Cardiovascular diseases are the main cause of death worldwide. The myocardial bride is one of the congenital abnormalities while part of the coronary artery is placed under the myocardium instead of resting on top of it. The myocardial bridge can be asymptomatic during adult life or can cause many cardiovascular consequences such as angina, myocardial ischemia, acute coronary syndrome, left ventricular dysfunction, arrhythmia, and even sudden cardiac death. Therefore, the investigation of the presented abnormality can significantly influence its understanding.

The presented research focuses on the numerical modelling of the blood flow through the myocardial bridge section. Computer modelling is a useful tool to assess the risk factors influenced by blood flow disturbances in arteriosclerosis disease development. The mentioned factors are mainly connected with the low shear stress affected blood vessels accompanied by high oscillations of the blood flow in specific regions. In the case of the myocardial bridge, the blood vessel movement and its contraction can significantly influence the results of the flow calculations. Therefore, numerical models should include the simulated arteries' geometry changes, which is a challenging model development. It can be realized by the dynamic mesh approach accounting for the dynamic discretization of the investigated domain during the calculations. The presented research describes two methods of mesh motion defining in the commercial CFD software (ANSYS Fluent) extended by additional user defined functions. The first is based on the shape reconstruction from the camera records collected in in-vitro conditions on the blood vessel phantom. Results from the model and experimental campaign are compared including shape, pressures and flow in the blood vessel phantom. The pressure range was in this case at the level of approx. 120/80 mmHg. The consistency between those results was at a satisfactory level. The second mesh motion deformation was based on the patient's medical images collected in in-vivo conditions and processed by auxiliary software (ANTS) performing advanced image transformation based on diffeomorphism. The obtained results from those two methods pointed out the validity of the dynamic domain definition. The relevant volume change of the vessel during the heart cycle also influences the assumed boundary conditions introduced to solve governing equations covering specifically the mass conservation principle. The dynamic shape reconstruction in the CFD model was compared with the instant segmentation data showing satisfactory agreement.

CFD is a noninvasive technique that could be applied to clinical practice to predict the blood flow in the coronary arteries. Including the vessel shape dynamics from medical images in the CFD models increase those models' accuracy.

Acknowledgements: This research is supported by National Science Centre (Poland) project No. 2017/27/B/ST8/01046 and project No. 2019/34/H/ST8/00624. This help is gratefully acknowledged.

Coil embolization is performed to treat intracranial aneurysms. During the follow-up after coil embolization, a recurrence of the aneurysm called recanalization may occur due to blood inflow. Therefore, it is expected that risk prediction of aneurysmal recanalization will enable effective treatment planning. Recently, computational fluid dynamics (CFD) has been conducted to identify the risk factors of recanalization. Although most of the previous studies assumed that blood is a Newtonian fluid, actual blood is a non-Newtonian fluid. Especially, the shear rate in coil embolized aneurysms becomes so low that the effect of non-Newtonian fluid is considered to be significant. In this study, we conducted CFD and statistical analysis to investigate the effect of non-Newtonian fluid properties on the prediction of aneurysmal recanalization. Our target of the analysis was basilar tip aneurysms. We identified 31 aneurysms including 4 recanalized aneurysms and 27 stable aneurysms. In this study, we defined recanalization as the case that recanalized less than 2 years after the first coil embolization. On the other hand, aneurysms that remained stable for more than 2 years after the embolization were defined as stable. We applied Newtonian and non-Newtonian blood models for these cases in the CFD analysis. We modeled the non-Newtonian blood using a modified Casson model based on the results of viscosity measurements on 12 patients with aneurysms. A porous media model was applied to model the embolized coil mass. We defined 33 hemodynamic and 3 morphological parameters. For each hemodynamic parameter, the value before (BF) and after (AF) coil embolization was obtained, and their reduction ratio (RR) was also calculated. We focused in particular on the V_Neck and the V_Dome, which indicate the dimensionless velocity on the neck plane in the aneurysm, respectively. In addition, the spatial mean, maximum, and minimum values were denoted as Ave, Max, and Min, respectively. Univariate logistic regression analysis was performed to compare each parameter between recanalized and stable cases. In addition, ROC analysis was also performed for parameters that showed statistically significant differences (P < 0.05). The sensitivity, specificity, and area under the curve (AUC) were evaluated among the parameters. As a result, by considering non-Newtonian fluid properties, V_{Neck_Ave_RR}, V_{Neck_Max_RR}, and V_{Dome_Ave_RR} newly indicated statistically significant differences between the recanalized and stable cases (i.e., the parameters that showed statistically significant differences in the Newtonian fluid also showed statistically significant differences in the non-Newtonian fluid). In addition, although the highest sensitivity (=0.75) and AUC (=0.84) were obtained with the V_{Dome_Ave_RR} when we assumed Newtonian fluid, the higher sensitivity (=1.00) and AUC (=0.89) were achieved when the non-Newtonian model was applied. The results showed that considering the non-Newtonian properties increased the sensitivity and AUC of recanalization prediction. In conclusion, non-Newtonian fluid properties changed the parameters that showed statistically significant differences between recanalization and stable cases. Furthermore, the V_{Dome_Ave_RR} obtained the highest accuracy in all researched parameters assuming Newtonian and Non-Newtonian fluid. The present result implies that the introduction of non-Newtonian fluid properties can improve the prediction accuracy of recanalization in coil embolized aneurysms.

