Impact of Patient-Specific Inflow Velocity Profile on Hemodynamics of the Thoracic Aorta

Generation of personalized synthetic 3-dimensional inlet velocity profiles for computational fluid dynamics simulations of type B aortic dissection

BACKGROUND

Computational fluid dynamics (CFD) simulations have shown promise in assessing type B aortic dissection (TBAD) to predict disease progression, and inlet velocity profiles (IVPs) are essential for such simulations. To truly capture patient-specific hemodynamic features, 3D IVPs extracted from 4D-flow magnetic resonance imaging (4D MRI) should be used, but 4D MRI is not commonly available.

METHOD

A new workflow was devised to generate personalized synthetic 3D IVPs that can replace 4D MRI-derived IVPs in CFD simulations. Based on 3D IVPs extracted from 4D MRI of 33 TBAD patients, statistical shape modelling and principal component analysis were performed to generate 270 synthetic 3D IVPs accounting for specific flow features. The synthetic 3D IVPs were then scaled and fine-tuned to match patient-specific stroke volume and systole-to-diastole ratio. The performance of personalized synthetic IVPs in CFD simulations was evaluated against patient-specific IVPs and compared with parabolic and flat IVPs.

RESULTS

Our results showed that the synthetic 3D IVP was sufficient for faithful reproduction of hemodynamics throughout the aorta. In the ascending aorta (AAo), where non-patient-specific IVPs failed to replicate in vivo flow features in previous studies, the personalized synthetic IVP was able to match not only the flow pattern but also time-averaged wall shear stress (TAWSS), with a mean TAWSS difference of 5.9 %, which was up to 36.5 % by idealized IVPs. Additionally, the predicted retrograde flow index in both the AAo (8.36 %) and descending aorta (8.17 %) matched closely the results obtained with the 4D MRI-derived IVP (7.36 % and 6.55 %). The maximum false lumen pressure difference was reduced to 11.6 % from 68.8 % by the parabolic IVP and 72.6 % by the flat IVP.

CONCLUSION

This study demonstrates the superiority of personalized synthetic 3D IVPs over commonly adopted parabolic or flat IVPs and offers a viable alternative to 4D MRI-derived IVP for CFD simulations of TBAD.

In Silico, Patient-Specific Assessment of Local Hemodynamic Predictors and Neointimal Hyperplasia Localisation in an Arteriovenous Graft

PURPOSE

Most computational fluid dynamics (CFD) studies on arteriovenous grafts (AVGs) adopt idealised geometries and simplified boundary conditions (BCs), potentially resulting in misleading conclusions when attempting to predict neointimal hyperplasia (NIH) development. Moreover, they often analyse a limited range of hemodynamic indices, lack verification, and fail to link the graft-altered hemodynamics with follow-up data. This study develops a novel patient-specific CFD workflow for AVGs using pathophysiological BCs. It verifies the CFD results with patient medical data and assesses the co-localisation between CFD results and NIH regions at follow-up.

METHODS

Contrast-enhanced computed tomography angiography images were used to segment the patient's AVG geometry. A uniform Doppler ultrasound (DUS)-derived velocity profile was imposed at the inlet, and three-element Windkessel models were applied at the arterial outlets of the domain. Transient, rigid-wall simulations were performed using the k-ω SST turbulence model. The CFD-derived flow waveform was compared with the patient's DUS image to ensure verification. Turbulent kinetic energy (TKE), helicity and near-wall hemodynamic descriptors were calculated and linked with regions presenting NIH from a 4-month follow-up fistulogram.

RESULTS

In the analysed patient, areas presenting high TKE and balanced helical flow structures at baseline exhibit NIH growth at follow-up. Transverse wall shear stress index is a stronger predictor of NIH than other commonly analysed near-wall hemodynamic indices, since luminal areas subjected to high values greatly co-localise with observed areas of remodelling.

CONCLUSION

This patient-specific computational workflow for AVGs could be applied to a larger cohort to unravel the link between altered hemodynamics and NIH progression in vascular access.

Guiding patient-specific cardiac simulations through data-assimilation of soft tissue kinematics from dynamic CT scan

Fluid-structure interaction (FSI) can be key in the generation of accurate digital replica of cardiovascular systems. To personalize these models, however, several patient-specific parameters need to be measured, which can be challenging to accomplish in a non-invasive manner. Alternatively, the cardiac kinematics of the patient can be extracted from imaging data and then directly imposed as a dynamic boundary condition in the computational model, also incorporating temporal and spatial measurement errors. A more advanced method combines FSI with kinematic driven simulations using data-assimilation. Despite its potential, the application of this technique to complex multi-physics cardiovascular simulations remains limited. In this study, we develop an FSI model of a patient's left ventricle (LV) and aorta, personalized with dynamic imaging data using a Nudging algorithm-a data assimilation technique-which is tailored to each cardiac chamber. In particular, for the LV, which embeds small-scale and irregular endocardial structures (higher measurement errors), the active contraction of the patient is replicated primarily using integral measurements (ventricular volume and surface area). On the other hand, the passive motion of the aorta is guided in the simulation relying directly on the local tissue positions from CT scan. The algorithm's simplicity and zero additional computational cost make it particularly suitable for multi-physics problems. Our results show that the assimilation procedure must be tuned to guide the system toward the measurements within the uncertainty range of the in-vivo data.

Modeling Techniques and Boundary Conditions in Abdominal Aortic Aneurysm Analysis: Latest Developments in Simulation and Integration of Machine Learning and Data-Driven Approaches

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.

A computational and experimental study of veno-arterial extracorporeal membrane oxygenation in cardiogenic shock: defining the trade-off between perfusion and afterload

Veno-Arterial Extracorporeal Membrane Oxygenation (VA-ECMO) is a type of mechanical circulatory support used, among others, in case of cardiogenic shock, consisting in percutaneous cannulation of the femoral artery. Despite the widespread use of this procedure in clinical practice, a deeper understanding of the complex interaction between native and ECMO output, as well as the fluid dynamics and perfusion of aorta and its branches is still required. Herein, a numerical and experimental approach is presented to model a VA-ECMO procedure on a patient-specific aortic geometry. For both approaches, cardiogenic shock was modeled by considering three different severities of left ventricular failure (mild, moderate, and severe), corresponding to a reduction in cardiac output of 30%, 50%, and 70% relative to the healthy condition, respectively. For each case, different levels of the ECMO support were simulated, ranging from 0 to 6 l/min. The performance of the VA-ECMO configuration was evaluated in terms of both afterload increase and flow at all aortic branches. Both methods highlighted the afterload increase in high levels of ECMO support. Furthermore, numerical and experimental data revealed the existence of a trade-off level of ECMO support that guarantees healthy perfusion of all vessels with the lowest afterload. This correlation opened a pathway for the definition of a tool for determining a suitable level of ECMO support on the basis of the knowledge of patient-specific data.

Computational fluid dynamics modelling of hemodynamics in aortic aneurysm and dissection: a review

Hemodynamic analysis based on computational fluid dynamics (CFD) modelling is expected to improve risk stratification for patients with aortic aneurysms and dissections. However, the parameter settings in CFD simulations involve considerable variability and uncertainty. Additionally, the exact relationship between hemodynamic features and disease progression remains unclear. These challenges limit the clinical application of aortic hemodynamic models. This review presents a detailed overview of the workflow for CFD-based aortic hemodynamic analysis, with a focus on recent advancements in the field. We also conducted a systematic review of 27 studies with large sample sizes (n > 5) that examine the hemodynamic characteristics of aortic aneurysms and dissections. Some studies identified consistent relationships between hemodynamic features and disease progression, reinforcing the potential for clinical application of aortic hemodynamic models. However, limitations such as small sample sizes and oversimplified patient-specific models remain. These findings emphasize the need for larger, more detailed studies to refine CFD modelling strategies, strengthen the connection between hemodynamics and diseases, and ultimately facilitate the clinical use of aortic hemodynamic models in disease management.

