SimVascular: An Open Source Pipeline for Cardiovascular Simulation

Towards fast and reliable estimations of 3D pressure, velocity and wall shear stress in aortic blood flow: CFD-based machine learning approach

In this work, we developed deep neural networks for the fast and comprehensive estimation of the most salient features of aortic blood flow. These features include velocity magnitude and direction, 3D pressure, and wall shear stress. Starting from 40 subject-specific aortic geometries obtained from 4D Flow MRI, we applied statistical shape modeling to generate 1,000 synthetic aorta geometries. Complete computational fluid dynamics (CFD) simulations of these geometries were performed to obtain ground-truth values. We then trained deep neural networks for each characteristic flow feature using 900 randomly selected aorta geometries. Testing on remaining 100 geometries resulted in average errors of 3.11% for velocity and 4.48% for pressure. For wall shear stress predictions, we applied two approaches: (i) directly derived from the neural network-predicted velocity, and, (ii) predicted from a separate neural network. Both approaches yielded similar accuracy, with average error of 4.8 and 4.7% compared to complete 3D CFD results, respectively. We recommend the second approach for potential clinical use due to its significantly simplified workflow. In conclusion, this proof-of-concept analysis demonstrates the numerical robustness, rapid calculation speed (less than seconds), and good accuracy of the CFD-based machine learning approach in predicting velocity, pressure, and wall shear stress distributions in subject-specific aortic flows.

Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century

Since the turn of the millennium, computational modelling of biological systems has evolved remarkably and sees matured use spanning basic and clinical research. While the topic of the peri-millennial debate about the virtues and limitations of 'reductionism and integrationism' seems less controversial today, a new apparent dichotomy dominates discussions: mechanistic versus data-driven modelling. In light of this distinction, we provide an overview of recent achievements and new challenges with a focus on the cardiovascular system. Attention has shifted from generating a universal model of the human to either models of individual humans (digital twins) or entire cohorts of models representative of clinical populations to enable in silico clinical trials. Disease-specific parametrization, inter-individual and intra-individual variability, uncertainty quantification as well as interoperable, standardized and quality-controlled data are important issues today, which call for open tools, data and metadata standards, as well as strong community interactions. The quantitative, biophysical and highly controlled approach provided by in silico methods has become an integral part of physiological and medical research. In silico methods have the potential to accelerate future progress also in the fields of integrated multi-physics modelling, multi-scale models, virtual cohort studies and machine learning beyond what is feasible today. In fact, mechanistic and data-driven modelling can complement each other synergistically and fuel tomorrow's artificial intelligence applications to further our understanding of physiology and disease mechanisms, to generate new hypotheses and assess their plausibility, and thus to contribute to the evolution of preventive, diagnostic and therapeutic approaches.This article is part of the theme issue 'Science into the next millennium: 25 years on'.

Exploring the influence of parametrized pulsatility on left ventricular washout under LVAD support: a computational study using reduced-order models

Introducing pulsatility in LVADs is known to reduce complications such as stagnation and thrombosis, but it is an ongoing topic of research on what the optimal form is. We present a framework consisting of parametrized full-order simulations, reduced-order models, and sensitivity analysis to systematically quantify the effects of parametrized pulsatility on washout. As a sample problem, we study the washout in an idealized 2D left ventricle and a parametrized sinusoidal LVAD flow rate. The framework yields speed-ups proportional to the number of samples required in the sensitivity analysis. In our setting, we find that short, intense pulses wash out the left ventricle best, while the time between consecutive pulses does not play a significant role.

Importance of Considering Temporal Variations in Pulse Wave Velocity for Accurate Blood Pressure Prediction

PURPOSE

Continuous, cuffless blood pressure (BP) monitoring devices based on measuring pulse wave velocity (PWV) or pulse transit time (PTT) are emerging but are often plagued by large prediction errors. A key issue is that these techniques typically rely on a single PWV value, assuming a linear response and small arterial wall deformations. However, arterial response to BP is inherently nonlinear, with PWV varying over time [PWV(t)] by up to 50% during a cardiac cycle. This study evaluates the impact of assuming a single PWV on BP prediction accuracy.