Understanding cardiac sympathovagal balance and its relationship to the degree of reinnervation after heart transplantation has been a longstanding challenge in medicine. While autonomic markers derived from cardiac rhythms are often used to assess cardiac sympathovagal balance, their relationship to reinnervation has not been fully explored. In this study, we used a mathematical model of the human cardiovascular system and its autonomic control to explore the influence of varying levels of vagal and sympathetic cardiac reinnervation on autonomic cardiac markers.

To investigate this relationship, we applied a mathematical model of the human cardiovascular system and its autonomic control. The model integrates the chronotropic and inotropic effects of the arterial baroreflex and pulmonary stretch reflex, which are the main contributors to heart rate variability. The work focused on markers that are commonly used to assess cardiac sympathovagal balance, including resting heart rate, bradycardic and tachycardic heart rate response to the Valsalva maneuver, root mean square error of normalized RR-intervals (RMSDD), high-frequency (HF) power, low-frequency (LF) power, and total power of the heart rate variability spectrum. To evaluate the strength of the relationship between the level of cardiac reinnervation and the respective markers we calculated Spearman's rank correlation coefficients.

Results showed that for assessing vagal cardiac reinnervation levels above 20%, resting heart rate, RMSDD, and total spectral power may be equally suitable as the commonly used measure of HF power. The strength of the correlation between these markers and vagal reinnervation was found to be very high, with correlation coefficients of ρ=0.99, ρ=0.97, and ρ=0.89, respectively (all p<0.05). Concerning sympathetic reinnervation, simulations suggest that LF/HF-ratio and tachycardic response to the Valsalva maneuver may be more suitable than the regularly used measure of LF-power. The strength of the correlation between these markers and sympathetic reinnervation was high: ρ=0.88 and ρ=0.84, respectively (both p<0.05). These findings suggest that there are differences in the performance of cardiac autonomic markers in assessing vagal and sympathetic reinnervation and that some markers may be more suitable than others depending on the level of reinnervation.

The developed mathematical model can provide critical insights into the genesis of autonomic cardiac markers and their relationship to cardiac reinnervation. Furthermore, it suggests strategies for designing and interpreting future clinical studies, which would then provide clinical evidence for the current findings and allow more accurate evaluation of cardiac (re)innervation levels after heart transplantation. Overall, the findings of this model study might have important implications for the assessment and management of cardiovascular disease and highlight the potential of mathematical models to enhance our understanding of complex physiological systems.

This work was funded by the European Project H2020-EU.1.2.2. “A neuroprosthesis to restore the vagal-cardiac closed-loop connection after heart transplantation, NeuHeart” (Grant agreement ID: 824071).

Objectives: A standard treatment for severe Aortic Stenosis (AS) is surgical valve replacement with biological prostheses. Fluid-structure-interaction (FSI) simulations are a powerful tool for studying hemodynamic valve behavior and thus optimize function, geometry and durability particularly of prosthetic aortic valves. In this study, an FSI simulation of a bioprosthetic bovine pericardium aortic valve was established, which incorporated an anisotropic hyperelastic material model for the valve leaflets.

