The heart is the first functional organ in a developing embryo. Cardiac development continues throughout developmental stages while the heart goes through a serious of drastic morphological changes. Previous animal experiments as well as clinical observations showed that disturbed hemodynamics interfere with the development of the heart and leads to the formation of a variety of defects in heart valves, heart chambers, and blood vessels, suggesting that hemodynamics is a governing factor for cardiogenesis, and disturbed hemodynamics is an important source of congenital heart defects. Therefore, there is an interest to image and quantify the flowing blood through a developing heart. Flow measurement in embryonic fetal heart can be performed using advanced techniques such as magnetic resonance imaging (MRI) or echocardiography. Computational fluid dynamics (CFD) modeling is another approach especially useful when the other imaging modalities are not available and in-depth flow assessment is needed. The approach is based on numerically solving relevant physical equations to approximate the flow hemodynamics and tissue behavior. This approach is becoming widely adapted to simulate cardiac flows during the embryonic development. While there are few studies for human fetal cardiac flows, many groups used zebrafish and chicken embryos as useful models for elucidating normal and diseased cardiogenesis. In this paper, we explain the major steps to generate CFD models for simulating cardiac hemodynamics in vivo and summarize the latest findings on chicken and zebrafish embryos as well as human fetal hearts.

Aortic valve calcification is an important cardiovascular disorder that deteriorates the accurate functioning of the valve leaflets. The increasing stiffness due to the calcification prevents the complete closure of the valve and therefore leads to significant hemodynamic alterations. Computational fluid dynamics (CFD) modeling enables the investigation of the entire flow domain by processing medical images from aortic valve patients. In this study, we computationally modeled and simulated a 3D aortic valve using patient-specific dimensions of the aortic root and aortic sinus. Leaflet stiffness is deteriorated in aortic valve disease due to calcification. In order to investigate the influence of leaflet calcification on flow dynamics, three different leaflet-stiffness values were considered for healthy, mildly calcified, and severely calcified leaflets. Time-dependent CFD results were used for applying the Lagrangian coherent structures (LCS) technique by performing finite-time Lyapunov exponent (FTLE) computations along with Lagrangian particle residence time (PRT) analysis to identify unique vortex structures at the front and backside of the leaflets. Obtained results indicated that the peak flow velocity at the valve orifice increased with the calcification rate. For the healthy aortic valve, a low-pressure field was observed at the leaflet tips. This low-pressure field gradually expanded through the entire aortic sinus as the calcification level increased. FTLE field plots of the healthy and calcified valves showed a variety of differences in terms of flow structures. When the number of fluid particles in the healthy valve model was taken as reference, 1.59 and 1.74 times more particles accumulated in the mildly and severely calcified valves, respectively, indicating that the calcified valves were not sufficiently opened to allow normal mass flow rates.

Calcification and bicuspid valve formation are important aortic valve disorders that disturb the hemodynamics and the valve function. The detailed analysis of aortic valve hemodynamics would lead to a better understanding of the disease’s etiology. We computationally modeled the aortic valve using simplified three-dimensional geometry and inlet velocity conditions obtained via echocardiography. We examined various calcification severities and bicuspid valve formation. Fluid-structure interaction (FSI) analyses were adapted using ANSYS Workbench to incorporate both flow dynamics and leaflet deformation accurately. Simulation results were validated by comparing leaflet movements in B-mode echo recordings. Results indicate that the biomechanical environment is significantly changed for calcified and bicuspid valves. High flow jet velocities are observed in the calcified valves which results in high transvalvular pressure difference (TPG). Wall shear stresses (WSS) increased with the calcification on both fibrosa (aorta side) and ventricularis (left ventricle side) surfaces of the leaflet. The WSS distribution is regular on the ventricularis, as the WSS values proportionally increase from the base to the tip of the leaflet. However, WSS patterns are spatially complex on the fibrosa side. Low WSS levels and spatially complex WSS patterns on the fibrosa side are considered as promoting factors for further calcification and valvular diseases.

Abdominal aortic aneurysm (AAA) is a critical health disorder, where the abdominal aorta dilates more than 50% of its normal diameter. Enlargement in abdominal aorta alters the hemodynamics and flow-induced forces on the AAA wall. Depending on the flow conditions, the hemodynamic forces on the wall may result in excessive mechanical stresses that lead to AAA rupture. The risk of rupture can be predicted using advanced computational techniques such as computational fluid dynamics (CFD) and fluid–structure interaction (FSI). For a reliable rupture risk assessment, formation of intraluminal thrombus (ILT) and uncertainty in arterial material properties should be taken into account, mainly due to the patient-specific differences and unknowns in AAAs. In this study, AAA models are computationally investigated by performing CFD simulations combined with FSI analysis. Various levels of ILT burdens are artificially generated in a realistic AAA geometry, and the peak effective stresses are evaluated to elucidate the effect of material models and ILT formation. The results indicate that increasing the ILT burden leads to lowered effective stresses on the AAA wall. The material properties of the artery and ILT are also effective on the stresses; however, these effects are limited compared to the effect of ILT volume in the AAA sac.

Research on abdominal aortic aneurysms (AAAs) primarily focuses on developing a clear understanding of the initiation, progression, and treatment of AAA through improved model accuracy. High-fidelity hemodynamic and biomechanical predictions are essential for clinicians to optimize preoperative planning and minimize therapeutic risks. Computational fluid dynamics (CFDs), finite element analysis (FEA), and fluid-structure interaction (FSI) are widely used to simulate AAA hemodynamics and biomechanics. However, the accuracy of these simulations depends on the utilization of realistic and sophisticated boundary conditions (BCs), which are essential for properly integrating the AAA with the rest of the cardiovascular system. Recent advances in machine learning (ML) techniques have introduced faster, data-driven surrogates for AAA modeling. These approaches can accelerate segmentation, predict hemodynamics and biomechanics, and assess disease progression. However, their reliability depends on high-quality training data derived from CFDs and FEA simulations, where BC modeling plays a crucial role. Accurate BCs can enhance ML predictions, increasing the clinical applicability. This paper reviews existing BC models, discussing their limitations and technical challenges. Additionally, recent advancements in ML and data-driven techniques are explored, discussing their current states, future directions, common algorithms, and limitations.

Atherosclerosis usually affects the entire cardiovascular system, including peripheral blood vessels. Peripheral arterial stenosis may indicate possible serious vascular disorders related to more vital organs. If peripheral arterial stenosis can be discerned at an early stage, it can serve as a warning sign to take precautions, such as using more invasive diagnostic techniques or adopting a healthier life style. In this study, peripheral regions, such as the thigh, upper arm, and neck are modelled considering stenosis of their major arteries. Stenosis generates a fluctuating pressure field on the arterial wall, which leads to vibration on the skin's surface. This stenosis-induced pressure field is modelled as a harmonic load and applied to the inner surface of the arterial structure. The vibration response on bare skin is computationally determined using the superposition of modal responses. Realistic geometries and hyperelastic material properties are used in modelling the layers of skin, fat, muscle, and bones. The results indicate that stenosis severities higher than 70% lead to a considerable increase in vibration-response amplitudes, especially at frequencies greater than 250 Hz. The detailed analysis of skin responses provides useful information to detect the stenosis location, where the sum of the vibration amplitudes attains its maximum value around the stenosis.