METHOD

Using a Fluid-structure Interaction (FSI) testbed, we simulate the radial and common carotid arteries with the Holzapfel-Gasser-Ogden (HGO) constitutive model to capture nonlinear arterial behavior under a pulsatile physiological blood flow. Pressure data from FSI simulation are used as the ground truth, while inner area A(t) and two PWV values, at diastole and systole, serve as inputs to BP prediction models. Two models are tested: one using a single PWV value, emulating existing PWV-based BP prediction methods; another using the two PWV values to account for PWV(t).

RESULTS

The single-PWV BP model produced prediction errors of 17.44 mmHg and 6.57 mmHg for the radial and carotid arteries, respectively. The model incorporating two PWV values reduced these errors by 90.6% and 96.8%, respectively.

CONCLUSION

Relying on a single PWV in BP prediction models can lead to significant errors. To improve BP accuracy, future efforts should focus on incorporating PWV(t), or at least both diastolic and systolic PWV values, into these models.

Hemodynamics indicates differences between patients with and without a stroke outcome after left ventricular assist device implantation

Stroke remains a leading cause of complications and mortality in heart failure patients treated with a Left Ventricular Assist Device (LVAD). Hemodynamics plays a central role underlying post-LVAD stroke risk and etiology. Yet, detailed quantitative assessment of hemodynamic variables and their relation to stroke outcomes in patients on LVAD support remains a challenge. Modalities for pre-implantation assessment of post-implantation hemodynamics can help address this challenge. We present an in silico hemodynamics analysis for a digital twin cohort 12 patients on LVAD support; 6 with reported stroke outcomes and 6 without. For each patient we created a post-implant twin with the LVAD outflow graft reconstructed from cardiac-gated CT images; and a pre-implant twin of an estimated baseline flow by removing the LVAD outflow graft and driving flow from the aortic valve opening. Hemodynamics was characterized using descriptors for helical flow, vortex generation, and wall shear stress. We observed higher average values for descriptors of positive helical flow, vortex generation, and wall shear stress, across the 6 cases with stroke outcomes when compared with cases without stroke. When the descriptors for LVAD-driven flow were compared against estimated pre-implantation flow, extent of positive helicity was higher, and vorticity and wall shear were lower in cases with stroke compared to those without. Our study suggests that quantitative analysis of hemodynamics after LVAD implantation; and hemodynamic alterations from a pre-implant flow scenario, can potentially reveal hidden information linked to stroke outcomes during LVAD support. This has broad implications on understanding stroke etiology; and using patient digital twins for LVAD treatment planning, surgical optimization, and efficacy assessment.

Patient-specific coronary angioplasty simulations - A mixed-dimensional finite element modeling approach

Coronary angioplasty with stent implantation is the most frequently used interventional treatment for coronary artery disease. However, reocclusion within the stent, referred to as in-stent restenosis, occurs in up to 10% of lesions. It is widely accepted that mechanical loads on the vessel wall strongly affect adaptive and maladaptive mechanisms. Yet, the role of procedural and lesion-specific influence on restenosis risk remains understudied. Computational modeling of the stenting procedure can provide new mechanistic insights, such as local stresses, that play a significant role in tissue growth and remodeling. Previous simulation studies often featured simplified artery and stent geometries and cannot be applied to real-world examples. Realistic simulations were computationally expensive since they featured fully resolved stenting device models. The aim of this work is to develop and present a mixed-dimensional formulation to simulate the patient-specific stenting procedure with a reduced-dimensional beam model for the stent and 3D models for the artery. In addition to presenting the numerical approach, we apply it to realistic cases to study the intervention's mechanical effect on the artery and correlate the findings with potential high-risk locations for in-stent restenosis. We found that high artery wall stresses develop during the coronary intervention in severely stenosed areas and at the stent boundaries. Herewith, we lay the groundwork for further studies towards preventing in-stent restenosis after coronary angioplasty.

Enhancing the predictive capability of magnetic resonance imaging using medical data-supervised cardiovascular flow simulations: A case study for analyzing patient-specific flow in the human aorta

BACKGROUND

Detailed hemodynamic parameters are essential for managing cardiovascular diseases, as they reveal blood flow dynamics that affect disease progression and treatment. However, even such advanced techniques as 4D Phase Contrast MRI face challenges in providing accurate, high-resolution data due to limitations in spatial and temporal resolution and image artifacts. Computational Fluid Dynamics (CFD) can estimate these parameters theoretically, but patient-specific accuracy may be compromised due to assumptions in boundary conditions and material properties.

