measured VFI and simulated CFD have similar flow patterns when compared qualitatively. A quantitative comparison of the velocity magnitude is also performed at specific regions of interest. These are evaluated at 11 non-overlapping time bins and compared by linear regression giving R2 = 0.809, SD = 0.060 m/s, intercept = - 0.039 m/s, and slope = 1.09. Excluding an outlier at the inlet, the correspondence between CFD and VFI improves to: R2 = 0.823, SD = 0.048 m/s, intercept = -0.030 m/s, and slope = 1.01.

CONCLUSION

The direct comparison of flow patterns shows that the proposed CFD pipeline provide realistic flow patterns in a well-controlled experimental setup. The demanded accuracy is obtained close to the inlet and outlet, but not in locations far from these.

CFD Study of the Effect of the Angle Pattern on Iliac Vein Compression Syndrome

Iliac vein compression syndrome (IVCS, or May-Thurner syndrome) occurs due to the compression of the left common iliac vein between the lumbar spine and right common iliac artery. Because most patients with compression are asymptomatic, the syndrome is difficult to diagnose based on the degree of anatomical compression. In this study, we investigated how the tilt angle of the left common iliac vein affects the flow patterns in the compressed blood vessel using three-dimensional computational fluid dynamic (CFD) simulations to determine the flow fields generated after compression sites. A patient-specific iliac venous CFD model was created to verify the boundary conditions and hemodynamic parameter set in this study. Thirty-one patient-specific CFD models with various iliac venous angles were developed using computed tomography (CT) angiograms. The angles between the right or left common iliac vein and inferior vena cava at the confluence level of the common iliac vein were defined as α1 and α2. Flow fields and vortex locations after compression were calculated and compared according to the tilt angle of the veins. Our results showed that α2 affected the incidence of flow field disturbance. At α2 angles greater than 60 degrees, the incidence rate of blood flow disturbance was 90%. In addition, when α2 and α1 + α2 angles were used as indicators, significant differences in tilt angle were found between veins with laminar, transitional, and turbulent flow (p < 0.05). Using this mathematical simulation, we concluded that the tilt angle of the left common iliac vein can be used as an auxiliary indicator to determine IVCS and its severity, and as a reference for clinical decision making.

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.

Current state of the art in hypoplastic left heart syndrome

Hypoplastic left heart syndrome (HLHS) is a complex congenital heart condition in which a neonate is born with an underdeveloped left ventricle and associated structures. Without palliative interventions, HLHS is fatal. Treatment typically includes medical management at the time of birth to maintain patency of the ductus arteriosus, followed by three palliative procedures: most commonly the Norwood procedure, bidirectional cavopulmonary shunt, and Fontan procedures. With recent advances in surgical management of HLHS patients, high survival rates are now obtained at tertiary treatment centers, though adverse neurodevelopmental outcomes remain a clinical challenge. While surgical management remains the standard of care for HLHS patients, innovative treatment strategies continue to be developing. Important for the development of new strategies for HLHS patients is an understanding of the genetic basis of this condition. Another investigational strategy being developed for HLHS patients is the injection of stem cells within the myocardium of the right ventricle. Recent innovations in tissue engineering and regenerative medicine promise to provide important tools to both understand the underlying basis of HLHS as well as provide new therapeutic strategies. In this review article, we provide an overview of HLHS, starting with a historical description and progressing through a discussion of the genetics, surgical management, post-surgical outcomes, stem cell therapy, hemodynamics and tissue engineering approaches.

Risk factors for in-hospital systemic thromboembolism in myocardial infarction patients with left-ventricular thrombus: A multicenter retrospective study

Left-ventricular thrombus (LVT) is a potentially life-threatening disease. However, few studies have explored the risk factors of in-hospital systemic thromboembolism (ST) in LVT patients. In this multicenter retrospective study, we enrolled myocardial infarction patients with LVT from January 2008 to September 2021. Multivariable logistic regression analysis was applied to identify the independent risk factors for ST in LVT patients. A total number of 160 hospitalized LVT patients [median follow-up period 50 months (18.3-82.5 months)] were subjected to analysis. Of them, 54 (33.8%) patients developed acute myocardial infarction, 16 (10%) had ST, and 33 (20.6%) died. Comparable baseline characteristics were established between the ST and non-ST groups, except for the heart failure classification (P = .014). We obtained the following results from our multivariable analysis, based on the use of HFrEF as a reference: HFpEF [odd ratio (OR), 6.2; 95% confidence interval (CI), 1.4-26.3; P = .014] and HFmrEF (OR, 5.0; 95%CI, 1.1-22.2; P = .033). In conclusion, HFpEF, and HFmrEF may be independent risk factors for in-hospital ST development.

Computational cardiac physiology for new modelers: Origins, foundations, and future

Mathematical models of the cardiovascular system have come a long way since they were first introduced in the early 19th century. Driven by a rapid development of experimental techniques, numerical methods, and computer hardware, detailed models that describe physical scales from the molecular level up to organs and organ systems have been derived and used for physiological research. Mathematical and computational models can be seen as condensed and quantitative formulations of extensive physiological knowledge and are used for formulating and testing hypotheses, interpreting and directing experimental research, and have contributed substantially to our understanding of cardiovascular physiology. However, in spite of the strengths of mathematics to precisely describe complex relationships and the obvious need for the mathematical and computational models to be informed by experimental data, there still exist considerable barriers between experimental and computational physiological research. In this review, we present a historical overview of the development of mathematical and computational models in cardiovascular physiology, including the current state of the art. We further argue why a tighter integration is needed between experimental and computational scientists in physiology, and point out important obstacles and challenges that must be overcome in order to fully realize the synergy of experimental and computational physiological research.

