Hemodynamic Evaluation of a Biological and Mechanical Aortic Valve Prosthesis Using Patient-Specific MRI-Based CFD

Stress Load and Ascending Aortic Aneurysms: An Observational, Longitudinal, Single-Center Study Using Computational Fluid Dynamics

Ascending aortic aneurysm (AAoA) is a silent disease with high mortality; however, the factors associated with a worse prognosis are not completely understood. The objective of this observational, longitudinal, single-center study was to identify the hemodynamic patterns and their influence on AAoA growth using computational fluid dynamics (CFD), focusing on the effects of geometrical variations on aortic hemodynamics. Personalized anatomic models were obtained from angiotomography scans of 30 patients in two different years (with intervals of one to three years between them), of which 16 (53%) showed aneurysm growth (defined as an increase in the ascending aorta volume by 5% or more). Numerically determined velocity and pressure fields were compared with the outcome of aneurysm growth. Through a statistical analysis, hemodynamic characteristics were found to be associated with aneurysm growth: average and maximum high pressure (superior to 100 Pa); average and maximum high wall shear stress (superior to 7 Pa) combined with high pressure (>100 Pa); and stress load over time (maximum pressure multiplied by the time interval between the exams). This study provides insights into a worse prognosis of this serious disease and may collaborate for the expansion of knowledge about mechanobiology in the progression of AAoA.

Assessing the impact of turbulent kinetic energy boundary conditions on turbulent flow simulations using computational fluid dynamics

Computational fluid dynamics has been widely used to study hemodynamics, but accurately determining boundary conditions for turbulent blood flow remains challenging. This study aims to investigate the effect of patient-specific turbulence boundary conditions on the accuracy of turbulent flow simulation. Using a stenosis model with 50% severity in diameter, the post-stenosis turbulence flow region was simulated with different planes to obtain inlet boundary conditions and simulate downstream flows. The errors of simulated flow fields obtained with turbulence kinetic energy (TKE) boundary data and arbitrary turbulence intensity were compared. Additionally, the study tested various TKE data resolutions and noise levels to simulate experimental environments. The mean absolute error of velocity and TKE was investigated with various turbulence intensities and TKE mapping. While voxel size and signal-to-noise ratio of the TKE data affected the results, simulation with SNR > 5 and voxel size < 10% resulted in better accuracy than simulations with turbulence intensities. The simulation with appropriate TKE boundary data resulted in a more accurate velocity and turbulence field than those with arbitrary turbulence intensity boundary conditions. The study demonstrated the potential improvement of turbulent blood flow simulation with patient-specific turbulence boundary conditions, which can be obtained from recent measurement techniques.

Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to capture personalised aortic haemodynamics

Data driven, reduced order modelling has shown promise in tackling the challenges associated with computational and experimental haemodynamic models. In this work, we focus on the use of Reduced Order Models (ROMs) to reconstruct velocity fields in a patient-specific dissected aorta, with the objective being to compare the ROMs obtained from Robust Proper Orthogonal Decomposition (RPOD) to those obtained from the traditional Proper Orthogonal Decomposition (POD). POD and RPOD are applied to in vitro, haemodynamic data acquired by Particle Image Velocimetry and compare the decomposed flows to those derived from Computational Fluid Dynamics (CFD) data for the same geometry and flow conditions. In this work, PIV and CFD results act as surrogates for clinical haemodynamic data e.g. MR, helping to demonstrate the potential use of ROMS in real clinical scenarios. The flow is reconstructed using different numbers of POD modes and the flow features obtained throughout the cardiac cycle are compared to the original Full Order Models (FOMs). Robust Principal Component Analysis (RPCA), the first step of RPOD, has been found to enhance the quality of PIV data, allowing POD to capture most of the kinetic energy of the flow in just two modes similar to the numerical data that are free from measurement noise. The reconstruction errors differ along the cardiac cycle with diastolic flows requiring more modes for accurate reconstruction. In general, modes 1-10 are found sufficient to represent the flow field. The results demonstrate that the coherent structures that characterise this aortic dissection flow are described by the first few POD modes suggesting that it is possible to represent the macroscale behaviour of aortic flow in a low-dimensional space; thus significantly simplifying the problem, and allowing for more computationally efficient flow simulations or machine learning based flow predictions that can pave the way for translation of such models to the clinic.