METHOD

Here, we aim to circumvent current limitations in medical imaging and CFD simulations by creating a comprehensive cardiovascular analytics model informed by clinical data. We develop a patient-specific simulation framework by deriving critical geometric parameters, boundary conditions, and aortic wall material properties directly from medical investigation and imaging data. This detailed information is subsequently integrated into Fluid-Structure-Interaction simulations to predict such key hemodynamic indicators as pressure distribution, wall deformation, time-averaged wall shear stress and oscillatory shear index to better assess individual vascular health. This approach effectively links imaging technology with computational modeling, as evidenced from our findings based on the medical imaging data of a representative human subject.

RESULTS AND CONCLUSION

The results reveal that such amalgamation of patient-specific parameters enhances the simulation's accuracy, offering a more comprehensive and precise assessment of cardiovascular health than the traditional generic approaches. This comprehensive framework thus has potential to become an invaluable clinical tool, enhancing the accuracy of hemodynamic assessment, moving toward more personalized care and informing effective treatment decision-making.

Understanding embolus transport and source to destination mapping of thromboemboli in hemodynamics driven by left ventricular assist device

Left Ventricular Assist Devices (LVADs) are a key treatment option for patients with advanced heart failure, but they carry a significant risk of thromboembolic complications. While improved LVAD design, and systemic anticoagulation regimen, have helped mitigate thromboembolic risks, ischemic stroke due to adverse thromboembolic events remains a major concern with current LVAD therapies. Improved understanding of embolic events, and embolus movement to the brain, is critical to develop techniques to minimize risks of occlusive embolic events such as a stroke after LVAD implantation. Here, we address this need, and devise a quantitative in silico framework to characterize thromboembolus transport and distrbution in hemodynamics driven by an operating LVAD. We conduct systematic numerical experiments to establish that our framework can quantify the source-to-destination transport patterns of thromboemboli as a function of: LVAD outflow graft anastomosis, LVAD operating pulse modulation, thromboembolus sizes, and origin locations of emboli. Additionally, we demonstrate how the resulting embolus distribution patterns compare and correlate with descriptors based solely on hemodynamic patterns such as helicity, vorticity, and wall shear stress. Using the concepts of size-dependent embolus-hemodynamics interactions, and jet impingement driven flow for hemodynamics under LVAD operation as established in our prior works, we gain valuable insights on departure of thromboembolus distribution from flow distribution, and establish that our in silico model can generate deep insights into embolus dynamics which is not otherwise available from standard of care imaging and clinical data.

Turbulent-like blood flow in neo-aorta in post-norwood patients

BACKGROUND AND OBJECTIVES

Surgical reconstruction of the aortic arch in children born with hypoplastic left heart syndrome (HLHS) can induce disturbed or turbulent-like blood flow patterns characterized by high-frequency velocity fluctuations (turbulent-like flow). However, previous studies have primarily focused on laminar flows as a surrogate marker of aorto-ventricular inefficiencies. In this study, we utilized high-resolution computational fluid dynamics (CFD) simulations and frequency-based analysis to demonstrate that abnormal neo-aortic geometries resulting from post-surgical anatomies can induce turbulent-like blood flow patterns.

MATERIALS AND METHODS

Four patients with various HLHS conditions after the Norwood surgery were included: i) two with healthy, unobstructed neo-aortic reconstructions; and ii) two with neo-aortic abnormalities, including stenosis and arch dilations. Two controls included were variants of single ventricle condition with a normal aorta. A novel geometric parameter, Diameter Deviation, was developed that quantified diameter irregularities while accounting for natural tapering of the aorta. High-resolution CFD simulations were performed and hemodynamic parameters, including wall shear stresses (WSS), oscillatory shear index (OSI) and spectral power index (SPI) were assessed; the latter quantified turbulent intensities. Power spectral density (PSD) of the velocity-time signal in the descending aorta was analysed.

RESULTS

SPI in diseased cases (Stenotic and Aneurysmal) was four to six-fold higher compared to the Control and Healthy cases, highlighting the presence of turbulent-like blood flow. Diameter Deviation was one-to three-fold higher in the diseased cases compared to the Control and Healthy cases. PSD analysis showed notable energy content even at frequencies as high as 1000 Hz in the diseased cases that were indictive of turbulent-like flow phenomenon.