A time-dependent offset field approach to simulating realistic interactions between beating hearts and surgical devices in virtual interventional radiology

Endovascular interventional radiology (IR) is a minimally invasive procedure for the treatment of vascular diseases. This procedure requires physicians to be highly skilled at manipulating interventional devices under the guidance of two-dimensional X-ray imaging. By offering a non-error-sensitive and radiation-free environment, a virtual reality-based simulator provides a promising alternative for surgical skills training and surgery planning. Building a realistic and interactive simulator is a challenging task. To achieve better realism, this paper proposes a novel method of simulating the heartbeat for both standard and patient-specific anatomical data. A time-dependent offset field approach is proposed to efficiently and stably simulate the interactive behavior between the dynamic heart mesh and surgical devices. For medical imaging simulation, we propose a GPU-based linear depth subtraction method to approximate fluoroscopic images based on the attenuation of the X-ray. On this basis, a topology-based flow map method is proposed to simulate the propagation of the contrast medium in angiography. Experimental results show that the proposed algorithm can simulate heartbeat stably for meshes with varying geometrical shapes and complexities. In efficiency, the dynamic heart mesh can interact with surgical devices stably at 60 frames/s. Under the simulated fluoroscopic imaging effect, the injected contrast medium can realistically visualize both dynamic and static vessels. In a face validity by medical students and clinicians, the category of effectiveness score 8.35 out of 10 on average, demonstrating that our simulator is useful in surgical skills training and surgery planning.

CT-Based Analysis of Left Ventricular Hemodynamics Using Statistical Shape Modeling and Computational Fluid Dynamics

Background

Cardiac computed tomography (CCT) based computational fluid dynamics (CFD) allows to assess intracardiac flow features, which are hypothesized as an early predictor for heart diseases and may support treatment decisions. However, the understanding of intracardiac flow is challenging due to high variability in heart shapes and contractility. Using statistical shape modeling (SSM) in combination with CFD facilitates an intracardiac flow analysis. The aim of this study is to prove the usability of a new approach to describe various cohorts.

Materials and Methods

CCT data of 125 patients (mean age: 60.6 ± 10.0 years, 16.8% woman) were used to generate SSMs representing aneurysmatic and non-aneurysmatic left ventricles (LVs). Using SSMs, seven group-averaged LV shapes and contraction fields were generated: four representing patients with and without aneurysms and with mild or severe mitral regurgitation (MR), and three distinguishing aneurysmatic patients with true, intermediate aneurysms, and globally hypokinetic LVs. End-diastolic LV volumes of the groups varied between 258 and 347 ml, whereas ejection fractions varied between 21 and 26%. MR degrees varied from 1.0 to 2.5. Prescribed motion CFD was used to simulate intracardiac flow, which was analyzed regarding large-scale flow features, kinetic energy, washout, and pressure gradients.

Results

SSMs of aneurysmatic and non-aneurysmatic LVs were generated. Differences in shapes and contractility were found in the first three shape modes. Ninety percent of the cumulative shape variance is described with approximately 30 modes. A comparison of hemodynamics between all groups found shape-, contractility- and MR-dependent differences. Disturbed blood washout in the apex region was found in the aneurysmatic cases. With increasing MR, the diastolic jet becomes less coherent, whereas energy dissipation increases by decreasing kinetic energy. The poorest blood washout was found for the globally hypokinetic group, whereas the weakest blood washout in the apex region was found for the true aneurysm group.

Conclusion

The proposed CCT-based analysis of hemodynamics combining CFD with SSM seems promising to facilitate the analysis of intracardiac flow, thus increasing the value of CCT for diagnostic and treatment decisions. With further enhancement of the computational approach, the methodology has the potential to be embedded in clinical routine workflows and support clinicians.

Co-simulation of hypertensive left ventricle based on computational fluid dynamics and a closed-loop network model

OBJECTIVE

Hypertension is one of the most common chronic and cardiovascular diseases, with the largest number of deaths. According to clinical experience, long-term hypertension will cause cardiac hypertrophy and other complications, and heart structure remodeling will significantly change the energy characteristics of the heart chambers, and impair heart function. Research shows that, early hypertension can be diagnosed by the blood flow and energy loss in the left ventricle. Therefore, it is important to choose an appropriate method to simulate and predict the flow domain of this ventricle.

METHODS

This study took the left ventricular flow field of patients with hypertensive myocardial hypertrophy as the research object, used MATLAB-SIMULINK to establish a closed-loop network cardiovascular model, provided flow boundary conditions for the computational fluid dynamics (CFD) numerical simulation method, and, finally, completed a co-simulation.

RESULTS

This article compared the degree of agreement between the energy loss in different phases of the heart cavity and clinical experimental data and summarized the characteristics of the flow field in patients with hypertensive myocardial hypertrophy. The analysis of three simulation groups (control group, non-left ventricular hypertrophy group, and left ventricular hypertrophy [LVH] group) showed that the vortices in the LVH group were irregular and not fully developed, accompanied by significant energy loss.

CONCLUSION

The simulation method used in this study is basically consistent with the clinical data. Myocardial hypertrophy has a significant influence on the blood flow of the left ventricle. Changes in the blood flow make the left ventricular vortex distribution abnormal during the rapid systole and rapid ejection periods, leading to a series of dangerous factors, including increased energy loss and a low cardiac ejection fraction.