Fluid-structure interaction modeling of compliant aortic valves using the lattice Boltzmann CFD and FEM methods

The lattice Boltzmann method (LBM) has been increasingly used as a stand-alone CFD solver in various biomechanical applications. This study proposes a new fluid-structure interaction (FSI) co-modeling framework for the hemodynamic-structural analysis of compliant aortic valves. Toward that goal, two commercial software packages are integrated using the lattice Boltzmann (LBM) and finite element (FE) methods. The suitability of the LBM-FE hemodynamic FSI is examined in modeling healthy tricuspid and bicuspid aortic valves (TAV and BAV), respectively. In addition, a multi-scale structural approach that has been employed explicitly recognizes the heterogeneous leaflet tissues and differentiates between the collagen fiber network (CFN) embedded within the elastin matrix of the leaflets. The CFN multi-scale tissue model is inspired by monitoring the distribution of the collagen in 15 porcine leaflets. Different simulations have been examined, and structural stresses and resulting hemodynamics are analyzed. We found that LBM-FE FSI approach can produce good predictions for the flow and structural behaviors of TAV and BAV and correlates well with those reported in the literature. The multi-scale heterogeneous CFN tissue structural model enhances our understanding of the mechanical roles of the CFN and the elastin matrix behaviors. The importance of LBM-FE FSI also emerges in its ability to resolve local hemodynamic and structural behaviors. In particular, the diastolic fluctuating velocity phenomenon near the leaflets is explicitly predicted, providing vital information on the flow transient nature. The full closure of the contacting leaflets in BAV is also demonstrated. Accordingly, good structural kinematics and deformations are captured for the entire cardiac cycle.

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.

A parametric geometry model of the aortic valve for subject-specific blood flow simulations using a resistive approach

Cardiac valves simulation is one of the most complex tasks in cardiovascular modeling. Fluid-structure interaction is not only highly computationally demanding but also requires knowledge of the mechanical properties of the tissue. Therefore, an alternative is to include valves as resistive flow obstacles, prescribing the geometry (and its possible changes) in a simple way, but, at the same time, with a geometry complex enough to reproduce both healthy and pathological configurations. In this work, we present a generalized parametric model of the aortic valve to obtain patient-specific geometries that can be included into blood flow simulations using a resistive immersed implicit surface (RIIS) approach. Numerical tests are presented for geometry generation and flow simulations in aortic stenosis patients whose parameters are extracted from ECG-gated CT images.

The impact of 4D-Flow MRI spatial resolution on patient-specific CFD simulations of the thoracic aorta

Magnetic Resonance Imaging (MRI) is considered the gold standard of medical imaging technologies as it allows for accurate imaging of blood vessels. 4-Dimensional Flow Magnetic Resonance Imaging (4D-Flow MRI) is built on conventional MRI, and provides flow data in the three vector directions and a time resolved magnitude data set. As such it can be used to retrospectively calculate haemodynamic parameters of interest, such as Wall Shear Stress (WSS). However, multiple studies have indicated that a significant limitation of the imaging technique is the spatiotemporal resolution that is currently available. Recent advances have proposed and successfully integrated 4D-Flow MRI imaging techniques with Computational Fluid Dynamics (CFD) to produce patient-specific simulations that have the potential to aid in treatments,surgical decision making, and risk stratification. However, the consequences of using insufficient 4D-Flow MRI spatial resolutions on any patient-specific CFD simulations is currently unclear, despite being a recognised limitation. The research presented in this study aims to quantify the inaccuracies in patient-specific 4D-Flow MRI based CFD simulations that can be attributed to insufficient spatial resolutions when acquiring 4D-Flow MRI data. For this research, a patient has undergone four 4D-Flow MRI scans acquired at various isotropic spatial resolutions and patient-specific CFD simulations have subsequently been run using geometry and velocity data produced from each scan. It was found that compared to CFD simulations based on a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.5\,{\text {mm}} \times 1.5\,{\text {mm}} \times 1.5\,{\text {mm}}$$\end{document}1.5mm×1.5mm×1.5mm, using a spatial resolution of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$4\,{\text {mm}} \times 4\,{\text {mm}} \times 4\,{\text {mm}}$$\end{document}4mm×4mm×4mm substantially underestimated the maximum velocity magnitude at peak systole by \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$110.55\%$$\end{document}110.55%. The impacts of 4D-Flow MRI spatial resolution on WSS calculated from CFD simulations have been investigated and it has been shown that WSS is underestimated in CFD simulations that are based on a coarse 4D-Flow MRI spatial resolution. The authors have concluded that a minimum 4D-Flow MRI spatial resolution of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.5\,{\text {mm}} \times 1.5\,{\text {mm}} \times 1.5\,{\text {mm}}$$\end{document}1.5mm×1.5mm×1.5mm must be used when acquiring 4D-Flow MRI data to perform patient-specific CFD simulations. A coarser spatial resolution will produce substantial differences within the flow field and geometry.