Investigation of hemodynamic bulk flow patterns caused by aortic stenosis using a combined 4D Flow MRI-CFD framework

Aortic stenosis (AS) leads to alterations of supra-valvular flow patterns which can cause increased damage of red blood cell (RBC) membranes. We investigated these patient specific patterns of a severe AS patient and their reversal in healthy flow through a 4D Flow MRI-based CFD methodology. Computational models of subject-specific aortic geometries were created using in-vivo medical imaging data. Temporally and spatially resolved boundary conditions derived from 4D Flow MRI were implemented for an AS patient and a healthy subject. After validation of the in-silico results with in-vivo data, a healthy inflow profile was set for the AS patient in the CFD model. Pathological versus healthy flow fields were compared regarding their blood flow characteristics, i.e., shear stresses on RBCs and helicity. The accuracy of the 4D Flow MRI-based CFD model was proven with excellent agreement between in-vivo and in-silico velocity fields and R² = 0.9. A pathological high shear stress region in the bulk flow was present during late systole with an increase of 125% compared to both healthy flow. The physiological bihelical structure with predominantly right-handed helices vanished for the pathological state. Instead, a left-handed helix appeared, accompanied by an overall increase in turbulent kinetic energy in areas of accumulated left-handed helicity. The validated 4D Flow MRI-based CFD model identified marked differences between AS and healthy flow. It suggests that altered turbulent and helical structures in the bulk flow are the cause for increased, potentially damaging forces acting upon RBCs in AS.

Computational Fluid Dynamics-Driven Comparison of Endovascular Treatment Strategies for Penetrating Aortic Ulcer

Background: Penetrating aortic ulcer (PAU) is an acute aortic syndrome characterized by a high rupture risk. There are several PAU-treatment procedures indicated for the management of this pathology associated with different effects on vessel morphology and hemodynamics. A deep evaluation of the different types of treatment may be helpful in decision making. Computational Fluid Dynamics (CFD) is a powerful tool for detailed inspection of cardiovascular diseases. The aim of this work was to implement a comparative analysis based on CFD evaluation of the effects of two type of PAU treatments. Methods: Thoracic endovascular aortic repair (TEVAR) with a left subclavian artery (LSA) branched aortic endograft (SBSG) and a hybrid approach including TEVAR and carotid-LSA bypass were considered. Aortic anatomical models were created from computed tomography (CT) images acquired before and after PAU treatment with SBSG for three patients. Starting from these models, a new aortic geometry corresponding to the outcome of the hybrid strategy was generated. Morphological analysis and CFD simulations were carried out for all aortic models to evaluate LSA outflow for the same predefined boundary conditions. Results: Reductions in LSA diameter were found between aortic models before and after the SBSG (18.2%, 20.8%, and 12.4% for CASE 1, CASE 2, and CASE 3, respectively). The flow rate at LSA changed between pre-configuration and aortic configuration after the PAU treatments: an averaged decrement of 1.08% and 7.5% was found for SBSG and the hybrid approach, respectively. The larger increase in pressure drop between the aortic arch and the LSA extremity was shown in the hybrid approach for all cases. Conclusions: CFD simulations suggest that SBSG preserves LSA perfusion more than a hybrid strategy and has less impact on thoracic aorta hemodynamics.

A Comprehensive Review on Computational Analysis, Research Advances, and Major Findings on Abdominal Aortic Aneurysms for the Years 2021 to 2023

BACKGROUND

Abdominal aortic aneurysm (AAA) is a pathological condition characterized by the dilation of the lower part of the aorta, where significant hemodynamic forces are present. The prevalence and high mortality risk associated with AAA remain major concerns within the scientific community. There is a critical need for extensive research to understand the underlying mechanisms, pathophysiological characteristics, and effective detection methods for abdominal aortic abnormalities. Additionally, it is imperative to develop and refine both medical and surgical management strategies. This review aims to indicate the role of computational analysis in the comprehension and management of AAAs and covers recent research studies regarding the computational analysis approach conducted between 2021 and 2023. Computational analysis methods have emerged as sophisticated and noninvasive approaches, providing detailed insights into the complex dynamics of AAA and enhancing our ability to study and manage this condition effectively.

METHODS

Computational analysis relies on fluid mechanics principles applied to arterial flow, using the Navier-Stokes equations to model blood flow dynamics. Key hemodynamic indicators relevant to AAAs include Time-Average Wall Shear Stress, Oscillatory Shear Index, Endothelial Cell Activation Potential, and Relative Residence Time. The primary methods employed for simulating the abdominal aorta and studying its biomechanical environment are computational fluid dynamics and Finite Element Methods. This review article encompasses a thorough examination of recent literature, focusing on studies conducted between 2021 and 2023.

RESULTS

The latest studies have elucidated crucial insights into the blood flow characteristics and geometric attributes of AAAs. Notably, blood flow patterns within AAAs are associated with increased rupture risk, along with elevated intraluminal thrombus volume and specific calcification thresholds. Asymmetric AAAs exhibit heightened risks of rupture and thrombus formation due to low and oscillating wall shear stresses. Moreover, larger aneurysms demonstrate increased wall stress, pressure, and energy loss. Advanced modeling techniques have augmented predictive capabilities concerning growth rates and surgical thresholds. Additionally, the influence of material properties and thrombus volume on wall stress levels is noteworthy, while inlet velocity profiles significantly modulate blood flow dynamics within AAAs.

CONCLUSIONS

This review highlights the potential utility of computational modeling. However, the clinical applicability of computational modeling has been limited by methodological variability despite the ongoing accumulation of evidence supporting the prognostic significance of biomechanical and hemodynamic indices in this field. The establishment of standardized reporting is critical for clinical implementation.

Impact of Geometric Attributes on Abdominal Aortic Aneurysm Rupture Risk: An In Vivo FSI-Based Study

Reported in this paper is a cutting-edge computational investigation into the influence of geometric characteristics on abdominal aortic aneurysm (AAA) rupture risk, beyond the traditional measure of maximum aneurysm diameter. A Comprehensive fluid-structure interaction (FSI) analysis was employed to assess risk factors in a range of patient scenarios, with the use of three-dimensional (3D) AAA models reconstructed from patient-specific aortic data and finite element method. Wall shear stress (WSS), and its derivatives such as time-averaged WSS (TAWSS), oscillatory shear index (OSI), relative residence time (RRT) and transverse WSS (transWSS) offer insights into the force dynamics acting on the AAA wall. Emphasis is placed on these WSS-based metrics and seven key geometric indices. By correlating these geometric discrepancies with biomechanical phenomena, this study highlights the novel and profound impact of geometry on risk prediction. This study demonstrates the necessity of a multidimensional assessment approach, future efforts should complement these findings with experimental validations for an applicable approach for clinical use.

Pathophysiology of the ascending aorta: Impact of dilation and valve phenotype on large-scale blood flow coherence detected by 4D flow MRI

BACKGROUND AND OBJECTIVE

The evidence on the role of hemodynamics in aorta pathophysiology has yet to be robustly translated into clinical applications, to improve risk stratification of aortic diseases. Motivated by the need to enrich the current understanding of the pathophysiology of the ascending aorta (AAo), this study evaluates in vivo how large-scale aortic flow coherence is affected by AAo dilation and aortic valve phenotype.

METHODS

A complex networks-based approach is applied to 4D flow MRI data to quantify subject-specific AAo flow coherence in terms of correlation between axial velocity waveforms and the aortic flow rate waveform along the cardiac cycle. The anatomical length of persistence of such correlation is quantified using the recently proposed network metric average weighted curvilinear distance (AWCD). The analysis considers 107 subjects selected to allow an ample stratification in terms of aortic valve morphology, absence/presence of AAo dilation and of aortic valve stenosis.

RESULTS

The analysis highlights that the presence of AAo dilation as well as of bicuspid aortic valve phenotype breaks the physiological AAo flow coherence, quantified in terms of AWCD. Of notice, it emerges that cycle-average blood flow rate and relative AAo dilation are main determinants of AWCD, playing opposite roles in promoting and hampering the persistence of large-scale flow coherence in AAo, respectively.

CONCLUSIONS

The findings of this study can contribute to broaden the current mechanistic link between large-scale blood flow coherence and aortic pathophysiology, with the prospect of enriching the existing tools for the in vivo non-invasive hemodynamic risk assessment for aortic diseases onset and progression.