Methods: A valve-specific geometric model from a µCT scan (voxel size 31µm) of the 21 mm Avalus™ Bioprosthesis (Model 400, Medtronic, Ireland) was obtained. An anisotropic hyperelastic material model was used, a first order Ogden model for the isotropic matrix and an exponentially-based strain energy function for embedded hyperelastic fibers representing collagen fibers in circumferential leaflet direction. The valve structure was then coupled with a fluid domain consisting of a tube with 24mm diameter as inlet, the sinuses of Valsalva and a tube with 30mm diameter as outlet. Blood was assumed to be a Newtonian fluid. The inlet pressure boundary condition was chosen to represent a clinically determined transvalvular pressure gradient during a cardiac cycle, while the outlet pressure boundary condition was set to zero. The valve hemodynamic behavior was analyzed by determining the valve’s effective orifice area, leaflet fluttering dynamics during systole and fluid velocities.

Results: The composite valve model was successfully implemented in the FSI simulations performed on the Vienna Scientific Cluster supercomputer. The simulated effective orifice area at mid-systole was 1.6 cm². Systolic leaflet fluttering – which is associated with fatigue and failure of bioprosthetic leaflets over time – was most pronounced at beginning of systole, with amplitudes up to 2.5 mm. However, during the course of systole, the leaflets stabilized, resulting in smaller fluttering amplitudes but higher fluttering frequencies (up to the simulation output sampling rate of 200 Hz). Peak velocities in the free jet during systole went up to 3.0 m/s.

Conclusions: A hyperelastic anisotropic leaflet material was successfully integrated in an aortic valve model and used for FSI analysis. The model is being refined and validated with experimental flow tests. After validation the influence of differently sized valves on the valve hydrodynamics will be investigated together with correlations of simulation results with clinical outcomes.

An intracranial aneurysm is a cerebrovascular disease that swells of bulges abnormally in the wall of intracranial arteries. Rupture of aneurysms cause subarachnoid hemorrhage with high mortality. Unruptured aneurysms occasionally have Thin-Walled Regions (TWRs) where the wall thickness is thinner than the surrounding areas. Although TWRs in intracranial aneurysms have a risk of rupture, imaging modalities cannot evaluate the thickness of the aneurysm wall. The surgical treatment will be able to perform safely by identifying TWRs before the treatment. TWRs have been related to hemodynamic factors. Although Computational Fluid Dynamics (CFD) analysis has been applied to identify the hemodynamic characteristics around TWRs, few previous studies have focused on the difference in hemodynamic factors between TWRs and non-TWRs by quantitative definition of TWRs. The purpose of this study is to investigate the hemodynamic factors involved in TWRs by comparing the results in TWRs and non-TWRs by CFD and image analysis. We identified 100 aneurysms (middle cerebral artery: 78, anterior cerebral artery: 20, internal carotid artery: 2) treated with craniotomy and clipping. Unsteady CFD analysis was performed with the inlet boundary condition imposed on the mean mass flow pulsations measured from healthy adults, the outlet boundary condition fixed at a static pressure of 0 Pa, and the no-slip wall boundary. We evaluate the pressure difference (PD), wall shear stress (WSS), and wall shear stress divergence (WSSD) on the aneurysm wall. All parameters are normalized by the dynamic pressure at the aneurysm inlet. Since TWRs generally indicate intense red, the comprehensive Red (cR) value was defined by the RGB color model to evaluate the redness. The cR value was calculated for each pixel of the intraoperative images, and TWRs were defined using the cR value. We extracted the TWRs in the three-dimensional geometry of the aneurysm and the other region of the aneurysm wall was defined as non-TWRs. The mean values of each parameter in TWRs and non-TWRs were calculated and compared between the two groups using Mann-Whitney’s U test. As a result, the mean PD of all cases was 0.0688 in TWRs and -0.0278 in non-TWRs, the mean WSS was 0.0510 in TWRs and 0.0439 in non-TWRs, and the mean WSSD was 0.0162 in TWRs and 0.0014 in non-TWRs, respectively. These three parameters were statistically significantly higher for TWRs than non-TWRs (P<0.05). The higher PD of the TWRs is thought to be caused by the vertical stress on the aneurysm wall due to flow impingement on the aneurysm wall. We considered that high friction against the aneurysm wall is related to high WSS in the TWRs, resulting in a decrease the number of endothelial cells in the wall and the thin-wall. The WSSD was high in the TWRs because the tensile forces on the aneurysm wall may lead to the thin-wall. In conclusion, our results suggested that these hemodynamic parameters are related to TWRs. By conducting a CFD analysis on each patient and examining the areas with high PD, WSS, and WSSD values, it may be possible to identify TWRs before treatment.