Simulation of Mechanical Heart Valve Dysfunction and the Non-Newtonian Blood Model Approach

The mechanical heart valve (MHV) is commonly used for the treatment of cardiovascular diseases. Nonphysiological hemodynamic in the MHV may cause hemolysis, platelet activation, and an increased risk of thromboembolism. Thromboembolism may cause severe complications and valve dysfunction. This paper thoroughly reviewed the simulation of physical quantities (velocity distribution, vortex formation, and shear stress) in healthy and dysfunctional MHV and reviewed the non-Newtonian blood flow characteristics in MHV. In the MHV numerical study, the dysfunction will affect the simulation results, increase the pressure gradient and shear stress, and change the blood flow patterns, increasing the risks of hemolysis and platelet activation. The blood flow passes downstream and has obvious recirculation and stagnation region with the increased dysfunction severity. Due to the complex structure of the MHV, the non-Newtonian shear-thinning viscosity blood characteristics become apparent in MHV simulations. The comparative study between Newtonian and non-Newtonian always shows the difference. The shear-thinning blood viscosity model is the basics to build the blood, also the blood exhibiting viscoelastic properties. More details are needed to establish a complete and more realistic simulation.

Abnormal aortic flow profiles persist after aortic valve replacement in the majority of patients with aortic valve disease: how model-based personalized therapy planning could improve results. A pilot study approach

OBJECTIVES

Complex blood flow profiles in the aorta are known to contribute to vessel dilatation. We studied flow profiles in the aorta in patients with aortic valve disease before and after surgical aortic valve replacement (AVR).

METHODS

Thirty-four patients with aortic valve disease underwent 4-dimensional velocity-encoded magnetic resonance imaging before and after AVR (biological valve = 27, mechanical valve = 7). Seven healthy volunteers served as controls. Eccentricity (ES) and complex flow scores (CFS) were determined from the degree of helicity, vorticity and eccentricity of flow profiles in the aorta. Model-based therapy planning was used in 4 cases to improve in silico postoperative flow profiles by personalized adjustment of size, rotation and angulation of the valve as well as aorta diameter.

RESULTS

Patients with aortic valve disease showed more complex flow than controls [median ES 2.5 (interquartile range (IQR) 2.3-2.7) vs 1.0 (IQR 1.0-1.0), P < 0.001, median CFS 4.7 (IQR 4.3-4.8) vs 1.0 (IQR 1.0-2.0), P < 0.001]. After surgery, flow complexity in the total patient cohort was reduced, but remained significantly higher compared to controls [median ES 2.3 (IQR 1.9-2.3) vs 1.0 (IQR 1.0-1.0), P < 0.001, median CFS 3.8 (IQR 3.0-4.3) vs 1.0 (IQR 1.0-2.0), P < 0.001]. In patients after mechanical AVR, flow complexity fell substantially and showed no difference from controls [median ES 1.0 (IQR 1.0-2.3) vs 1.0 (IQR 1.0-1.0), P = 0.46, median CFS 1.0 (IQR 1.0-3.3) vs 1.0 (IQR 1.0-2.0), P = 0.71]. In all 4 selected cases (biological, n = 2; mechanical, n = 2), model-based therapy planning reduced in silico complexity of flow profiles compared to the existing post-surgical findings [median ES 1.7 (IQR 1.4-1.7) vs 2.3 (IQR 2.3-2.3); CFS 1.7 (IQR 1.4-2.5) vs 3.8 (IQR 3.3-4.3)].

CONCLUSIONS

Abnormal flow profiles in the aorta more frequently persist after surgical AVR. Model-based therapy planning might have the potential to optimize treatment for best possible individual outcome.