Multiscale structure and function of the aortic valve apparatus

Whereas studying the aortic valve in isolation has facilitated the development of life-saving procedures and technologies, the dynamic interplay of the aortic valve and its surrounding structures is vital to preserving their function across the wide range of conditions encountered in an active lifestyle. Our view is that these structures should be viewed as an integrated functional unit, here referred to as the aortic valve apparatus (AVA). The coupling of the aortic valve and root, left ventricular outflow tract, and blood circulation is crucial for AVA's functions: unidirectional flow out of the left ventricle, coronary perfusion, reservoir function, and support of left ventricular function. In this review, we explore the multiscale biological and physical phenomena that underlie the simultaneous fulfillment of these functions. A brief overview of the tools used to investigate the AVA, such as medical imaging modalities, experimental methods, and computational modeling, specifically fluid-structure interaction (FSI) simulations, is included. Some pathologies affecting the AVA are explored, and insights are provided on treatments and interventions that aim to maintain quality of life. The concepts explained in this article support the idea of AVA being an integrated functional unit and help identify unanswered research questions. Incorporating phenomena through the molecular, micro, meso, and whole tissue scales is crucial for understanding the sophisticated normal functions and diseases of the AVA.

A new method for scaling inlet flow waveform in hemodynamic analysis of aortic dissection

Computational fluid dynamics (CFD) simulations have shown great potentials in cardiovascular disease diagnosis and postoperative assessment. Patient-specific and well-tuned boundary conditions are key to obtaining accurate and reliable hemodynamic results. However, CFD simulations are usually performed under non-patient-specific flow conditions due to the absence of in vivo flow and pressure measurements. This study proposes a new method to overcome this challenge by tuning inlet boundary conditions using data extracted from electrocardiogram (ECG). Five patient-specific geometric models of type B aortic dissection were reconstructed from computed tomography (CT) images. Other available data included stoke volume (SV), ECG, and 4D-flow magnetic resonance imaging (MRI). ECG waveforms were processed to extract patient-specific systole to diastole ratio (SDR). Inlet boundary conditions were defined based on a generic aortic flow waveform tuned using (1) SV only, and (2) with ECG and SV (ECG + SV). 4D-flow MRI derived inlet boundary conditions were also used in patient-specific simulations to provide the gold standard for comparison and validation. Simulations using inlet flow waveform tuned with ECG + SV not only successfully reproduced flow distributions in the descending aorta but also provided accurate prediction of time-averaged wall shear stress (TAWSS) in the primary entry tear (PET) and abdominal regions, as well as maximum pressure difference, ∆Pmax, from the aortic root to the distal false lumen. Compared with simulations with inlet waveform tuned with SV alone, using ECG + SV in the tuning method significantly reduced the error in false lumen ejection fraction at the PET (from 149.1% to 6.2%), reduced errors in TAWSS at the PET (from 54.1% to 5.7%) and in the abdominal region (from 61.3% to 11.1%), and improved ∆Pmax prediction (from 283.1% to 18.8%) However, neither of these inlet waveforms could be used for accurate prediction of TAWSS in the ascending aorta. This study demonstrates the importance of SDR in tailoring inlet flow waveforms for patient-specific hemodynamic simulations. A well-tuned flow waveform is essential for ensuring that the simulation results are patient-specific, thereby enhancing the confidence and fidelity of computational tools in future clinical applications.

A comprehensive MRI-based computational model of blood flow in compliant aorta using radial basis function interpolation

BACKGROUND

Properly understanding the origin and progression of the thoracic aortic aneurysm (TAA) can help prevent its growth and rupture. For a better understanding of this pathogenesis, the aortic blood flow has to be studied and interpreted in great detail. We can obtain detailed aortic blood flow information using magnetic resonance imaging (MRI) based computational fluid dynamics (CFD) with a prescribed motion of the aortic wall.

METHODS

We performed two different types of simulations-static (rigid wall) and dynamic (moving wall) for healthy control and a patient with a TAA. For the latter, we have developed a novel morphing approach based on the radial basis function (RBF) interpolation of the segmented 4D-flow MRI geometries at different time instants. Additionally, we have applied reconstructed 4D-flow MRI velocity profiles at the inlet with an automatic registration protocol.

RESULTS

The simulated RBF-based movement of the aorta matched well with the original 4D-flow MRI geometries. The wall movement was most dominant in the ascending aorta, accompanied by the highest variation of the blood flow patterns. The resulting data indicated significant differences between the dynamic and static simulations, with a relative difference for the patient of 7.47±14.18% in time-averaged wall shear stress and 15.97±43.32% in the oscillatory shear index (for the whole domain).

CONCLUSIONS

In conclusion, the RBF-based morphing approach proved to be numerically accurate and computationally efficient in capturing complex kinematics of the aorta, as validated by 4D-flow MRI. We recommend this approach for future use in MRI-based CFD simulations in broad population studies. Performing these would bring a better understanding of the onset and growth of TAA.

Modelling lower-limb peripheral arterial disease using clinically available datasets: impact of inflow boundary conditions on hemodynamic indices for restenosis prediction

BACKGROUND AND OBJECTIVES

The integration of hemodynamic markers as risk factors in restenosis prediction models for lower-limb peripheral arteries is hindered by fragmented clinical datasets. Computed tomography (CT) scans enable vessel geometry reconstruction and can be obtained at different times than the Doppler ultrasound (DUS) images, which provide information on blood flow velocity. Computational fluid dynamics (CFD) simulations allow the computation of near-wall hemodynamic indices, whose accuracy depends on the prescribed inlet boundary condition (BC), derived from the DUS images. This study aims to: (i) investigate the impact of different DUS-derived velocity waveforms on CFD results; (ii) test whether the same vessel areas, subjected to altered hemodynamics, can be detected independently of the applied inlet BC; (iii) suggest suitable DUS images to obtain reliable CFD results.

METHODS

CFD simulations were conducted on three patients treated with bypass surgery, using patient-specific DUS-derived inlet BCs recorded at either the same or different time points than the CT scan. The impact of the chosen inflow condition on bypass hemodynamics was assessed in terms of wall shear stress (WSS)-derived quantities. Patient-specific critical thresholds for the hemodynamic indices were applied to identify critical luminal areas and compare the results with a reference obtained with a DUS image acquired in close temporal proximity to the CT scan.

RESULTS

The main findings indicate that: (i) DUS-derived inlet velocity waveforms acquired at different time points than the CT scan led to statistically significantly different CFD results (p<0.001); (ii) the same luminal surface areas, exposed to low time-averaged WSS, could be identified independently of the applied inlet BCs; (iii) similar outcomes were observed for the other hemodynamic indices if the prescribed inlet velocity waveform had the same shape and comparable systolic acceleration time to the one recorded in close temporal proximity to the CT scan.

CONCLUSIONS

Despite a lack of standardised data collection for diseased lower-limb peripheral arteries, an accurate estimation of luminal areas subjected to altered near-wall hemodynamics is possible independently of the applied inlet BC. This holds if the applied inlet waveform shares some characteristics - derivable from the DUS report - as one matching the acquisition time of the CT scan.

Image2Flow: A proof-of-concept hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data

Computational fluid dynamics (CFD) can be used for non-invasive evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and time-consuming simulation. This study aims to train a deep learning model to both generate patient-specific volume-meshes of the pulmonary artery from 3D cardiac MRI data and directly estimate CFD flow fields. This proof-of-concept study used 135 3D cardiac MRIs from both a public and private dataset. The pulmonary arteries in the MRIs were manually segmented and converted into volume-meshes. CFD simulations were performed on ground truth meshes and interpolated onto point-point correspondent meshes to create the ground truth dataset. The dataset was split 110/10/15 for training, validation, and testing. Image2Flow, a hybrid image and graph convolutional neural network, was trained to transform a pulmonary artery template to patient-specific anatomy and CFD values, taking a specific inlet velocity as an additional input. Image2Flow was evaluated in terms of segmentation, and the accuracy of predicted CFD was assessed using node-wise comparisons. In addition, the ability of Image2Flow to respond to increasing inlet velocities was also evaluated. Image2Flow achieved excellent segmentation accuracy with a median Dice score of 0.91 (IQR: 0.86-0.92). The median node-wise normalized absolute error for pressure and velocity magnitude was 11.75% (IQR: 9.60-15.30%) and 9.90% (IQR: 8.47-11.90), respectively. Image2Flow also showed an expected response to increased inlet velocities with increasing pressure and velocity values. This proof-of-concept study has shown that it is possible to simultaneously perform patient-specific volume-mesh based segmentation and pressure and flow field estimation using Image2Flow. Image2Flow completes segmentation and CFD in ~330ms, which is ~5000 times faster than manual methods, making it more feasible in a clinical environment.