CONCLUSION

Abnormal diameter irregularities may induce turbulent-like flow patterns that could be detrimental to the healthy growth of the neo-aorta. Post-surgical outcomes could potentially be improved through optimal neo-aortic reconstructions that minimize diameter irregularities and turbulent-like blood flow conditions.

Analysis of the hemodynamic impact of coronary plaque morphology in mild coronary artery stenosis

OBJECTIVES

As is well known, plaque morphology plays an important role in the hemodynamics of stenotic coronary arteries, thus their clinic outcomes. However, so far, there has been no research on how the cross-sectional shape of a stenotic lumen affects its hemodynamics. Therefore, this study aims to explore the impact of plaque cross-sectional shape on coronary hemodynamics under mild or moderate stenosis conditions (diameter stenosis degree ≤50 %).

METHODS

A three-dimensional model of the coronary tree was established using CT images of a subject without coronary stenosis. Based on real CT images of patients, six types of plaque cross-sectional morphologies were created at the same location in one main left coronary artery model, controlling for 50 % and 25 % diameter stenosis, respectively. Computational fluid dynamics (CFD) simulations were performed on the six stenosed coronary models and one normal control model under the same boundary conditions. The differences in hemodynamic results among the models were compared.

RESULTS

(1) Type III plaque caused the largest disturbance in the flow field. (2) In type IV plaque, the area with an oscillatory shear index (OSI) >0.1 accounted for 11.18 %. (3) Type V plaque exhibited the most prominent vortex flow lines. (4) Hemodynamic parameters within type VI plaques were most similar to those of normal coronary arteries. (5) Area stenosis better reflects the severity of coronary stenosis.

CONCLUSION

Different cross-sectional morphologies can lead to abnormalities in different hemodynamic parameters, leading to different clinical outcomes. Especially, type III plaques are most likely to cause vascular wall damage, while type V plaques warrant caution due to the risk of complications such as thrombosis. Considering plaque cross-sectional morphology can provide doctors with more information and theoretical support for diagnosis.

Noninvasive estimation of central blood pressure through fluid-structure interaction modeling

Central blood pressure (cBP) is considered a superior indicator of cardiovascular fitness than brachial blood pressure (bBP). Even though bBP is easy to measure noninvasively, it is usually higher than cBP due to pulse wave amplification, characterized by the gradual increase in peak systolic pressure during pulse wave propagation. In this study, we aim to develop an individualized transfer function that can accurately estimate cBP from bBP. We first construct a three-dimensional, patient-specific model of the upper limb arterial system using fluid-structure interaction simulations, incorporating variable material properties and complex boundary conditions. Then, we develop an analytical brachial-aortic transfer function based on novel solutions for compliant vessels. The accuracy of this transfer function is successfully validated against numerical simulation results, which effectively reproduce pulse wave propagation and amplification, with key hemodynamic parameters falling within the range of clinical measurements. Further analysis of the transfer function reveals that cBP is a linear combination of bBP and aortic flow rate in the frequency domain, with the coefficients determined by vessel geometry, material properties, and boundary conditions. Additionally, bBP primarily contributes to the steady component of cBP, while the aortic flow rate is responsible for the pulsatile component. Furthermore, local sensitivity analysis indicates that the lumen radius is the most influential parameter in accurately estimating cBP. Although not directly applicable clinically, the proposed transfer function enhances understanding of the underlying physics-highlighting the importance of aortic flow and lumen radius-and can guide the development of more practical transfer functions.