CLINICAL TRIAL REGISTRATION NUMBER

clinicaltrials.gov NCT03172338, 1 June 2017, retrospectively registered; NCT02591940, 30 October 2015, retrospectively registered.

Aortic valve function post-replacement of severe aortic stenosis by transcatheter procedure versus surgery: a systematic review and metanalysis

Transcatheter aortic valve replacement (TAVR) has shown to reduce mortality compared to surgical aortic valve replacement (sAVR). However, it is unknown which procedure is associated with better post-procedural valvular function. We conducted a meta-analysis of randomized clinical trials that compared TAVR to sAVR for at least 2 years. The primary outcome was post-procedural patient-prosthesis-mismatch (PPM). Secondary outcomes were post-procedural and 2-year: effective orifice area (EOA), paravalvular gradient (PVG) and moderate/severe paravalvular leak (PVL). We identified 6 trials with a total of 7022 participants with severe aortic stenosis. TAVR was associated with 37% (95% CI [0.51–0.78) mean RR reduction of post-procedural PPM, a decrease that was not affected by the surgical risk at inclusion, neither by the transcatheter heart valve system. Postprocedural changes in gradient and EOA were also in favor of TAVR as there was a pooled mean difference decrease of 0.56 (95% CI [0.73–0.38]) in gradient and an increase of 0.47 (95% CI [0.38–0.56]) in EOA. Additionally, self-expandable valves were associated with a higher decrease in gradient than balloon ones (beta = 0.38; 95% CI [0.12–0.64]). However, TAVR was associated with a higher risk of moderate/severe PVL (pooled RR: 9.54, 95% CI [5.53–16.46]). All results were sustainable at 2 years.

Personalized intervention cardiology with transcatheter aortic valve replacement made possible with a non-invasive monitoring and diagnostic framework

One of the most common acute and chronic cardiovascular disease conditions is aortic stenosis, a disease in which the aortic valve is damaged and can no longer function properly. Moreover, aortic stenosis commonly exists in combination with other conditions causing so many patients suffer from the most general and fundamentally challenging condition: complex valvular, ventricular and vascular disease (C3VD). Transcatheter aortic valve replacement (TAVR) is a new less invasive intervention and is a growing alternative for patients with aortic stenosis. Although blood flow quantification is critical for accurate and early diagnosis of C3VD in both pre and post-TAVR, proper diagnostic methods are still lacking because the fluid-dynamics methods that can be used as engines of new diagnostic tools are not well developed yet. Despite remarkable advances in medical imaging, imaging on its own is not enough to quantify the blood flow effectively. Moreover, understanding of C3VD in both pre and post-TAVR and its progression has been hindered by the absence of a proper non-invasive tool for the assessment of the cardiovascular function. To enable the development of new non-invasive diagnostic methods, we developed an innovative image-based patient-specific computational fluid dynamics framework for patients with C3VD who undergo TAVR to quantify metrics of: (1) global circulatory function; (2) global cardiac function as well as (3) local cardiac fluid dynamics. This framework is based on an innovative non-invasive Doppler-based patient-specific lumped-parameter algorithm and a 3-D strongly-coupled fluid-solid interaction. We validated the framework against clinical cardiac catheterization and Doppler echocardiographic measurements and demonstrated its diagnostic utility by providing novel analyses and interpretations of clinical data in eleven C3VD patients in pre and post-TAVR status. Our findings position this framework as a promising new non-invasive diagnostic tool that can provide blood flow metrics while posing no risk to the patient. The diagnostic information, that the framework can provide, is vitally needed to improve clinical outcomes, to assess patient risk and to plan treatment.

Hemodynamic Modeling of Biological Aortic Valve Replacement Using Preoperative Data Only

Objectives: Prediction of aortic hemodynamics after aortic valve replacement (AVR) could help optimize treatment planning and improve outcomes. This study aims to demonstrate an approach to predict postoperative maximum velocity, maximum pressure gradient, secondary flow degree (SFD), and normalized flow displacement (NFD) in patients receiving biological AVR.

Methods: Virtual AVR was performed for 10 patients, who received actual AVR with a biological prosthesis. The virtual AVRs used only preoperative anatomical and 4D flow MRI data. Subsequently, computational fluid dynamics (CFD) simulations were performed and the abovementioned hemodynamic parameters compared between postoperative 4D flow MRI data and CFD results.