Computational investigation of outflow graft variation impact on hemocompatibility profile in LVADs

BACKGROUND

Hemocompatibility-related adverse events (HRAE) occur commonly in patients with left ventricular assist devices (LVADs) and add to morbidity and mortality. It is unclear whether the outflow graft orientation can impact flow conditions leading to HRAE. This study presents a simulation-based approach using exact patient anatomy from medical images to investigate the influence of outflow cannula orientation in modulating flow conditions leading to HRAEs.

METHODS

A 3D model of a proximal aorta and outflow graft was reconstructed from a computed tomography (CT) scan of an LVAD patient and virtually modified to model multiple cannula orientations (n = 10) by varying polar (cranio-caudal) (n = 5) and off-set (anterior-posterior) (n = 2) angles. Time-dependent computational flow simulations were then performed for each anatomical orientation. Qualitative and quantitative hemodynamics metrics of thrombogenicity including time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), endothelial cell platelet activation potential (ECAP), particle residence time (PRT), and platelet activation potential (PLAP) were analyzed.

RESULTS

Within the simulations performed, endothelial cell activation potential (ECAP) and particle residence time (PRT) were found to be lowest with a polar angle of 85°, regardless of offset angle. However, polar angles that produced parameters at levels least associated with thrombosis varied when the offset angle was changed from 0° to 12°. For offset angles of 0° and 12° respectively, flow shear was lowest at 65° and 75°, time averaged wall shear stress (TAWSS) was highest at 85° and 35°, and platelet activation potential (PLAP) was lowest at 65° and 45°.

CONCLUSION

This study suggests that computational fluid dynamic modeling based on patient-specific anatomy can be a powerful analytical tool when identifying optimal positioning of an LVAD. Contrary to previous work, our findings suggest that there may be an "ideal" outflow cannula for each individual patient based on a CFD-based hemocompatibility profile.

Uncertainty quantification of the impact of peripheral arterial disease on abdominal aortic aneurysms in blood flow simulations

Peripheral arterial disease (PAD) and abdominal aortic aneurysms (AAAs) often coexist and pose significant risks of mortality, yet their mutual interactions remain largely unexplored. Here, we introduce a fluid mechanics model designed to simulate the haemodynamic impact of PAD on AAA-associated risk factors. Our focus lies on quantifying the uncertainty inherent in controlling the flow rates within PAD-affected vessels and predicting AAA risk factors derived from wall shear stress. We perform a sensitivity analysis on nine critical model parameters through simulations of three-dimensional blood flow within a comprehensive arterial geometry. Our results show effective control of the flow rates using two-element Windkessel models, although specific outlets need attention. Quantities of interest like endothelial cell activation potential (ECAP) and relative residence time are instructive for identifying high-risk regions, with ECAP showing greater reliability and adaptability. Our analysis reveals that the uncertainty in the quantities of interest is 187% of that of the input parameters. Notably, parameters governing the amplitude and frequency of the inlet velocity exert the strongest influence on the risk factors' variability and warrant precise determination. This study forms the foundation for patient-specific simulations involving PAD and AAAs which should ultimately improve patient outcomes and reduce associated mortality rates.

Flow patterns in ascending aortic aneurysms: Determining the role of hypertension using phase contrast magnetic resonance and computational fluid dynamics

Thoracic aortic aneurysm (TAA) is a local dilation of the thoracic aorta. Although universally used, aneurysm diameter alone is a poor predictor of major complications such as rupture. There is a need for better biomarkers for risk assessment that also reflect the aberrant flow patterns found in TAAs. Furthermore, hypertension is often present in TAA patients and may play a role in progression of aneurysm. The exact relation between TAAs and hypertension is poorly understood. This study aims to create a numerical model of hypertension in the aorta by using computational fluid dynamics. First, a normotensive state was simulated in which flow and resistance were kept unaltered. Second, a hypertensive state was modeled in which blood inflow was increased by 30%. Third, a hypertensive state was modeled in which the proximal and peripheral resistances and capacitance parameters from the three-element Windkessel boundary condition were adjusted to mimic an increase in resistance of the rest of the cardiovascular system. One patient with degenerative TAA and one healthy control were successfully simulated at hypertensive states and were extensively analyzed. Furthermore, three additional TAA patients and controls were simulated to validate our method. Hemodynamic variables such as wall shear stress, oscillatory shear index, endothelial cell activation potential (ECAP), vorticity and helicity were studied to gain more insight on the effects of hypertension on flow patterns in TAAs. By comparing a TAA patient and a control at normotensive state at peak-systole, helicity and vorticity were found to be lower in the TAA patient throughout the entire domain. No major changes in flow and flow derived quantities were observed for the TAA patient and control when resistance was increased. When flow rate was increased, regions with high ECAP values were found to reduce in TAA patients in the aneurysm region which could reduce the risk of thrombogenesis. Thus, it may be important to assess cardiac output in patients with TAA.

Heat transfer mechanism in idealized healthy and diseased aortas using fluid-structure interaction method

The heat transfer mechanism inside the human aorta may be related to the physiological function and lesion formation of the aortic wall. The objective of this study was to acquire the temperature distribution in the three-dimensional idealized aorta. An idealized healthy aortic geometry and three representative diseased aortas: aortic aneurysm, coarctation of the aorta, and aortic dissection were constructed. Advanced fluid-structure interaction (FSI) computational framework was applied to predict the aortic temperature distribution. The movement of the aortic root due to the heartbeat was also considered. The displacement distribution of the aortic vessel wall was consistent with clinical observation. The lesser curvature of the aortic arch, aneurysm body, coarctation region, and false lumen were all exposed to relatively high temperatures (over 310.006 K). We found that the rigid wall assumption slightly underestimated the magnitude of the whole aortic wall-averaged temperature while the changing trend and local temperature were like the results of the FSI method. Besides, the wall-averaged temperature would increase and the temperature inflection point would advance when the aortic vessel wall was loaded with a high heat flux. This pilot study revealed the aortic heat transfer mechanism and temperature distribution, and the findings may help to understand the physiological characteristics of the aortic vessel wall.

Impact of the Spatial Velocity Inlet Distribution on the Hemodynamics of the Thoracic Aorta

The impact of the distribution in space of the inlet velocity in the numerical simulations of the hemodynamics in the thoracic aorta is systematically investigated. A real healthy aorta geometry, for which in-vivo measurements are available, is considered. The distribution is modeled through a truncated cone shape, which is a suitable approximation of the real one downstream of a trileaflet aortic valve during the systolic part of the cardiac cycle. The ratio between the upper and the lower base of the truncated cone and the position of the center of the upper base are selected as uncertain parameters. A stochastic approach is chosen, based on the generalized Polynomial Chaos expansion, to obtain accurate response surfaces of the quantities of interest in the parameter space. The selected parameters influence the velocity distribution in the ascending aorta. Consequently, effects on the wall shear stress are observed, confirming the need to use patient-specific inlet conditions if interested in the hemodynamics of this region. The surface base ratio is globally the most important parameter. Conversely, the impact on the velocity and wall shear stress in the aortic arch and descending aorta is almost negligible.

Aneurysmal growth in type-B aortic dissection: assessing the impact of patient-specific inlet conditions on key haemodynamic indices

Type-B aortic dissection is a cardiovascular disease in which a tear develops in the intimal layer of the descending aorta, allowing pressurized blood to delaminate the layers of the vessel wall. In medically managed patients, long-term aneurysmal dilatation of the false lumen (FL) is considered virtually inevitable and is associated with poorer disease outcomes. While the pathophysiological mechanisms driving FL dilatation are not yet understood, haemodynamic factors are believed to play a key role. Computational fluid dynamics (CFD) and 4D-flow MRI (4DMR) analyses have revealed correlations between flow helicity, oscillatory wall shear stress and aneurysmal dilatation of the FL. In this study, we compare CFD simulations using a patient-specific, three-dimensional, three-component inlet velocity profile (4D IVP) extracted from 4DMR data against simulations with flow rate-matched uniform and axial velocity profiles that remain widely used in the absence of 4DMR. We also evaluate the influence of measurement errors in 4DMR data by scaling the 4D IVP to the degree of imaging error detected in prior studies. We observe that oscillatory shear and helicity are highly sensitive to inlet velocity distribution and flow volume throughout the FL and conclude that the choice of IVP may greatly affect the future clinical value of simulations.