CT-Based Simulation of Left Ventricular Hemodynamics: A Pilot Study in Mitral Regurgitation and Left Ventricle Aneurysm Patients

Background

Cardiac CT (CCT) is well suited for a detailed analysis of heart structures due to its high spatial resolution, but in contrast to MRI and echocardiography, CCT does not allow an assessment of intracardiac flow. Computational fluid dynamics (CFD) can complement this shortcoming. It enables the computation of hemodynamics at a high spatio-temporal resolution based on medical images. The aim of this proposed study is to establish a CCT-based CFD methodology for the analysis of left ventricle (LV) hemodynamics and to assess the usability of the computational framework for clinical practice.

Materials and Methods

The methodology is demonstrated by means of four cases selected from a cohort of 125 multiphase CCT examinations of heart failure patients. These cases represent subcohorts of patients with and without LV aneurysm and with severe and no mitral regurgitation (MR). All selected LVs are dilated and characterized by a reduced ejection fraction (EF). End-diastolic and end-systolic image data was used to reconstruct LV geometries with 2D valves as well as the ventricular movement. The intraventricular hemodynamics were computed with a prescribed-motion CFD approach and evaluated in terms of large-scale flow patterns, energetic behavior, and intraventricular washout.

Results

In the MR patients, a disrupted E-wave jet, a fragmentary diastolic vortex formation and an increased specific energy dissipation in systole are observed. In all cases, regions with an impaired washout are visible. The results furthermore indicate that considering several cycles might provide a more detailed view of the washout process. The pre-processing times and computational expenses are in reach of clinical feasibility.

Conclusion

The proposed CCT-based CFD method allows to compute patient-specific intraventricular hemodynamics and thus complements the informative value of CCT. The method can be applied to any CCT data of common quality and represents a fair balance between model accuracy and overall expenses. With further model enhancements, the computational framework has the potential to be embedded in clinical routine workflows, to support clinical decision making and treatment planning.

Prediction of steady flows passing fixed cylinders using deep learning

Considerable attention has been given to deep-learning and machine-learning techniques in an effort to reduce the computational cost of computational fluid dynamics simulation. The present paper addresses the prediction of steady flows passing many fixed cylinders using a deep-learning model and investigates the accuracy of the predicted velocity field. The deep-learning model outputs the x- and y-components of the flow velocity field when the cylinder arrangement is input. The accuracy of the predicted velocity field is investigated, focusing on the velocity profile of the fluid flow and the fluid force acting on the cylinders. The present model accurately predicts the flow when the number of cylinders is equal to or close to that set in the training dataset. The extrapolation of the prediction to a smaller number of cylinders results in error, which can be interpreted as internal friction of the fluid. The results of the fluid force acting on the cylinders suggest that the present deep-learning model has good generalization performance for systems with a larger number of cylinders.

Left atrial appendage shape impacts on the left atrial flow hemodynamics: A numerical hypothesis generating study on two cases

BACKGROUND AND OBJECTIVES

The left atrial appendage (LAA) is the most common region for thrombus formation in atrial fibrillation (AF). Morphological parameters such as shape, size, and LAA volume can cause insufficient effectiveness of available therapeutic options. This study aimed to examine blood flow inside LAA and its removal effects. Computational fluid dynamic (CFD) simulations were carried out on two patients with different morphologies.

METHODS

Two patients' CT was used to reconstruct the 3D geometries of the left atrium (LA) and left atrial appendage (LAA). Then, the geometries were refined in the mentioned software, and the LAA in some models was removed. Next, in generated 3D volume mesh, sinus rhythm (SR) and atrial fibrillation (AF) outflow velocity were imposed at the mitral valve as boundary conditions. Finally, CFD simulation was conducted to analyzing blood flow within LA with/without LA.

RESULTS

The results confirmed that velocity and vorticity decreased under AF conditions inside the LA domain for both patients. However, removing LAA may cause unpredictable consequences, due to different shape and volume of LAA. LAA removal had insignificant effects on velocity and vorticity within LA in SR-mitral outflow. However, removing LAA increased the blood flow rate by 9.15% and vorticity by 7.27% for patient one under AF rhythm (SR)-outflow. In contrast, for patient two, LAA removal in both AF and SR decreased velocity and vorticity within the LA domain. In SR-mitral outflow, velocity dropped by 18.8 %, and vorticity by 13.2%. Also, under AF velocity and vorticity decreased by 23.33% and 18.6% respectively. Meanwhile, the results indicated that the vorticity magnitude increased inside the LAA under AF associated with the risk of thrombus formation, particularly for patient one under AF. The distal part of LAA in both patients was the most common region for blood stasis because of the lowest velocity magnitude.

CONCLUSION

Overall, the morphology of LAA could be the critical parameter to determine the possibility of thrombosis formation, particularly under AF conditions. High volume, low blood flow velocity and two-lobe-appendage are more likely to have blood stasis. Furthermore, the morphology difference can affect the LAA removal result and make it more complicated. So, it could be challenging to generalize LAA removal as a therapeutic option for different patients. The implication of this CFD observation needs more investigation.

Hemodynamic analysis of hepatic arteries for the early evaluation of hepatic fibrosis in biliary atresia

BACKGROUND AND OBJECTIVE

Hepatic fibrosis is the prominent characteristic of biliary atresia (BA), may even progress continually after Kasai procedure (KP). BA, as a devastating pediatric hepatic disease, mainly leads to newborn cholestasis, even liver cirrhosis, eventually hepatic failure. Earlier diagnosis of hepatic fibrosis, which used to be detected by liver biopsy commonly, is consistent with better outcomes of KP. Due to potential risks and uncertainty of liver biopsy, it is an urge to seek a safer and more precise evaluation method as alternative. The purpose of this study is to investigate the hemodynamics of hepatic artery (HA) in hepatic fibrosis of early BA based on computational fluid dynamics (CFD) for evaluating the value of CFD for hepatic fibrosis diagnosis.