Results: For maximum velocities and pressure gradients, postoperative 4D flow MRI data and CFD results were strongly correlated (R² = 0.75 and R² = 0.81) with low root mean square error (0.21 m/s and 3.8 mmHg). SFD and NFD were moderately and weakly correlated at R² = 0.44 and R² = 0.20, respectively. Flow visualization through streamlines indicates good qualitative agreement between 4D flow MRI data and CFD results in most cases.

Conclusion: The approach presented here seems suitable to estimate postoperative maximum velocity and pressure gradient in patients receiving biological AVR, using only preoperative MRI data. The workflow can be performed in a reasonable time frame and offers a method to estimate postoperative valve prosthesis performance and to identify patients at risk of patient-prosthesis mismatch preoperatively. Novel parameters, such as SFD and NFD, appear to be more sensitive, and estimation seems harder. Further workflow optimization and validation of results seems warranted.

Hemodynamic Performance of Dysfunctional Prosthetic Heart Valve with the Concomitant Presence of Subaortic Stenosis: In Silico Study

The prosthetic heart valve is vulnerable to dysfunction after surgery, thus a frequent assessment is required. Doppler electrocardiography and its quantitative parameters are commonly used to assess the performance of the prosthetic heart valves and provide detailed information on the interaction between the heart chambers and related prosthetic valves, allowing early detection of complications. However, in the case of the presence of subaortic stenosis, the accuracy of Doppler has not been fully investigated in previous studies and guidelines. Therefore, it is important to evaluate the accuracy of the parameters in such cases to get early detection, and a proper treatment plan for the patient, at the right time. In the current study, a CFD simulation was performed for the blood flow through a Bileaflet Mechanical Heart Valve (BMHV) with concomitant obstruction in the Left Ventricle Outflow Tract (LVOT). The current study explores the impact of the presence of the subaortic on flow patterns. It also investigates the accuracy of (BMHV) evaluation using Doppler parameters, as proposed in the American Society of Echocardiography (ASE) guidelines.

Phase-contrast magnetic resonance imaging and computational fluid dynamics assessment of thoracic aorta blood flow: a literature review

The death rate from thoracic aortic disease is on the rise and represents a growing global health concern as patients are often asymptomatic before acute events, which have devastating effects on health-related quality of life. Biomechanical factors have been found to play a major role in the development of both acquired and congenital aortic diseases. However, much is still unknown and translational benefits of this knowledge are yet to be seen. Phase-contrast cardiovascular magnetic resonance imaging of thoracic aortic blood flow has emerged as an exceptionally powerful non-invasive tool enabling visualization of complex flow patterns, and calculation of variables such as wall shear stress. This has led to multiple new findings in the areas of phenotype-dependent bicuspid valve flow patterns, thoracic aortic aneurysm formation and aortic prosthesis performance assessment. Phase-contrast cardiovascular magnetic resonance imaging has also been used in conjunction with computational fluid modelling techniques to produce even more sophisticated analyses, by allowing the calculation of haemodynamic variables with exceptional temporal and spatial resolution. Translationally, these technologies may potentially play a major role in the emergence of precision medicine and patient-specific treatments in patients with aortic disease. This clinically focused review will provide a systematic overview of key insights from published studies to date.

Vector Flow Imaging of the Ascending Aorta in Patients with Tricuspid and Bicuspid Aortic Valve Stenosis Treated with Biological and Mechanical Implants

Aortic valve stenosis (AS) is treated with biological prostheses (BPs) and mechanical prostheses (MPs). Vector flow imaging (VFI), an angle-independent ultrasound method, can quantify flow complexity (vector concentration (VC)) and secondary rotation (SR). Ten patients (mean age: 70.7 y) with tricuspid AS scheduled for BPs, 10 patients (mean age: 56.2 y) with bicuspid AS scheduled for MPs and 10 patients (mean age: 63.9 y) with normal aortic valves were scanned intra-operatively on the ascending aorta with VFI and conventional spectral Doppler. Bicuspid AS (peak systolic velocity (PSV): 380.9 cm/s, SR: 16.7 Hz, VC: 0.21) had more complex flow (p < 0.02) than tricuspid AS (PSV: 346.1 cm/s, SR: 17.1 Hz, VC: 0.33). Both groups had more complex and faster flow (p < 0.0001) than normal aortic valve patients (PSV: 124.0 cm/s, SR: 4.3 Hz, VC: 0.79). VC (r = 0.87) and SR (r = 0.89) correlated to PSV. After surgery, flow parameters changed (p < 0.0001) for patients with MPs (PSV: 250.4 cm/s, SR: 9.8 Hz, VC: 0.54) and BPs (PSV: 232.4 cm/s, SR: 12.5 Hz, VC: 0.61), with MPs having slower SR (p < 0.01). None of the implants had normal flow (p < 0.0001). In conclusion, VFI can provide new flow parameters for AS and implant assessment.