Numerical computation of pulsatile hemodynamics and diagnostic concern of coronary bifurcated artery flow for Newtonian and non-Newtonian fluid

Atherosclerotic with the high occurrence of plaque formation due to stenosis has attracted wide attention among researchers. The left coronary artery has been studied in two-dimensional and in three-dimensional (3D) bifurcation as the models of blood flow through Newtonian and non-Newtonian fluids to better understand the physical mechanism. The computational Fluid Dynamics (CFD) technique is incorporated in COMSOL Multiphysics and then it is justified by satisfactory validation. It is found that the Newtonian model shows larger recirculation zones than non-Newtonian does. The present study also focuses on the evaluations of the lesion of diagnostic and the coefficient of pressure drop assessments on the basis of the diagnostic parameter's critical values affected by the rheological model. Nevertheless, the leading concentration of the subsisting investigation works is confined to the change of importance factor (IFc) affected by arterial blockage. But the IFc of non-Newtonian fluid for 3D left coronary artery bifurcation model decreases with increasing bifurcation angle and the time-averaged inlet pressure is the least for smaller bifurcation angles. The current research further concentrates that the flow separation length reduces with developing bifurcation angle in bifurcated geometry. It is significant to mention that non-Newtonian blood flow model incorporating hemodynamic and diagnostic parameters has great impacts on instantaneous flow systems.

Fluid-structure interaction study for biomechanics and risk factors in Stanford type A aortic dissection

Aortic dissection is a life-threatening condition with a rising prevalence in the elderly population, possibly as a consequence of the increasing population life expectancy. Untreated aortic dissection can lead to myocardial infarction, aortic branch malperfusion or occlusion, rupture, aneurysm formation and death. This study aims to assess the potential of a biomechanical model in predicting the risks of a non-dilated thoracic aorta with Stanford type A dissection. To achieve this, a fully coupled fluid-structure interaction model was developed under realistic blood flow conditions. This model of the aorta was developed by considering three-dimensional artery geometry, multiple artery layers, hyperelastic artery wall, in vivo-based physiological time-varying blood velocity profiles, and non-Newtonian blood behaviours. The results demonstrate that in a thoracic aorta with Stanford type A dissection, the wall shear stress (WSS) is significantly low in the ascending aorta and false lumen, leading to potential aortic dilation and thrombus formation. The results also reveal that the WSS is highly related to blood flow patterns. The aortic arch region near the brachiocephalic and left common carotid artery is prone to rupture, showing a good agreement with the clinical reports. The results have been translated into their potential clinical relevance by revealing the role of the stress state, WSS and flow characteristics as the main parameters affecting lesion progression, including rupture and aneurysm. The developed model can be tailored for patient-specific studies and utilised as a predictive tool to estimate aneurysm growth and initiation of wall rupture inside the human thoracic aorta.

Impact of wall displacements on the large-scale flow coherence in ascending aorta

In the context of aortic hemodynamics, uncertainties affecting blood flow simulations hamper their translational potential as supportive technology in clinics. Computational fluid dynamics (CFD) simulations under rigid-walls assumption are largely adopted, even though the aorta contributes markedly to the systemic compliance and is characterized by a complex motion. To account for personalized wall displacements in aortic hemodynamics simulations, the moving-boundary method (MBM) has been recently proposed as a computationally convenient strategy, although its implementation requires dynamic imaging acquisitions not always available in clinics. In this study we aim to clarify the real need for introducing aortic wall displacements in CFD simulations to accurately capture the large-scale flow structures in the healthy human ascending aorta (AAo). To do that, the impact of wall displacements is analyzed using subject-specific models where two CFD simulations are performed imposing (1) rigid walls, and (2) personalized wall displacements adopting a MBM, integrating dynamic CT imaging and a mesh morphing technique based on radial basis functions. The impact of wall displacements on AAo hemodynamics is analyzed in terms of large-scale flow patterns of physiological significance, namely axial blood flow coherence (quantified applying the Complex Networks theory), secondary flows, helical flow and wall shear stress (WSS). From the comparison with rigid-wall simulations, it emerges that wall displacements have a minor impact on the AAo large-scale axial flow, but they can affect secondary flows and WSS directional changes. Overall, helical flow topology is moderately affected by aortic wall displacements, whereas helicity intensity remains almost unchanged. We conclude that CFD simulations with rigid-wall assumption can be a valid approach to study large-scale aortic flows of physiological significance.

Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta

BACKGROUND AND OBJECTIVE

Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoratic aortic aneurysms (ATAA). To accurately reproduce in vivo hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements, still makes researchers resort to idealized BCs. The aim of this study was to generate and thoroughly characterize a large dataset of synthetic 4D aortic velocity profiles sampled on a 2D cross-section along the ascending aorta with features similar to clinical cohorts of patients with ATAA.

METHODS

Time-resolved 3D phase contrast magnetic resonance (4D flow MRI) scans of 30 subjects with ATAA were processed through in-house code to extract anatomically consistent cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated.

RESULTS

Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors.

CONCLUSIONS

We built a data-driven generative model of 4D aortic inlet velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set.

Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields

Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is yet to be realised. Barriers for CFD include high computational resources, specialist experience needed for designing simulation set-ups, and long processing times. The aim of this study was to explore the use of machine learning (ML) to replicate conventional aortic CFD with automatic and fast regression models. Data used to train/test the model consisted of 3,000 CFD simulations performed on synthetically generated 3D aortic shapes. These subjects were generated from a statistical shape model (SSM) built on real patient-specific aortas (N = 67). Inference performed on 200 test shapes resulted in average errors of 6.01% ±3.12 SD and 3.99% ±0.93 SD for pressure and velocity, respectively. Our ML-based models performed CFD in ∼0.075 seconds (4,000x faster than the solver). This proof-of-concept study shows that results from conventional vascular CFD can be reproduced using ML at a much faster rate, in an automatic process, and with reasonable accuracy.

The Effect of Blood Rheology and Inlet Boundary Conditions on Realistic Abdominal Aortic Aneurysms under Pulsatile Flow Conditions

BACKGROUND

The effects of non-Newtonian rheology and boundary conditions on various pathophysiologies have been studied quite extensively in the literature. The majority of results present qualitative and/or quantitative conclusions that are not thoroughly assessed from a statistical perspective.

METHODS

The finite volume method was employed for the numerical simulation of seven patient-specific abdominal aortic aneurysms. For each case, five rheological models and three inlet velocity boundary conditions were considered. Outlier- and heteroscedasticity-robust ANOVA tests assessed the simultaneous effect of rheological specifications and boundary conditions on fourteen variables that capture important characteristics of vascular flows.

RESULTS

The selection of inlet velocity profiles appears as a more critical factor relative to rheological specifications, especially regarding differences in the oscillatory characteristics of computed flows. Response variables that relate to the average tangential force on the wall over the entire cycle do not differ significantly across alternative factor levels, as long as one focuses on non-Newtonian specifications.

CONCLUSIONS

The two factors, namely blood rheological models and inlet velocity boundary condition, exert additive effects on variables that characterize vascular flows, with negligible interaction effects. Regarding thrombus-prone conditions, the Plug inlet profile offers an advantageous hemodynamic configuration with respect to the other two profiles.

An artificial intelligence-based platform for automatically estimating time-averaged wall shear stress in the ascending aorta

Aims

Aortopathies are a series of disorders requiring multiple indicators to assess risk. Time-averaged wall shear stress (TAWSS) is currently considered as the primary indicator of aortopathies progression, which can only be calculated by Computational Fluid Dynamics (CFD). However, CFD's complexity and high computational cost, greatly limit its application. The study aimed to construct a deep learning platform which could accurately estimate TAWSS in ascending aorta.

Methods and results

A total of 154 patients who had thoracic computed tomography angiography were included and randomly divided into two parts: training set (90%, n = 139) and testing set (10%, n = 15). TAWSS were calculated via CFD. The artificial intelligence (AI)-based model was trained and assessed using the dice coefficient (DC), normalized mean absolute error (NMAE), and root mean square error (RMSE). Our AI platform brought into correspondence with the manual segmentation (DC = 0.86) and the CFD findings (NMAE, 7.8773% ± 4.7144%; RMSE, 0.0098 ± 0.0097), while saving 12000-fold computational cost.

Conclusion

The high-efficiency and robust AI platform can automatically estimate value and distribution of TAWSS in ascending aorta, which may be suitable for clinical applications and provide potential ideas for CFD-based problem solving.