SIRE: Scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks

The orientation of a blood vessel as visualized in 3D medical images is an important descriptor of its geometry that can be used for centerline extraction and subsequent segmentation, labeling, and visualization. Blood vessels appear at multiple scales and levels of tortuosity, and determining the exact orientation of a vessel is a challenging problem. Recent works have used 3D convolutional neural networks (CNNs) for this purpose, but CNNs are sensitive to variations in vessel size and orientation. We present SIRE: a scale-invariant rotation-equivariant estimator for local vessel orientation. SIRE is modular and has strongly generalizing properties due to symmetry preservations. SIRE consists of a gauge equivariant mesh CNN (GEM-CNN) that operates in parallel on multiple nested spherical meshes with different sizes. The features on each mesh are a projection of image intensities within the corresponding sphere. These features are intrinsic to the sphere and, in combination with the gauge equivariant properties of GEM-CNN, lead to SO(3) rotation equivariance. Approximate scale invariance is achieved by weight sharing and use of a symmetric maximum aggregation function to combine predictions at multiple scales. Hence, SIRE can be trained with arbitrarily oriented vessels with varying radii to generalize to vessels with a wide range of calibres and tortuosity. We demonstrate the efficacy of SIRE using three datasets containing vessels of varying scales; the vascular model repository (VMR), the ASOCA coronary artery set, and an in-house set of abdominal aortic aneurysms (AAAs). We embed SIRE in a centerline tracker which accurately tracks large calibre AAAs, regardless of the data SIRE is trained with. Moreover, a tracker can use SIRE to track small-calibre tortuous coronary arteries, even when trained only with large-calibre, non-tortuous AAAs. Additional experiments are performed to verify the rotational equivariant and scale invariant properties of SIRE. In conclusion, by incorporating SO(3) and scale symmetries, SIRE can be used to determine orientations of vessels outside of the training domain, offering a robust and data-efficient solution to geometric analysis of blood vessels in 3D medical images.

Hemodynamics of Proximal Coronary Lesions in Patients Undergoing Transcatheter Aortic Valve Implantation: Patient-Specific In Silico Study

Aortic stenosis (AS) frequently coexists with coronary artery disease (CAD), complicating revascularization decisions. The use of coronary physiology indices, such as the fractional flow reserve (FFR), instantaneous wave-free ratio (iFR), and coronary flow reserve (CFR), in AS patients remains debated, particularly after transcatheter aortic valve implantation (TAVI). In this study, we employ computational fluid dynamics (CFD) to evaluate coronary hemodynamics and assess changes in the wall shear stress (WSS) before and after TAVI. Our analysis demonstrates strong agreement between CFD-derived and invasive FFR measurements, confirming CFD's reliability as a non-invasive tool for coronary physiology assessment. Furthermore, our results show no significant changes in FFR (p=0.92), iFR (p=0.67), or CFR (p=0.34) post-TAVI, suggesting that these indices remain stable following aortic valve intervention. However, a significant reduction in high WSS exposure (59% to 40.8%, p<0.001) and the oscillatory shear index (OSI: 0.32 to 0.21, p<0.001) was observed, indicating improved hemodynamic stability. These findings suggest that coronary physiology indices remain reliable for revascularization guidance post-TAVI and highlight a potential beneficial effect of aortic stenosis treatment on plaque shear stress dynamics. Our study underscores the clinical utility of CFD modeling in CAD management, paving the way for further research into its prognostic implications.

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.

Oversized Conduits Predict Stenosis in Tissue Engineered Vascular Grafts

Tissue-engineered vascular grafts (TEVGs) offer promising advancements in treating congenital heart disease by enabling the creation of autologous tissue for complex cardiac repairs. Our approach involves implanting biodegradable scaffolds seeded with autologous cells that remodel into functional neovessels. To understand better the factors guiding neovessel formation, we evaluated 50 ovine thoracic TEVGs using angiography at 1 and 6 weeks postimplantation. Nondimensionalization accounted for anatomical differences between animals and identified hemodynamics and surgical sizing as potential driving factors. Regression analysis revealed that narrowing at the inflow anastomosis and graft oversizing correlated significantly with stenosis development. Computational fluid dynamics showed that these factors influenced wall shear stress and flow patterns, contributing to neovessel narrowing. Comparisons with clinical trial data from Fontan conduits supported these findings, emphasizing that matching graft size to the native inflow vessel can reduce stenosis and enhance TEVG performance.