Hemodynamic effects of support modes of LVADs on the aortic valve

As the alternative treatment for heart failure, left ventricular assist devices (LVADs) have been widely applied to clinical practice. However, the effects of the support modes of LVADs on the biomechanical states of the aortic valve are still poorly understood. Hence, the present study investigates such effects and proposes a novel fluid-structure interaction (FSI) approach that combines the lattice Boltzmann method (LBM) and finite element (FE) method. Two support modes of LVADs, namely constant speed mode and constant flow mode, which have been widely applied to clinical practice, are also designed. Results demonstrate that the support modes of LVADs could significantly affect the biomechanical states of the aortic valve and the blood flow pattern of the ascending aorta. Compared with those in the constant flow mode, the leaflets in the constant speed mode could achieve better dynamic performance and lower stress during the systolic phase. The max radial displacement of the leaflets in the constant speed mode is at 8 mm, whereas that in the constant flow mode is at 0.8 mm. Furthermore, the outflow of LVADs directly impacts the aortic surfaces of the leaflets during the diastolic phase by increasing the level of wall shear stress of the leaflets. The leaflets in the constant speed mode receive less impact than those in the constant flow mode. The condition with such minimal impact is conducive to maintaining the normal structure of leaflets and benefits the reduction of the risk of valvular diseases. In sum, the support modes of LVADs exert a crucial effect on the biomechanical environment of the aortic valve. The constant speed mode is better than the constant flow mode in terms of providing a good hemodynamic environment for the aortic valve.

In vitro 4D Flow MRI evaluation of aortic valve replacements reveals disturbed flow distal to biological but not to mechanical valves

BACKGROUND AND AIM OF THE STUDY

Aortic hemodynamics influence the integrity of the vessel wall and cardiac afterload. The aim of this study was to compare hemodynamics distal to biological (BV) and mechanical aortic valve (MV) replacements by in vitro 4D Flow MRI excluding confounding factors of in-vivo testing potentially influencing hemodynamics.

METHODS

Two BV (Perimount MagnaEase [Carpentier-Edwards], Trifecta [Abbott]) and two MV (On-X [CryoLife], prototype trileaflet valve) were scanned in a flexible aortic phantom at 3T using a recommended 4D Flow MR sequence. A triphasic aortic flow profile with blood-mimicking fluid was established. Using GTFlow (Gyrotools), area and velocity of the ejection jet were measured. Presence and extent of sinus vortices and secondary flow patterns were graded on a 0 to 3 scale.

RESULTS

A narrow, accelerated central ejection jet (Area = 27 ± 7% of vessel area, Velocity = 166 ± 13 cm/s; measured at sinotubular junction) was observed in BV as compared to MV (Area = 53 ± 13%, Velocity = 109 ± 21 cm/s). As opposed to MV, the jet distal to BV impacted the outer curvature of the ascending aorta and resulted in large secondary flow patterns (BV: n = 4, grades 3, 3, 2, 1; MV: n = 1, grade 1). Sinus vortices only formed distal to MV. Although physiologically configured, they were larger than normal (grade 3).

CONCLUSIONS

In contrast to mechanical valves, biological valve replacements induced accelerated and increased flow patterns deviating from physiological ones. While it remains speculative whether this increases the risk of aneurysm formation through wall shear stress changes, findings are contrasted by almost no secondary flow patterns and typical, near-physiological sinus vortex formation distal to mechanical valves.