METHODS

40 patients were divided into three groups, including the control group, the abnormal liver function group and the mild to moderate hepatic fibrosis group. CFD was applied to quantify primary hemodynamic parameters of HA and related arteries, including blood flow distribution ratio (FDR), pressure, wall shear stress (WSS) and energy loss (EL). Statistical analyses were also performed to compare the differences amongst these above groups.

RESULTS

With the progression of hepatic fibrosis, the increasing tendency of hemodynamic parameters values of HA and related arteries were observed. Values of FDR, pressure, WSS and EL of the mild to moderate group was higher than those of the control group and the abnormal liver function group. There were significant differences on FDR_AA, FDR_HA and EL between the control group and the mild to moderate hepatic fibrosis group (t = 0.037, 0.030 and <0.001, P < 0.05).

CONCLUSION

Significant variations of HA hemodynamics acquired by CFD between the control group and the mild to moderate hepatic fibrosis group demonstrated the relationship between the progression of hepatic fibrosis and the hemodynamic disorder, and suggested that CFD had the potential to assist the diagnosis of hepatic fibrosis in early BA.

Dynamic mechanical interaction between injection liquid and human tissue simulant induced by needle-free injection of a highly focused microjet

This study investigated the fluid-tissue interaction of needle-free injection by evaluating the dynamics of the cavity induced in body-tissue simulant and the resulting unsteady mechanical stress field. Temporal evolution of cavity shape, stress intensity field, and stress vector field during the injection of a conventional injection needle, a proposed highly focused microjet (tip diameter much smaller than capillary nozzle), and a typical non-focused microjet in gelatin were measured using a state-of-the-art high-speed polarization camera, at a frame rate up to 25,000 f.p.s. During the needle injection performed by an experienced nurse, high stress intensity lasted for an order of seconds (from beginning of needle penetration until end of withdrawal), which is much longer than the order of milliseconds during needle-free injections, causing more damage to the body tissue. The cavity induced by focused microjet resembled a funnel which had a narrow tip that penetrated deep into tissue simulant, exerting shear stress in low intensity which diffused through shear stress wave. Whereas the cavity induced by non-focused microjet rebounded elastically (quickly expanded into a sphere and shrank into a small cavity which remained), exerting compressive stress on tissue simulant in high stress intensity. By comparing the distribution of stress intensity, tip shape of the focused microjet contributed to a better performance than non-focused microjet with its ability to penetrate deep while only inducing stress at lower intensity. Dynamic mechanical interaction revealed in this research uncovered the importance of the jet shape for the development of minimally invasive medical devices.

Molecular and Mechanical Mechanisms of Calcification Pathology Induced by Bicuspid Aortic Valve Abnormalities

Bicuspid aortic valve (BAV) is a congenital defect affecting 1-2% of the general population that is distinguished from the normal tricuspid aortic valve (TAV) by the existence of two, rather than three, functional leaflets (or cusps). BAV presents in different morphologic phenotypes based on the configuration of cusp fusion. The most common phenotypes are Type 1 (containing one raphe), where fusion between right coronary and left coronary cusps (BAV R/L) is the most common configuration followed by fusion between right coronary and non-coronary cusps (BAV R/NC). While anatomically different, BAV R/L and BAV R/NC configurations are both associated with abnormal hemodynamic and biomechanical environments. The natural history of BAV has shown that it is not necessarily the primary structural malformation that enforces the need for treatment in young adults, but the secondary onset of premature calcification in ~50% of BAV patients, that can lead to aortic stenosis. While an underlying genetic basis is a major pathogenic contributor of the structural malformation, recent studies have implemented computational models, cardiac imaging studies, and bench-top methods to reveal BAV-associated hemodynamic and biomechanical alterations that likely contribute to secondary complications. Contributions to the field, however, lack support for a direct link between the external valvular environment and calcific aortic valve disease in the setting of BAV R/L and R/NC BAV. Here we review the literature of BAV hemodynamics and biomechanics and discuss its previously proposed contribution to calcification. We also offer means to improve upon previous studies in order to further characterize BAV and its secondary complications.

Linking statistical shape models and simulated function in the healthy adult human heart

Cardiac anatomy plays a crucial role in determining cardiac function. However, there is a poor understanding of how specific and localised anatomical changes affect different cardiac functional outputs. In this work, we test the hypothesis that in a statistical shape model (SSM), the modes that are most relevant for describing anatomy are also most important for determining the output of cardiac electromechanics simulations. We made patient-specific four-chamber heart meshes (n = 20) from cardiac CT images in asymptomatic subjects and created a SSM from 19 cases. Nine modes captured 90% of the anatomical variation in the SSM. Functional simulation outputs correlated best with modes 2, 3 and 9 on average (R = 0.49 ± 0.17, 0.37 ± 0.23 and 0.34 ± 0.17 respectively). We performed a global sensitivity analysis to identify the different modes responsible for different simulated electrical and mechanical measures of cardiac function. Modes 2 and 9 were the most important for determining simulated left ventricular mechanics and pressure-derived phenotypes. Mode 2 explained 28.56 ± 16.48% and 25.5 ± 20.85, and mode 9 explained 12.1 ± 8.74% and 13.54 ± 16.91% of the variances of mechanics and pressure-derived phenotypes, respectively. Electrophysiological biomarkers were explained by the interaction of 3 ± 1 modes. In the healthy adult human heart, shape modes that explain large portions of anatomical variance do not explain equivalent levels of electromechanical functional variation. As a result, in cardiac models, representing patient anatomy using a limited number of modes of anatomical variation can cause a loss in accuracy of simulated electromechanical function.