High-resolution cerebral blood flow simulation with a domain decomposition method and verified by the TCD measurement

BACKGROUND

An efficient and accurate blood flow simulation can be useful for understanding many vascular diseases. Accurately resolving the blood flow velocity based on patient-specific geometries and model parameters is still a major challenge because of complex geomerty and turbulence issues. In addition, obtaining results in a short amount of computing time is important so that the simulation can be used in the clinical environment. In this work, we present a parallel scalable method for the patient-specific blood flow simulation with focuses on its parallel performance study and clinical verification.

METHODS

We adopt a fully implicit unstructured finite element method for a patient-specific simulation of blood flow in a full precerebral artery. The 3D artery is constructed from MRI images, and a parallel Newton-Krylov method preconditioned with a two-level domain decomposition method is adopted to solve the large nonlinear system discretized from the time-dependent 3D Navier-Stokes equations in the artery with an integral outlet boundary condition. The simulated results are verified using the clinical data measured by transcranial Doppler ultrasound, and the parallel performance of the algorithm is studied on a supercomputer.

RESULTS

The simulated velocity matches the clinical measured data well. Other simulated blood flow parameters, such as pressure and wall shear stress, are within reasonable ranges. The results show that the parallel algorithm scales up to 2160 processors with a 49% parallel efficiency for solving a problem with over 20 million unstructured elements on a supercomputer. For a standard cerebral blood flow simulation case with approximately 4 million finite elements, the calculation of one cardiac cycle can be finished within one hour with 1000 processors.

CONCLUSION

The proposed method is able to perform high-resolution 3D blood flow simulations in a patient-specific full precerebral artery within an acceptable time, and the simulated results are comparable with the clinical measured data, which demonstrates its high potential for clinical applications.

Component quantification of aortic blood flow energy loss using computational fluid-structure interaction hemodynamics

BACKGROUND AND OBJECTIVES

The aorta serves as the main tube of the human blood circulation system. Energy loss (EL) occurs when blood flows through the aorta and there may be a potential correlation between EL and aortic diseases. However, the components of blood flow EL are still not fully understood. This study aims to quantitatively reveal the EL components in healthy and diseased aortas.

METHODS

We construct an idealized healthy aorta and three idealized representative diseased aortas: aortic aneurysm, coarctation of the aorta, and aortic dissection. Computational hemodynamic studies are carried out by using the fluid-structure interaction simulation framework.

RESULTS

Four kinds of EL components: viscous friction, turbulence dissipation, wall deformation, and local lesion are firstly acquired in healthy and diseased aortas based on the high-resolution blood flow information. Viscous friction contributes most to the EL (45.69%-57.22%). EL caused by the deformation of the aortic wall ranks second (15.18%-33.12%). The proportions of turbulence dissipation and local lesion depend on individual geometric characteristics. Besides, the buffering efficiency of the healthy and diseased aorta is about 80%.

CONCLUSIONS

This study quantitatively reports the components of blood flow EL in healthy and diseased aortas, the finding may provide novel insights into the pathogenesis of aortic diseases.

Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond

Recent progress in machine learning (ML), together with advanced computational power, have provided new research opportunities in cardiovascular modeling. While classifying patient outcomes and medical image segmentation with ML have already shown significant promising results, ML for the prediction of biomechanics such as blood flow or tissue dynamics is in its infancy. This perspective article discusses some of the challenges in using ML for replacing well-established physics-based models in cardiovascular biomechanics. Specifically, we discuss the large landscape of input features in 3D patient-specific modeling as well as the high-dimensional output space of field variables that vary in space and time. We argue that the end purpose of such ML models needs to be clearly defined and the tradeoff between the loss in accuracy and the gained speedup carefully interpreted in the context of translational modeling. We also discuss several exciting venues where ML could be strategically used to augment traditional physics-based modeling in cardiovascular biomechanics. In these applications, ML is not replacing physics-based modeling, but providing opportunities to solve ill-defined problems, improve measurement data quality, enable a solution to computationally expensive problems, and interpret complex spatiotemporal data by extracting hidden patterns. In summary, we suggest a strategic integration of ML in cardiovascular biomechanics modeling where the ML model is not the end goal but rather a tool to facilitate enhanced modeling.

On the Role and Effects of Uncertainties in Cardiovascular in silico Analyses

The assessment of cardiovascular hemodynamics with computational techniques is establishing its fundamental contribution within the world of modern clinics. Great research interest was focused on the aortic vessel. The study of aortic flow, pressure, and stresses is at the basis of the understanding of complex pathologies such as aneurysms. Nevertheless, the computational approaches are still affected by sources of errors and uncertainties. These phenomena occur at different levels of the computational analysis, and they also strongly depend on the type of approach adopted. With the current study, the effect of error sources was characterized for an aortic case. In particular, the geometry of a patient-specific aorta structure was segmented at different phases of a cardiac cycle to be adopted in a computational analysis. Different levels of surface smoothing were imposed to define their influence on the numerical results. After this, three different simulation methods were imposed on the same geometry: a rigid wall computational fluid dynamics (CFD), a moving-wall CFD based on radial basis functions (RBF) CFD, and a fluid-structure interaction (FSI) simulation. The differences of the implemented methods were defined in terms of wall shear stress (WSS) analysis. In particular, for all the cases reported, the systolic WSS and the time-averaged WSS (TAWSS) were defined.

Hemodynamic effects of stent-graft introducer sheath during thoracic endovascular aortic repair

Thoracic endovascular aortic repair (TEVAR) has become the standard treatment of a variety of aortic pathologies. The objective of this study is to evaluate the hemodynamic effects of stent-graft introducer sheath during TEVAR. Three idealized representative diseased aortas were designed: aortic aneurysm, coarctation of the aorta, and aortic dissection. Computational fluid dynamics studies were performed in the above idealized aortic geometries. An introducer sheath routinely used in the clinic was virtually placed into diseased aortas. Comparative analysis was carried out to evaluate the hemodynamic effects of the introducer sheath. Results show that the blood flow to the supra-aortic branches would increase above 9% due to the obstruction of the introducer sheath. The region exposed to high endothelial cell activation potential (ECAP) expands in the scenarios of coarctation of the aorta and aortic dissection, which indicates that the probability of thrombus formation may increase during TEVAR. The pressure magnitude in peak systole shows an obvious rise, and a similar phenomenon is not observed in early diastole. The blood viscosity in the aortic arch and descending aorta is remarkably altered by the introducer sheath. The uneven viscosity distribution confirms the necessity of using non-Newtonian models, and high-viscosity region with high ECAP further promotes thrombosis. Our results highlight the hemodynamic effects of stent-graft introducer sheath during TEVAR, which may associate with perioperative complications.

Investigations into the Potential of Using Open Source CFD to Analyze the Differences in Hemodynamic Parameters for Aortic Dissections (Healthy versus Stanford Type A and B)

BACKGROUND

The objective of this study was to develop a method to evaluate the effects of an aortic dissection on hemodynamic parameters by conducting a comparison with that of a healthy (nondissected) aorta. Open-source software will be implemented, no proprietary software/application will be used to ensure accessorily and repeatability, in all the data analysis and processing. Computed tomography (CT) images of aortic dissection are used for the model geometry segmentation. Boundary conditions from literature are implemented to computational fluid dynamics (CFD) to analyze the hemodynamic parameters.

METHODS

A numerical simulation model was created by obtaining accurate 3-dimensional geometries of aortae from CT images. In this study, CT images of 8 cases of aortic dissection (Stanford type-A and type-B) and 3 cases of healthy aortae are used for the actual aorta model geometry segmentation. These models were exported into an open-source CFD software, OpenFOAM, where a simplified pulsating flow was simulated by controlling the flow pressure. Ten cycles of the pulsatile flow (0.50 sec/cycle) conditions, totaling 5 sec, were calculated.

RESULTS

The pressure distribution, wall shear stress (WSS) and flow velocity streamlines within the aorta and the false lumen were calculated and visualized. It was found that the flow velocity and WSS had a high correlation in high WSS areas of the intermittent layer between the true and false lumen. Most of the Stanford type-A dissections in the study showed high WSS, over 38 Pa, at the systole phase. This indicates that the arterial walls in type-A dissections are more likely to be damaged with pulsatile flow.

CONCLUSIONS

Using CFD to estimate localized high WSS areas may help in deciding to treat a type-A or B dissection with a stent graft to prevent a potential rupture.