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

Aortic valve replacement (AVR) does not usually restore physiological flow profiles. Complex flow profiles are associated with aorta dilatation, ventricle remodeling, aneurysms, and development of atherosclerosis. All these affect long-term morbidity and often require reoperations. In this pilot study, we aim to investigate an ability to optimize the real surgical AVR procedure toward flow profile associated with healthy persons. Four cases of surgical AVR (two with biological and two with mechanical valve prosthesis) with available post-treatment cardiac magnetic resonance imaging (MRI), including four-dimensional flow MRI and showing abnormal complex post-treatment hemodynamics, were investigated. All cases feature complex hemodynamic outcomes associated with valve-jet eccentricity and strong secondary flow characterized by helical flow and recirculation regions. A commercial computational fluid dynamics solver was used to simulate peak systolic hemodynamics of the real post-treatment outcome using patient-specific MRI measured boundary conditions. Then, an attempt to optimize hemodynamic outcome by modifying valve size and orientation as well as ascending aorta size reduction was made. Pressure drop, wall shear stress, secondary flow degree, helicity, maximal velocity, and turbulent kinetic energy were evaluated to characterize the AVR hemodynamic outcome. The proposed optimization strategy was successful in three of four cases investigated. Although no single parameter was identified as the sole predictor for a successful flow optimization, downsizing of the ascending aorta in combination with the valve orientation was the most effective optimization approach. Simulations promise to become an effective tool to predict hemodynamic outcome. The translation of these tools requires, however, studies with a larger cohort of patients followed by a prospective clinical validation study.

User-dependent variability in mitral valve segmentation and its impact on CFD-computed hemodynamic parameters

PURPOSE

While novel tools for segmentation of the mitral valve are often based on automatic image processing, they mostly require manual interaction by a proficient user. Those segmentations are essential for numerical support of mitral valve treatment using computational fluid dynamics, where the reconstructed geometry is incorporated into a simulation domain. To quantify the uncertainty and reliability of hemodynamic simulations, it is crucial to examine the influence of user-dependent variability in valve segmentation.

METHODS

Previously, the inter-user variability of landmarks in mitral valve segmentation was investigated. Here, the inter-user variability of geometric parameters of the mitral valve, projected orifice area (OA) and projected annulus area (AA), is investigated for 10 mitral valve geometries, each segmented by three users. Furthermore, the propagation of those variations into numerically calculated hemodynamics, i.e., the blood flow velocity, was investigated.

RESULTS

Among the three geometric valve parameters, AA was least user-dependent. Almost all deviations to the mean were below 10%. Larger variations were observed for OA. Variations observed for the numerically calculated hemodynamics were in the same order of magnitude as those of geometric parameters. No correlation between variation of geometric parameters and variation of calculated hemodynamic parameters was found.

CONCLUSION

Errors introduced due to the user-dependency were of the same size as the variations of calculated hemodynamics. The variation was thereby of the same scale as deviations in clinical measurements of blood flow velocity using Doppler echocardiography. Since no correlation between geometric and hemodynamic uncertainty was found, further investigation of the complex relationship between anatomy, leaflet shape and flow is necessary.

Hodge decomposition of wall shear stress vector fields characterizing biological flows

A discrete boundary-sensitive Hodge decomposition is proposed as a central tool for the analysis of wall shear stress (WSS) vector fields in aortic blood flows. The method is based on novel results for the smooth and discrete Hodge-Morrey-Friedrichs decomposition on manifolds with boundary and subdivides the WSS vector field into five components: gradient (curl-free), co-gradient (divergence-free) and three harmonic fields induced from the boundary, which are called the centre, Neumann and Dirichlet fields. First, an analysis of WSS in several simulated simplified phantom geometries (duct and idealized aorta) was performed in order to understand the nature of the five components. It was shown that the decomposition is able to distinguish harmonic blood flow arising from the inlet from harmonic circulations induced by the interior topology of the geometry. Finally, a comparative analysis of 11 patients with coarctation of the aorta (CoA) before and after treatment as well as 10 control patients was done. The study shows a significant difference between the CoA patients before and after the treatment, and the healthy controls. This means a global difference between aortic shapes of diseased and healthy subjects, thus leading to a new type of WSS-based analysis and classification of pathological and physiological blood flow.