Digitally Fabricated and Naturally Augmented In Vitro Tissues

Human in vitro tissues are extracorporeal 3D cultures of human cells embedded in biomaterials, commonly hydrogels, which recapitulate the heterogeneous, multiscale, and architectural environment of the human body. Contemporary strategies used in 3D tissue and organ engineering integrate the use of automated digital manufacturing methods, such as 3D printing, bioprinting, and biofabrication. Human tissues and organs, and their intra- and interphysiological interplay, are particularly intricate. For this reason, attentiveness is rising to intersect materials science, medicine, and biology with arts and informatics. This report presents advances in computational modeling of bioink polymerization and its compatibility with bioprinting, the use of digital design and fabrication in the development of fluidic culture devices, and the employment of generative algorithms for modeling the natural and biological augmentation of in vitro tissues. As a future direction, the use of serially linked in vitro tissues as human body-mimicking systems and their application in drug pharmacokinetics and metabolism, disease modeling, and diagnostics are discussed.

Neural Network Vessel Lumen Regression for Automated Lumen Cross-Section Segmentation in Cardiovascular Image-Based Modeling

PURPOSE

We accelerate a pathline-based cardiovascular model building method by training machine learning models to directly predict vessel lumen surface points from computed tomography (CT) and magnetic resonance (MR) medical image data.

METHODS

We formulate vessel lumen detection as a regression task using a polar coordiantes representation.

RESULTS

Neural networks trained with our regression formulation allow predictions to be made with significantly higher accuracy than existing methods that identify the vessel lumen through binary pixel classification. The regression formulation enables machine learning models to be trained end-to-end for vessel lumen detection without post-processing steps that reduce accuracy.

CONCLUSION

By employing our models in a pathline-based cardiovascular model building pipeline we substantially reduce the manual segmentation effort required to build accurate cardiovascular models, and reduce the overall time required to perform patient-specific cardiovascular simulations. While our method is applied here for cardiovascular model building it is generally applicable to segmentation of tree-like and tubular structures from image data.

Combining statistical shape modeling, CFD, and meta-modeling to approximate the patient-specific pressure-drop across the aortic valve in real-time

BACKGROUND

Advances in medical imaging, segmentation techniques, and high performance computing have stimulated the use of complex, patient-specific, three-dimensional Computational Fluid Dynamics (CFD) simulations. Patient-specific, CFD-compatible geometries of the aortic valve are readily obtained. CFD can then be used to obtain the patient-specific pressure-flow relationship of the aortic valve. However, such CFD simulations are computationally expensive, and real-time alternatives are desired.

AIM

The aim of this work is to evaluate the performance of a meta-model with respect to high-fidelity, three-dimensional CFD simulations of the aortic valve.

METHODS

Principal component analysis was used to build a statistical shape model (SSM) from a population of 74 iso-topological meshes of the aortic valve. Synthetic meshes were created with the SSM, and steady-state CFD simulations at flow-rates between 50 and 650 mL/s were performed to build a meta-model. The meta-model related the statistical shape variance, and flow-rate to the pressure-drop.

RESULTS

Even though the first three shape modes account for only 46% of shape variance, the features relevant for the pressure-drop seem to be captured. The three-mode shape-model approximates the pressure-drop with an average error of 8.8% to 10.6% for aortic valves with a geometric orifice area below 150 mm² . The proposed methodology was least accurate for aortic valve areas above 150 mm² . Further reduction to a meta-model introduces an additional 3% error.

CONCLUSIONS

Statistical shape modeling can be used to capture shape variation of the aortic valve. Meta-models trained by SSM-based CFD simulations can provide an estimate of the pressure-flow relationship in real-time.

Left Ventricular Blood Flow Kinetic Energy Assessment by 4D Flow Cardiovascular Magnetic Resonance: A Systematic Review of the Clinical Relevance

Background: There is an emerging body of evidence that supports the potential clinical value of left ventricular (LV) intracavity blood flow kinetic energy (KE) assessment using four-dimensional flow cardiovascular magnetic resonance imaging (4D flow CMR). The aim of this systematic review is to summarize studies evaluating LV intracavity blood flow KE quantification methods and its potential clinical significance. Methods: A systematic review search was carried out on Medline, Pubmed, EMBASE and CINAHL. Results: Of the 677 articles screened, 16 studies met eligibility. These included six (37%) studies on LV diastolic function, another six (37%) studies on heart failure or cardiomyopathies, three (19%) studies on ischemic heart disease or myocardial infarction and finally, one (6%) study on valvular heart disease, namely, mitral regurgitation. One of the main strengths identified by these studies is high reproducibility of LV blood flow KE hemodynamic assessment (mean coefficient of variability = 6 ±  2%) for the evaluation of LV diastolic function. Conclusions: The evidence gathered in this systematic review suggests that LV blood flow KE has great promise for LV hemodynamic assessment. Studies showed increased diagnostic confidence at no cost of additional time. Results were highly reproducible with low intraobserver variability.

Computational fluid dynamics: a primer for congenital heart disease clinicians

Computational fluid dynamics has become an important tool for studying blood flow dynamics. As an in-silico collection of methods, computational fluid dynamics is noninvasive and provides numerical values for the most important parameters of blood flow, such as velocity and pressure that are crucial in hemodynamic studies. In this primer, we briefly explain the basic theory and workflow of the two most commonly applied computational fluid dynamics techniques used in the congenital heart disease literature: the finite element method and the finite volume method. We define important terminology and include specific examples of how using these methods can answer important clinical questions in congenital cardiac surgery planning and perioperative patient management.