A novel MRI-based data fusion methodology for efficient, personalised, compliant simulations of aortic haemodynamics

We present a novel, cost-efficient methodology to simulate aortic haemodynamics in a patient-specific, compliant aorta using an MRI data fusion process. Based on a previously-developed Moving Boundary Method, this technique circumvents the high computational cost and numerous structural modelling assumptions required by traditional Fluid-Structure Interaction techniques. Without the need for Computed Tomography (CT) data, the MRI images required to construct the simulation can be obtained during a single imaging session. Black Blood MR Angiography and 2D Cine-MRI data were used to reconstruct the luminal geometry and calibrate wall movement specifically to each region of the aorta. 4D-Flow MRI and non-invasive pressure measurements informed patient-specific inlet and outlet boundary conditions. Luminal area closely matched 2D Cine-MRI measurements with a mean error of less than 4.6% across the cardiac cycle, while physiological pressure and flow distributions were simulated to within 3.3% of patient-specific targets. Moderate agreement with 4D-Flow MRI velocity data was observed. Despite lower peak velocity, an equivalent rigid-wall simulation predicted a mean Time-Averaged Wall Shear Stress (TAWSS) 13% higher than the compliant simulation. The agreement observed between compliant simulation results and MRI data is testament to the accuracy and efficiency of this MRI-based simulation technique.

Fluid-structure interaction: Insights into biomechanical implications of endograft after thoracic endovascular aortic repair

Thoracic endovascular aortic repair (TEVAR) has developed to be the most effective treatment for aortic diseases. This study aims to evaluate the biomechanical implications of the implanted endograft after TEVAR. We present a novel image-based, patient-specific, fluid-structure computational framework. The geometries of blood, endograft, and aortic wall were reconstructed based on clinical images. Patient-specific measurement data was collected to determine the parameters of the three-element Windkessel. We designed three postoperative scenarios with rigid wall assumption, blood-wall interaction, blood-endograft-wall interplay, respectively, where a two-way fluid-structure interaction (FSI) method was applied to predict the deformation of the composite stent-wall. Computational results were validated with Doppler ultrasound data. Results show that the rigid wall assumption fails to predict the waveforms of blood outflow and energy loss (EL). The complete storage and release process of blood flow energy, which consists of four phases is captured by the FSI method. The endograft implantation would weaken the buffer function of the aorta and reduce mean EL by 19.1%. The closed curve area of wall pressure and aortic volume could indicate the EL caused by the interaction between blood flow and wall deformation, which accounts for 68.8% of the total EL. Both the FSI and endograft have a slight effect on wall shear stress-related-indices. The deformability of the composite stent-wall region is remarkably limited by the endograft. Our results highlight the importance of considering the interaction between blood flow, the implanted endograft, and the aortic wall to acquire physiologically accurate hemodynamics in post-TEVAR computational studies and the deformation of the aortic wall is responsible for the major EL of the blood flow.

Hemodynamic effects of the human aorta arch with different inflow rate waveforms from the ascending aorta inlet: A numerical study

BACKGROUND

Heart failure (HF) is a common disease globally. Ventricular assist devices (VADs) are widely used to treat HF. In contrast to the natural heart, different VADs generate different blood flow waves in the aorta.

OBJECTIVE

To explore whether the different inflow rate waveforms from the ascending aorta generate far-reaching hemodynamic influences on the human aortic arch.

METHODS

An aortic geometric model was reconstructed based on computed tomography data of a patient with HF. A total of five numerical simulations were conducted, including a case with the inflow rate waveforms from the ascending aorta with normal physiological conditions, two HF, and two with typical VAD support. The hemodynamic parameters, wall shear stress (WSS), oscillatory shear index (OSI), relative residence time (RRT), and the strength of the helical flow, were calculated.

RESULTS

In contrast to the natural heart, numerical simulations showed that HF decreased WSS and induced higher OSI and RRT. Moreover, HF weakened helical flow strength. Pulsatile flow VADs that elevated the WSS, induced some helical flow, while continuous flow VADs could not.

CONCLUSIONS

HF leads to an adverse hemodynamic environment by decreasing WSS and reducing the helical flow strength. Based upon hemodynamic effects, pulsatile flow VADs may be more advantageous than continuous flow VADs. Thus, pulsatile flow VADs may be a better option for patients with HF.

Efficient parallel simulation of hemodynamics in patient-specific abdominal aorta with aneurysm

Surgical planning for aortic aneurysm repair is a difficult task. In addition to the morphological features obtained from medical imaging, alternative features obtained with computational modeling may provide additional useful information. Though numerical studies are noninvasive, they are often time-consuming, especially when we need to study and compare multiple repair scenarios, because of the high computational complexity. In this paper, we present a highly parallel algorithm for the numerical simulation of unsteady blood flows in the patient-specific abdominal aorta before and after the aneurysmic repair. We model the blood flow with the unsteady incompressible Navier-Stokes equations with different outlet boundary conditions, and solve the discretized system with a highly scalable domain decomposition method. With this approach, a high resolution simulation of a full-size adult aorta can be obtained in less than an hour, instead of days with older methods and software. In addition, we show that the parallel efficiency of the proposed method is near 70% on a parallel computer with 2, 880 processor cores.

Aortic hemodynamics assessment prior and after valve sparing reconstruction: A patient-specific 4D flow-based FSI model

INTRODUCTION

Valve-sparing root replacement (VSRR) of the ascending aorta is a life-saving procedure for the treatment of aortic aneurysms, but patients remain at risk for post-operative events involving the downstream native aorta, the mechanism for which is uncertain. It is possible that proximal graft replacement of the ascending aorta induces hemodynamics alterations in the descending aorta, which could trigger adverse events. Herein, we present a fluid-structure interaction (FSI) protocol, based on patient-specific geometry and boundary conditions, to assess impact of proximal aortic grafts on downstream aortic hemodynamics and distensibility.

METHODS

Cardiac magnetic resonance (CMR), including MRA, cine-CMR and 4D flow sequences, was performed prior and after VSRR on one subject. Central blood pressure was non-invasively acquired at the time of the CMR: data were used to reconstruct the pre- and post-VSRR model and derive patient-specific boundary conditions for the FSI and a computational fluid dynamic (CFD) analysis with the same settings. Results were validated comparing the predicted velocity field against 4D flow dataset, over four landmarks along the aorta, and the predicted distensibility against the cine-CMR derived value.

RESULTS

Instantaneous velocity magnitudes extracted from 4D flow and FSI were similar (p > 0.05), while CFD-predicted velocity was significantly higher (p < 0.001), especially in the descending aorta of the pre-VSRR model (v_max was 73 cm/s, 76 cm/s and 99 cm/s, respectively). As measured in cine-CMR, FSI predicted an increase in descending aorta distensibility after grafting (i.e., 4.02 to 5.79 10^-3 mmHg^-1). In the descending aorta, the post-VSRR model showed increased velocity, aortic distensibility, stress and strain and wall shear stress.

CONCLUSIONS

Our Results indicate that i) the distensibility of the wall cannot be neglected, and hence the FSI method is necessary to obtain reliable results; ii) graft implantation induces alterations in the hemodynamics and biomechanics along the thoracic aorta, that may trigger adverse vessel remodeling.

Hemodynamic Abnormalities in the Aorta of Turner Syndrome Girls

Congenital abnormalities in girls and women with Turner syndrome (TS), alongside an underlying predisposition to obesity and hypertension, contribute to an increased risk of cardiovascular disease and ultimately reduced life expectancy. We observe that children with TS present a greater variance in aortic arch morphology than their healthy counterparts, and hypothesize that their hemodynamics is also different. In this study, computational fluid dynamic (CFD) simulations were performed for four TS girls, and three age-matched healthy girls, using patient-specific inlet boundary conditions, obtained from phase-contrast MRI data. The visualization of multidirectional blood flow revealed an increase in vortical flow in the arch, supra-aortic vessels, and descending aorta, and a correlation between the presence of aortic abnormalities and disturbed flow. Compared to the relatively homogeneous pattern of time-averaged wall shear stress (TAWSS) on the healthy aortae, a highly heterogeneous distribution with elevated TAWSS values was observed in the TS geometries. Visualization of further shear stress parameters, such as oscillatory shear index (OSI), normalized relative residence time (RRTn), and transverse WSS (transWSS), revealed dissimilar heterogeneity in the oscillatory and multidirectional nature of the aortic flow. Taking into account the young age of our TS cohort (average age 13 ± 2 years) and their obesity level (75% were obese or overweight), which is believed to accelerate the initiation and progression of endothelial dysfunction, these findings may be an indication of atherosclerotic disease manifesting earlier in life in TS patients. Age, obesity and aortic morphology may, therefore, play a key role in assessing cardiovascular risk in TS children.