Dynamic Virtual Simulation with Real-Time Haptic Feedback for Robotic Internal Mammary Artery Harvesting

Coronary heart disease, a leading global cause of mortality, has witnessed significant advancement through robotic coronary artery bypass grafting (CABG), with the internal mammary artery (IMA) emerging as the preferred "golden conduit" for its exceptional long-term patency. Despite these advances, robotic-assisted IMA harvesting remains challenging due to the absence of force feedback, complex surgical maneuvers, and proximity to the beating heart. This study introduces a novel virtual simulation platform for robotic IMA harvesting that integrates dynamic anatomical modeling and real-time haptic feedback. By incorporating a dynamic cardiac model into the surgical scene, our system precisely simulates the impact of cardiac pulsation on thoracic cavity operations. The platform features high-fidelity representations of thoracic anatomy and soft tissue deformation, underpinned by a comprehensive biomechanical framework encompassing fascia, adipose tissue, and vascular structures. Our key innovations include a topology-preserving cutting algorithm, a bidirectional tissue coupling mechanism, and dual-channel haptic feedback for electrocautery simulation. Quantitative assessment using our newly proposed Spatial Asymmetry Index (SAI) demonstrated significant behavioral adaptations to cardiac motion, with dynamic scenarios yielding superior SAI values compared to static conditions. These results validate the platform's potential as an anatomically accurate, interactive, and computationally efficient solution for enhancing surgical skill acquisition in complex cardiac procedures.

Physics-constrained coupled neural differential equations for one dimensional blood flow modeling

BACKGROUND

Computational cardiovascular flow modeling plays a crucial role in understanding blood flow dynamics. While 3D models provide acute details, they are computationally expensive, especially with fluid-structure interaction (FSI) simulations. 1D models offer a computationally efficient alternative, by simplifying the 3D Navier-Stokes equations through axisymmetric flow assumption and cross-sectional averaging. However, traditional 1D models based on finite element methods (FEM) often lack accuracy compared to 3D averaged solutions.

METHODS

This study introduces a novel physics-constrained machine learning technique that enhances the accuracy of 1D cardiovascular flow models while maintaining computational efficiency. Our approach, utilizing a physics-constrained coupled neural differential equation (PCNDE) framework, demonstrates superior performance compared to conventional FEM-based 1D models across a wide range of inlet boundary condition waveforms and stenosis blockage ratios. A key innovation lies in the spatial formulation of the momentum conservation equation, departing from the traditional temporal approach and capitalizing on the inherent temporal periodicity of blood flow.

RESULTS

This spatial neural differential equation formulation switches space and time and overcomes issues related to coupling stability and smoothness, while simplifying boundary condition implementation. The model accurately captures flow rate, area, and pressure variations for unseen waveforms and geometries, having 3-5 times smaller error than 1D FEM, and less than 1.2% relative error compared to 3D averaged training data. We evaluate the model's robustness to input noise and explore the loss landscapes associated with the inclusion of different physics terms.

CONCLUSION

This advanced 1D modeling technique offers promising potential for rapid cardiovascular simulations, achieving computational efficiency and accuracy. By combining the strengths of physics-based and data-driven modeling, this approach enables fast and accurate cardiovascular simulations.

Assessment of Aortic Dissection Remodeling With Patient-Specific Fluid-Structure Interaction Models

Aortic dissection leads to late complications due tochronic degeneration and dilatation of the false lumen. This study examines the interaction between hemodynamics and long-term remodeling of a patient's aortic dissection, tracked from pre-dissection to the chronic phase using CT angiography. Fluid-structure interaction models with tissue prestress, external support, and anisotropic properties were used to analyze hemodynamic markers. Each aortic wall layer had distinct thicknesses and material properties. The boundary conditions were guided by in vitro 4D-flow MRI and the patient's blood pressure. Aortic dilatation was most significant distal to the left subclavian artery, reaching 6 cm in the chronic phase. Simulations quantified the flow jet velocity through the entry tear, which peaked at 185 cm/s in the subacute phase and decreased to 123 to 133 cm/s in the chronic phase, corresponding to an increased entry tear size. Flow jet impingement on the false lumen resulted in a localized pressure increase of 11 and 2 mmHg in the subacute and chronic phases, with wall shear stress reaching 4 Pa. These hemodynamic changes appear to be the main drivers of aortic growth and morphological changes. Despite moderate overall flap movement, in-plane displacement increased from 0.6 to 1.8 mm as disease progressed, which was associated with an overall increase in aortic diameter. Simulations with a significant reduction in flap stiffness during the subacute phase resulted in increased flap motion up to 9.5 mm. Although these results are based on a single patient, they suggest a strong relationship between hemodynamics and aortic growth.