A hybrid echocardiography-CFD framework for ventricular flow simulations

Image-based CFD is a powerful tool to study cardiovascular flows while 2D echocardiography (echo) is the most widely used noninvasive imaging modality for the diagnosis of heart disease. Here, echo is combined with CFD, that is, an echo-CFD framework, to study ventricular flows. To achieve this, the previous 3D reconstruction from multiple 2D echo at standard cross sections is extended by: (a) reconstructing aortic and mitral valves from 2D echo and closing the left-ventricle (LV) geometry by approximating a superior wall; (b) incorporating the physiological assumption of the fixed apex as a reference (fixed) point in the 3D reconstruction; and (c) incorporating several smoothing algorithms to remove the nonphysical oscillations (ringing) near the basal section. The method is applied to echo from a baseline LV and one after inducing acute myocardial ischemia (AMI). The 3D reconstruction is validated by comparing it against a reference reconstruction from many echo sections while flow simulations are validated against the Doppler ultrasound velocity measurements. The sensitivity study shows that the choice of the smoothing algorithm does not change the flow pattern inside the LV. However, the presence of the mitral valve can significantly change the flow pattern during the diastole phase. In addition, the abnormal shape of a LV with AMI can drastically change the flow during diastole. Furthermore, the hemodynamic energy loss, as an indicator of the LV pumping performance, for different test cases is calculated, which shows a larger energy loss for a LV with AMI compared to the baseline one.

A Patient-Specific CFD Pipeline Using Doppler Echocardiography for Application in Coarctation of the Aorta in a Limited Resource Clinical Context

Congenital heart disease (CHD) is the most common birth defect globally and coarctation of the aorta (CoA) is one of the commoner CHD conditions, affecting around 1/1800 live births. CoA is considered a CHD of critical severity. Unfortunately, the prognosis for a child born in a low and lower-middle income country (LLMICs) with CoA is far worse than in a high-income country. Reduced diagnostic and interventional capacities of specialists in these regions lead to delayed diagnosis and treatment, which in turn lead to more cases presenting at an advanced stage. Computational fluid dynamics (CFD) is an important tool in this context since it can provide additional diagnostic data in the form of hemodynamic parameters. It also provides an in silico framework, both to test potential procedures and to assess the risk of further complications arising post-repair. Although this concept is already in practice in high income countries, the clinical infrastructure in LLMICs can be sparse, and access to advanced imaging modalities such as phase contrast magnetic resonance imaging (PC-MRI) is limited, if not impossible. In this study, a pipeline was developed in conjunction with clinicians at the Red Cross War Memorial Children’s Hospital, Cape Town and was applied to perform a patient-specific CFD study of CoA. The pipeline uses data acquired from CT angiography and Doppler transthoracic echocardiography (both much more clinically available than MRI in LLMICs), while segmentation is conducted via SimVascular and simulation is realized using OpenFOAM. The reduction in cost through use of open-source software and the use of broadly available imaging modalities makes the methodology clinically feasible and repeatable within resource-constrained environments. The project identifies the key role of Doppler echocardiography, despite its disadvantages, as an intrinsic component of the pipeline if it is to be used routinely in LLMICs.

Computational Simulation of Cardiac Function and Blood Flow in the Circulatory System under Continuous Flow Left Ventricular Assist Device Support during Atrial Fibrillation

Prevalence of atrial fibrillation (AF) is high in heart failure patients supported by a continuous flow left ventricular assist device (CF-LVAD); however, the long term effects remain unclear. In this study, a computational model simulating effects of AF on cardiac function and blood flow for heart failure and CF-LVAD support is presented. The computational model describes left and right heart, systemic and pulmonary circulations and cerebral circulation, and utilises patient-derived RR interval series for normal sinus rhythm (SR). Moreover, AF was simulated using patient-derived unimodal and bimodal distributed RR interval series and patient specific left ventricular systolic functions. The cardiovascular system model simulated clinically-observed haemodynamic outcomes under CF-LVAD support during AF, such as reduced right ventricular ejection fraction and elevated systolic pulmonary arterial pressure. Moreover, relatively high aortic peak pressures and middle arterial peak flow rates during AF with bimodal RR interval distribution, reduced to similar levels as during normal SR and AF with unimodal RR interval distribution under CF-LVAD support. The simulation results suggest that factors such as distribution of RR intervals and systolic left ventricular function may influence haemodynamic outcome of CF-LVAD support during AF.

Fluid-Structure Interaction Simulation of Blood Flow and Cerebral Aneurysm: Effect of Partly Blocked Vessel

In this study, using fluid-structure interaction (FSI), 3-dimensional blood flow in an aneurysm in the circle of Willis - which is located in the middle cerebral artery (MCA) - has been simulated. The purpose of this study is to evaluate the effect of a partly blocked vessel on an aneurysm. To achieve this purpose, two cases have been investigated using the FSI method: in the first case, an ideal geometry of aneurysm in the MCA has been simulated; in the second case, modeling is performed for an ideal geometry of the aneurysm in the MCA with a partly blocked vessel. All boundary conditions, properties and modeling methods were considered the same for both cases. The only difference between the two cases was that part of the MCA parent artery was blocked in the second case. In order to consider the hyperelastic property of the wall and the non-Newtonian properties of the blood, the Mooney-Rivlin model and the Carreau model have been used, respectively. In the second case, the Von Mises stress in the peak systole is 26% higher than in the first case. With regard to the high amount of Von Mises stress, the risk of rupture of the aneurysm is higher in this case. In the second case, the maximum wall shear stress (WSS) is 12% higher than in the first case. And maximum displacement in the second case is also higher than in the first. So, the risk of growth of the aneurysm is higher in cases with a partly blocked vessel.