Introduction

Image-based computational models of cardiac electromechanics (EM) are a powerful tool to understand the
mechanisms underlying physiological and pathological conditions in cardiac function and to improve diagnosis and therapy planning. To realize such advanced applications methodological key challenges must be addressed. First, enhanced computational efficiency and robustness is crucial to facilitate model personalization and the simulation of prolonged observation periods under a broad range of conditions. Second, physiological completeness encompassing therapy-relevant mechanisms is needed to endow models with predictive capabilities beyond the mere replication of observations.​​

Methods

In this talk, we report on a universal cardiac EM modeling framework that builds on a flexible method for
coupling a 3D model of cardiac EM to the physiologically comprehensive 0D CircAdapt model representing
closed-loop circulation. Additionally, we present recent advances in EM cardiac model personalization. In
particular, we focus on the identification of passive cardiac properties. Here, we present a novel methodology to simultaneously perform an automated identification of in-vivo passive mechanical properties and an estimation of the unloaded reference configuration.

Results

We report on the efficiency, robustness, and accuracy of the numerical scheme and solver implementation and show the model’s ability to replicate physiological behaviors by simulating the transient response to alterations in loading conditions and contractility, as induced by experimental protocols used for assessing systolic and diastolic ventricular properties. Further, we demonstrate the applicability of the framework to several clinically relevant problems.

Conclusion

The mechanistic completeness and efficiency of the framework renders advanced EM modeling applications feasible. The model facilitates the efficient and robust exploration of parameter spaces over prolonged observation periods which is pivotal for personalizing models to closely match observations. Moreover, the model can be trusted to provide predictions of the acute transient response to interventions or therapies altering loading conditions and contractility.

Coronary artery disease is a chronic disease in which the blood vessels are blocked, as a result of the growth of atherosclerotic plaques inside the arteries. To treat atherosclerosis, different clinical treatments are employed, including percutaneous coronary intervention (PCI). PCI includes the insertion of a stent, a metallic scaffold, its placement and expansion inside the diseased artery and the restoration of the blood flow. For a stent to be released to the market, its performance in terms of safety and efficacy is initially tested and evaluated, in in vitro, animal studies and clinical trials. However, this process is time consuming and costly, and there are certain limitations, such as the onset of severe complications and adverse events during clinical studies, which can be addressed by the exploitation of the very promising in-silico approaches. The fast-emerging technology of in-silico technologies is considered as an effective means for providing answers to “What if” questions. Among the available computational methods, the application of finite element method has proven to be the “method of choice” in replicating the biomechanical response of stents, by modelling their deployment in diseased, idealized or realistic, arteries. Structural simulations of stent implantation allow the evaluation of quantities of interest which could be difficult or even impossible to be observed through other means. By using models of coronary arteries and different stents or stenting techniques, the analysis of several geometrical (e.g., size of atherosclerotic plaques, malapposition, stent cell size, minimum lumen area, etc.) and mechanical quantities (type of atherosclerotic plaques, stent stress/strain, arterial stress/strain, etc.) can be performed. Such models provide useful insights into several aspects of stent design, towards of a final stent configuration for optimal post-implantation outcomes. In this work, a finite element analysis is performed to simulate the deployment of a commercially available stent inside a reconstructed patient specific artery. In more detail, for the reconstruction of the arterial wall and the underlying atherosclerotic plaque components, Optical Coherence Tomography and Invasive Coronary Angiography imaging are used, and an in-house 3D reconstruction software tool is employed. The next step involves the design of the 3D stent and its positioning in the stenosed artery. Then by the application of appropriate boundary conditions in the arterial-stent model and the implementation of a pressure driven approach, the in-silico expansion of the stent is achieved. For the finite model, representative material properties and material models are used. This study focuses on investigating the effect of the material properties of the underlying arterial wall and atherosclerotic plaques (fibrous, lipid, calcified) for providing guidance in appropriate material selection in stent modeling approaches towards improving stent design and performance. In more detail, we investigate the stresses, strains and deformations caused in the scaffold and the arterial wall considering the calcified plaques stiffness variance.