CRIMSON: An open-source software framework for cardiovascular integrated modelling and simulation

In this work, we describe the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software environment. CRIMSON provides a powerful, customizable and user-friendly system for performing three-dimensional and reduced-order computational haemodynamics studies via a pipeline which involves: 1) segmenting vascular structures from medical images; 2) constructing analytic arterial and venous geometric models; 3) performing finite element mesh generation; 4) designing, and 5) applying boundary conditions; 6) running incompressible Navier-Stokes simulations of blood flow with fluid-structure interaction capabilities; and 7) post-processing and visualizing the results, including velocity, pressure and wall shear stress fields. A key aim of CRIMSON is to create a software environment that makes powerful computational haemodynamics tools accessible to a wide audience, including clinicians and students, both within our research laboratories and throughout the community. The overall philosophy is to leverage best-in-class open source standards for medical image processing, parallel flow computation, geometric solid modelling, data assimilation, and mesh generation. It is actively used by researchers in Europe, North and South America, Asia, and Australia. It has been applied to numerous clinical problems; we illustrate applications of CRIMSON to real-world problems using examples ranging from pre-operative surgical planning to medical device design optimization.

Effects of Ageing on Aortic Circulation During Atrial Fibrillation; a Numerical Study on Different Aortic Morphologies

Atrial fibrillation (AF) can alter intra-cardiac flow and cardiac output that subsequently affects aortic flow circulation. These changes may become more significant where they occur concomitantly with ageing. Aortic ageing is accompanied with morphological changes such as dilation, lengthening, and arch unfolding. While the recognition of AF mechanism has been the subject of numerous studies, less focus has been devoted to the aortic circulation during the AF and there is a lack of such investigation at different ages. The current work aims to address the present gap. First, we analyse aortic flow distribution in three configurations, which attribute to young, middle and old people, using geometries constructed via clinical data. We then introduce two transient inlet flow conditions representative of key AF-associated defects. Results demonstrate that both AF and ageing negatively affect flow circulation. The main consequence of concomitant occurrence is enhancement of endothelial cell activation potential (ECAP) throughout the vascular domain, mainly at aortic arch and descending thoracic aorta, which is consistent with some clinical observations. The outcome of the current study suggests that AF exacerbates the vascular defects occurred due to the ageing, which increases the possibility of cardiovascular diseases per se.

Numerical Simulation of Blood Flows in Patient-specific Abdominal Aorta with Primary Organs

The abdominal aorta is the largest artery in the abdominal cavity that supplies blood flows to vital organs through the complex visceral arterial branches, including the celiac trunk (the liver, stomach, spleen, etc.), the renal arteries (the kidneys) and the superior and inferior mesenteric arteries (the small and large intestine, pancreas, etc.). An accurate simulation of blood flows in this network of arteries is important for the understanding of the hemodynamics in various organs of healthy and diseased patients, but the computational cost is very high. As a result, most researchers choose to focus on a portion of the artery or use a low-dimensional approximation of the artery. In the present work, we introduce a parallel algorithm for the modeling of pulsatile flows in the abdominal aorta with branches to the primary organs, and an organ-based two-level method for calculating the resistances for the outflow boundary conditions. With this highly parallel approach, the simulation of the blood flow for a cardiac cycle of the anatomically detailed aorta can be obtained within a few hours, and the blood distribution to organs including liver, spleen and kidneys are also computed with certain accuracy. Moreover, we discuss the significant hemodynamic differences resulted from the influence of the peripheral branches. In addition, we examine the accuracy of the results with respect to the mesh size and time-step size and show the high parallel scalability of the proposed algorithm with up to 3000 processor cores.

Numerical investigation of patient-specific thoracic aortic aneurysms and comparison with normal subject via computational fluid dynamics (CFD)

Vascular hemodynamics play an important role in cardiovascular diseases. This work aimed to investigate the effects of an increase in ascending aortic diameter (AAD) on hemodynamics throughout a cardiac cycle for real patients. In this study, two scans of thoracic aortic aneurysm (TAA) subject with different AADs (42.94 mm and 48.01 mm) and a scan of a normal subject (19.81 mm) were analyzed to assess the effects of hemodynamics on the progression of TAA with the same flow rate. Real-patient aortic geometries were scanned by computed tomography angiography (CTA), and steady and pulsatile flow conditions were used to simulate real patient aortic geometries. Aortic arches were obtained from routine clinical scans. Computational fluid dynamics (CFD) simulations were performed with in vivo boundary conditions, and 3D Navier-Stokes equations were solved by a UDF (user-defined function) code defining a real cardiac cycle of one patient using Fourier series (FS). Wall shear stress (WSS) and pressure distributions were presented from normal subject to TAA cases. The results show that during the peak systolic phase pressure load increased by 18.56% from normal subject to TAA case 1 and by 23.8% from normal subject to TAA case 2 in the aneurysm region. It is concluded that although overall WSS increased in aneurysm cases but was low in dilatation areas. As a result, abnormal changes in WSS and higher pressure load may lead to rupture and risk of further dilatation. CFD simulations were highly effective to guide clinical predictions and assess the progress of aneurysm regions in case of early surgical intervention. Graphical abstract.

A novel formulation for the study of the ascending aortic fluid dynamics with in vivo data

Numerical simulations to evaluate thoracic aortic hemodynamics include a computational fluid dynamic (CFD) approach or fluid-structure interaction (FSI) approach. While CFD neglects the arterial deformation along the cardiac cycle by applying a rigid wall simplification, on the other side the FSI simulation requires a lot of assumptions for the material properties definition and high computational costs. The aim of this study is to investigate the feasibility of a new strategy, based on Radial Basis Functions (RBF) mesh morphing technique and transient simulations, able to introduce the patient-specific changes in aortic geometry during the cardiac cycle. Starting from medical images, aorta models at different phases of cardiac cycle were reconstructed and a transient shape deformation was obtained by proper activating incremental RBF solutions during the simulation process. The results, in terms of main hemodynamic parameters, were compared with two performed CFD simulations for the aortic model at minimum and maximum volume. Our implemented strategy copes the actual arterial variation during cardiac cycle with high accuracy, capturing the impact of geometrical variations on fluid dynamics, overcoming the complexity of a standard FSI approach.

Patient-Specific CT-Based Fluid-Structure-Interaction Aorta Model to Quantify Mechanical Conditions for the Investigation of Ascending Aortic Dilation in TOF Patients

Background

Some adult patients with Tetralogy of Fallot (TOF) were found to simultaneously develop ascending aortic dilation. Severe aortic dilation would lead to several aortic diseases, including aortic aneurysm and dissection, which seriously affect patients' living quality and even cause patients' death. Current practice guidelines of aortic-dilation-related diseases mainly focus on aortic diameter, which has been found not always a good indicator. Therefore, it may be clinically useful to identify some other factors that can potentially better predict aortic response to dilation.

Methods

20 TOF patients scheduled for TOF repair surgery were recruited in this study and were divided into dilated and nondilated groups according to the Z scores of ascending aorta diameters. Patient-specific aortic CT images, pressure, and flow rates were used in the construction of computational biomechanical models.

Results

Simulation results demonstrated a good coincidence between numerical mean flow rate at inlet and the one obtained from color Doppler ultrasonography, which implied that computational models were able to simulate the movement of the aorta and blood inside accurately. Our results indicated that aortic stress can effectively differentiate patients of the dilated group from the ones of the nondilated group. Mean ascending aortic stress-P1 (maximal principal stress) from the dilated group was 54% higher than that from the nondilated group (97.97 kPa vs. 63.47 kPa, p value = 0.044) under systolic pressure. Velocity magnitude in the aorta and aortic wall displacement of the dilated group were also greater than those of the nondilated group with p value < 0.1.

Conclusion

Computational modeling and ascending aortic biomechanical factors may be used as a potential tool to identify and analyze aortic response to dilation. Large-scale clinical studies are needed to validate these preliminary findings.