Computational Fluid Dynamics of Vascular Disease in Animal Models

Recent applications of computational fluid dynamics (CFD) applied to the cardiovascular system have demonstrated its power in investigating the impact of hemodynamics on disease initiation, progression, and treatment outcomes. Flow metrics such as pressure distributions, wall shear stresses (WSS), and blood velocity profiles can be quantified to provide insight into observed pathologies, assist with surgical planning, or even predict disease progression. While numerous studies have performed simulations on clinical human patient data, it often lacks prediagnosis information and can be subject to large intersubject variability, limiting the generalizability of findings. Thus, animal models are often used to identify and manipulate specific factors contributing to vascular disease because they provide a more controlled environment. In this review, we explore the use of CFD in animal models in recent studies to investigate the initiating mechanisms, progression, and intervention effects of various vascular diseases. The first section provides a brief overview of the CFD theory and tools that are commonly used to study blood flow. The following sections are separated by anatomical region, with the abdominal, thoracic, and cerebral areas specifically highlighted. We discuss the associated benefits and obstacles to performing CFD modeling in each location. Finally, we highlight animal CFD studies focusing on common surgical treatments, including arteriovenous fistulas (AVF) and pulmonary artery grafts. The studies included in this review demonstrate the value of combining CFD with animal imaging and should encourage further research to optimize and expand upon these techniques for the study of vascular disease.

Accelerating cardiovascular model building with convolutional neural networks

The objective of this work is to reduce the user effort required for 2D segmentation when building patient-specific cardiovascular models using the SimVascular cardiovascular modeling software package. The proposed method uses a fully convolutional neural network (FCNN) to generate 2D cardiovascular segmentations. Given vessel pathlines, the neural network generates 2D vessel enhancement images along the pathlines. Thereafter, vessel segmentations are extracted using the marching-squares algorithm, which are then used to construct 3D cardiovascular models. The neural network is trained using a novel loss function, tailored for partially labeled segmentation data. An automated quality control method is also developed, allowing promising segmentations to be selected. Compared with a threshold and level set algorithm, the FCNN method improved 2D segmentation accuracy across several metrics. The proposed quality control approach further improved the average DICE score by 25.8%. In tests with users of SimVascular, when using quality control, users accepted 80% of segmentations produced by the best performing FCNN. The FCNN cardiovascular model building method reduces the amount of manual segmentation effort required for patient-specific model construction, by as much as 73%. This leads to reduced turnaround time for cardiovascular simulations. While the method was used for cardiovascular model building, it is applicable to general tubular structures. Graphical Abstract Proposed FCNN-based cardiovascular model building pipeline. a.) Image data and vessel pathline supplied by the user. b.) Path information is used to extract image pixel intensities in plane perpendicular to the vessel path. c.) 2D images extracted along vessel pathlines are input to the FCNN. d.) FCNN acts on the input images to compute local vessel enhancement images. e.) Vessel enhancement images computed by the FCNN, the pixel values are between 0 and 1 indicating vessel tissue likelihood. f.) The marching-squares algorithm is appliedto each enhanced image to extract the central vessel segmentation. g.) 2D extracted vessel surface points overlayed on original input images. h.) The 2D vessel surface points are transformed back to 3D space. i.) 3D crosssectional vessel surfaces are interpolated along the pathline to form the final vessel model.

Impact of myocardial infarction on intraventricular vortex and flow energetics assessed using computational simulations

Flow energetics have been proposed as early indicators of progressive left ventricular (LV) functional impairment in patients with myocardial infarction (MI), but its correlation with individual MI parameters has not been fully explored. Using electro-fluid-structure interaction LV models, this study investigated the correlation between four MI parameters: infarct size, infarct multiplicity, regional enhancement of contractility at the viable myocardium area (RECVM), and LV mechanical dyssynchrony (LVMD) with intraventricular vortex and flow energetics. In LV with small infarcts, our results showed that infarct appearance amplified the energy dissipation index (DI), where substantial viscous energy loss was observed in areas with high flow velocity and near the infarct-vortex interface. The LV with small multiple infarcts and RECVM showed remarkable DI increment during systole and diastole. In correlation analysis, the systolic kinetic energy fluctuation index (E') was positively related to ejection fraction (EF) (R²  = 0.982) but negatively correlated with diastolic E' (R²  = 0.970). Diastolic E' was inversely correlated with vortex kinetic energy (R²  = 0.960) and vortex depth (R²  = 0.876). We showed an excessive systolic DI could differentiate infarcted LV with normal EF from healthy LV. Strong flow acceleration, LVMD, and vortex-infarct interactions were predominant factors that induced excessive DI in infarcted LVs. Instead of causing undesired flow turbulence, high systolic E' suggested the existence of energetic flow acceleration, while high diastolic E' implied an inefficient diastolic filling. Thus, systolic E' is not a suitable early indicator for progressive LV dysfunction in MI patients, while diastolic E' may be a useful index to indicate diastolic impairment in these patients.