Artificial Organs 2018: A Year in Review

In this Editor's Review, articles published in 2018 are organized by category and summarized. We provide a brief reflection of the research and progress in artificial organs intended to advance and better human life while providing insight for continued application of these technologies and methods. Artificial Organs continues in the original mission of its founders "to foster communications in the field of artificial organs on an international level." Artificial Organs continues to publish developments and clinical applications of artificial organ technologies in this broad and expanding field of organ Replacement, Recovery, and Regeneration from all over the world. Peer-reviewed special issues this year included contributions from the 13th International Conference on Pediatric Mechanical Circulatory Support Systems and Pediatric Cardiopulmonary Perfusion edited by Dr. Akif Undar, and the 25th Congress of the International Society for Mechanical Circulatory Support edited by Dr. Marvin Slepian. Additionally, many editorials highlighted the worldwide survival differences in hemodialysis and perspectives on mechanical circulatory support and stem cell therapies for cardiac support. We take this time also to express our gratitude to our authors for offering their work to this journal. We offer our very special thanks to our reviewers who give so generously of time and expertise to review, critique, and especially provide meaningful suggestions to the author's work whether eventually accepted or rejected. Without these excellent and dedicated reviewers the quality expected from such a journal could not be possible. We also express our special thanks to our Publisher, John Wiley & Sons for their expert attention and support in the production and marketing of Artificial Organs. We look forward to reporting further advances in the coming years.

Uncertainty Quantification for Non-invasive Assessment of Pressure Drop Across a Coarctation of the Aorta Using CFD

PURPOSE

Numerical assessment of the pressure drop across an aortic coarctation using CFD is a promising approach to replace invasive catheter-based measurements. The aim of this study was to investigate and quantify the uncertainty of numerical calculation of the pressure drop introduced during two essential steps of medical image processing: segmentation of the patient-specific geometry and measurement of patient-specific flow rates from 4D-flow-MRI.

METHODS

Based on the baseline segmentation, geometries with different stenosis diameters were generated for a sample of ten patients. The pressure drop generated by these geometries was calculated for different volume flow rates using computational fluid dynamics. Based on these simulations, a second order polynomial fit was calculated. Based on these polynomial fits an uncertainty of pressure drop calculation was quantified.

RESULTS

The calculated pressure drop values varied strongly between the patients. In four patients, pressure drops above and below the clinical threshold of 20 mmHg were found. The median standard deviation of the pressure drop was 2.3 mmHg. The sensitivity of the pressure drop toward changes in the volume flow rate or the stenosis geometry varied between patients.

CONCLUSION

The uncertainty of numerical pressure drop calculation introduced by uncertainties during image segmentation and measurement of volume flow rates was comparable to the uncertainty of pressure drop measurements using invasive catheterization. However, in some patients this uncertainty would have led to different treatment decision. Therefore, patient-specific uncertainty assessment might help to better understand the reliability of a numerically calculated biomarker as the pressure drop across an aortic coarctation.

Model-Based Therapy Planning Allows Prediction of Haemodynamic Outcome after Aortic Valve Replacement

Optimizing treatment planning is essential for advances in patient care and outcomes. Precisely tailored therapy for each patient remains a yearned-for goal. Cardiovascular modelling has the potential to simulate and predict the functional response before the actual intervention is performed. The objective of this study was to proof the validity of model-based prediction of haemodynamic outcome after aortic valve replacement. In a prospective study design virtual (model-based) treatment of the valve and the surrounding vasculature were performed alongside the actual surgical procedure (control group). The resulting predictions of anatomic and haemodynamic outcome based on information from magnetic resonance imaging before the procedure were compared to post-operative imaging assessment of the surgical control group in ten patients. Predicted vs. post-operative peak velocities across the valve were comparable (2.97 ± 1.12 vs. 2.68 ± 0.67 m/s; p = 0.362). In wall shear stress (17.3 ± 12.3 Pa vs. 16.7 ± 16.84 Pa; p = 0.803) and secondary flow degree (0.44 ± 0.32 vs. 0.49 ± 0.23; p = 0.277) significant linear correlations (p < 0.001) were found between predicted and post-operative outcomes. Between groups blood flow patterns showed good agreement (helicity p = 0.852, vorticity p = 0.185, eccentricity p = 0.333). Model-based therapy planning is able to accurately predict post-operative haemodynamics after aortic valve replacement. These validated virtual treatment procedures open up promising opportunities for individually targeted interventions.