Application of Patient-Specific Computational Fluid Dynamics in Coronary and Intra-Cardiac Flow Simulations: Challenges and Opportunities

The emergence of new cardiac diagnostics and therapeutics of the heart has given rise to the challenging field of virtual design and testing of technologies in a patient-specific environment. Given the recent advances in medical imaging, computational power and mathematical algorithms, patient-specific cardiac models can be produced from cardiac images faster, and more efficiently than ever before. The emergence of patient-specific computational fluid dynamics (CFD) has paved the way for the new field of computer-aided diagnostics. This article provides a review of CFD methods, challenges and opportunities in coronary and intra-cardiac flow simulations. It includes a review of market products and clinical trials. Key components of patient-specific CFD are covered briefly which include image segmentation, geometry reconstruction, mesh generation, fluid-structure interaction, and solver techniques.

Cardiovascular tissue engineering: From basic science to clinical application

Valvular heart disease is an increasing population health problem and, especially in the elderly, a significant cause of morbidity and mortality. The current treatment options, such as mechanical and bioprosthetic heart valve replacements, have significant restrictions and limitations. Considering the increased life expectancy of our aging population, there is an urgent need for novel heart valve concepts that remain functional throughout life to prevent the need for reoperation. Heart valve tissue engineering aims to overcome these constraints by creating regenerative, self-repairing valve substitutes with life-long durability. In this review, we give an overview of advances in the development of tissue engineered heart valves, and describe the steps required to design and validate a novel valve prosthesis before reaching first-in-men clinical trials. In-silico and in-vitro models are proposed as tools for the assessment of valve design, functionality and compatibility, while in-vivo preclinical models are required to confirm the remodeling and growth potential of the tissue engineered heart valves. An overview of the tissue engineered heart valve studies that have reached clinical translation is also presented. Final remarks highlight the possibilities as well as the obstacles to overcome in translating heart valve prostheses into clinical application.

Exploring kinetic energy as a new marker of cardiac function in the single ventricle circulation

Ventricular volumetric ejection fraction (VV EF) is often normal in patients with single ventricle circulations despite them experiencing symptoms related to circulatory failure. We sought to determine if kinetic energy (KE) could be a better marker of ventricular performance. KE was prospectively quantified using four-dimensional flow MRI in 41 patients with a single ventricle circulation (aged 0.5-28 yr) and compared with 43 healthy volunteers (aged 1.5-62 yr) and 14 patients with left ventricular (LV) dysfunction (aged 28-79 yr). Intraventricular end-diastolic blood was tracked through systole and divided into ejected and residual blood components. Two ejection fraction (EF) metrics were devised based on the KE of the ejected component over the total of both the ejected and residual components using 1) instantaneous peak KE to assess KE EF or 2) summating individual peak particle energy (PE) to assess PE EF. KE EF and PE EF had a smaller range than VV EF in healthy subjects (97.9 ± 0.8 vs. 97.3 ± 0.8 vs. 60.1 ± 5.2%). LV dysfunction caused a fall in KE EF ( P = 0.01) and PE EF ( P = 0.0001). VV EF in healthy LVs and single ventricle hearts was equivalent; however, KE EF and PE EF were lower ( P < 0.001) with a wider range indicating a spectrum of severity. Those reporting the greatest symptomatic impairment (New York Heart Association II) had lower PE EF than asymptomatic subjects ( P = 0.0067). KE metrics are markers of healthy cardiac function. PE EF may be useful in grading dysfunction. NEW & NOTEWORTHY Kinetic energy (KE) represents the useful work of the heart in ejecting blood. This article details the utilization of KE indexes to assess cardiac function in health and a variety of pathophysiological conditions. KE ejection fraction and particle energy ejection fraction (PE EF) showed a narrow range in health and a lower wider range in disease representing a spectrum of severity. PE EF was altered by functional status potentially offering the opportunity to grade dysfunction.

Ventricular Assist Devices: Current State and Challenges

Cardiovascular disease (CVD), as the most prevalent human disease, incorporates a broad spectrum of cardiovascular system malfunctions/disorders. While cardiac transplantation is widely acknowledged as the optional treatment for patients suffering from end-stage heart failure (HF), due to its related drawbacks, such as the unavailability of heart donors, alternative treatments, i.e., implanting a ventricular assist device (VAD), it has been extensively utilized in recent years to recover heart function. However, this solution is thought problematic as it fails to satisfactorily provide lifelong support for patients at the end-stage of HF, nor does is solve the problem of their extensive postsurgery complications. In recent years, the huge technological advancements have enabled the manufacturing of a wide variety of reliable VAD devices, which provides a promising avenue for utilizing VAD implantation as the destination therapy (DT) in the future. Along with typical VAD systems, other innovative mechanical devices for cardiac support, as well as cell therapy and bioartificial cardiac tissue, have resulted in researchers proposing a new HF therapy. This paper aims to concisely review the current state of VAD technology, summarize recent advancements, discuss related complications, and argue for the development of the envisioned alternatives of HF therapy.

Two-dimensional intraventricular flow pattern visualization using the image-based computational fluid dynamics

The image-based computational fluid dynamics (IB-CFD) technique, as the combination of medical images and the CFD method, is utilized in this research to analyze the left ventricle (LV) hemodynamics. The research primarily aims to propose a semi-automated technique utilizing some freely available and commercial software packages in order to simulate the LV hemodynamics using the IB-CFD technique. In this research, moreover, two different physiological time-resolved 2D models of a patient-specific LV with two different types of aortic and mitral valves, including the orifice-type valves and integrated with rigid leaflets, are adopted to visualize the process of developing intraventricular vortex formation and propagation. The blood flow pattern over the whole cardiac cycle of two models is also compared to investigate the effect of utilizing different valve types in the process of the intraventricular vortex formation. Numerical findings indicate that the model with integrated valves can predict more complex intraventricular flow that can match better the physiological flow pattern in comparison to the orifice-type model.