Hemodynamics Indicates Differences Between Patients With And Without A Stroke Outcome After Left Ventricular Assist Device Implantation

Stroke remains a leading cause of complications and mortality in heart failure patients treated with a Left Ventricular Assist Device (LVAD). Hemodynamics plays a central role underlying post-LVAD stroke risk and etiology. Yet, detailed quantitative assessment of hemodynamic variables and their relation to stroke outcomes in patients on LVAD support remains a challenge. Modalities for pre-implantation assessment of post-implantation hemodynamics can help address this challenge. We present an in silico hemodynamics analysis for a digital twin cohort 12 patients on LVAD support; 6 with reported stroke outcomes and 6 without. For each patient we created a post-implant twin with the LVAD outflow graft reconstructed from cardiac-gated CT images; and a pre-implant twin of an estimated baseline flow by removing the LVAD outflow graft and driving flow from the aortic valve opening. Hemodynamics was characterized using descriptors for helical flow, vortex generation, and wall shear stress. We observed higher average values for descriptors of positive helical flow, vortex generation, and wall shear stress, across the 6 cases with stroke outcomes when compared with cases without stroke. When the descriptors for LVAD-driven flow were compared against estimated pre-implantation flow, extent of positive helicity was higher, and vorticity and wall shear were lower in cases with stroke compared to those without. Our study suggests that quantitative analysis of hemodynamics after LVAD implantation; and hemodynamic alterations from a pre-implant flow scenario, can potentially reveal hidden information linked to stroke outcomes during LVAD support. This has broad implications on understanding stroke etiology; and using patient digital twins for LVAD treatment planning, surgical optimization, and efficacy assessment.

A Matlab Toolbox for cardiac electrophysiology simulations on patient-specific geometries

In this paper, we present CardioMat, a Matlab toolbox for cardiac electrophysiology simulation based on patient-specific anatomies. The strength of CardioMat is the easy and fast construction of electrophysiology cardiac digital twins from segmented anatomical images in a general-purpose software such as Matlab. CardioMat implements a quasi-automatic pipeline that guides the user toward the construction of anatomically detailed cardiac electrophysiology models. Importantly, the CardioMat framework includes the generation of physiologically plausible fiber orientation and Purkinje networks. The main novelty of our framework is its ability to handle voxel-based geometries as produced by segmentation procedures directly, without the need for an unstructured mesh. Indeed, the CardioMat monodomain solver uses a smoothed boundary approach and runs completely on GPU for fast simulations. We employed CardioMat in different application scenarios to show its potentialities and provide preliminary assessment of the feasibility, diagnostic performance, and accuracy of the toolbox. In particular, we showed that CardioMat simulations derived from post-infarction patients hold high sensitivity, specificity, predictive value, and accuracy for localization of deceleration zones in sinus rhythm.

Fluid-structure interaction simulations for the prediction of fractional flow reserve in pediatric patients with anomalous aortic origin of a coronary artery

Computer simulations of blood flow in patients with anomalous aortic origin of a coronary artery (AAOCA) have the promise to provide insight into this complex disease. They provide an in silico experimental platform to explore possible mechanisms of myocardial ischemia, a potentially deadly complication for patients with this defect. This paper focuses on the question of model calibration for fluid-structure interaction models of pediatric AAOCA patients. Imaging and cardiac catheterization data provide partial information for model construction and calibration. However, parameters for downstream boundary conditions needed for these models are difficult to estimate. Further, important model predictions, like fractional flow reserve (FFR), are sensitive to these parameters. We describe an approach to calibrate downstream boundary condition parameters to clinical measurements of resting FFR. The calibrated models are then used to predict FFR at stress, an invasively measured quantity that can be used in the clinical evaluation of these patients. We find reasonable agreement between the model predicted and clinically measured FFR at stress, indicating the credibility of this modeling framework for predicting hemodynamics of pediatric AAOCA patients. This approach could lead to important clinical applications since it may serve as a tool for risk stratifying children with AAOCA.

Hemodynamic Analysis in Aortic Dilatation after Arterial Switch Operation for Patients with Transposition of Great Arteries Using Computational Fluid Dynamics

After an arterial switch operation for complete transposition of the great arteries, neo-aortic root dilatation occurs, with unclear hemodynamic effects. This study analyzes three groups (severe dilation, mild dilation, and normal) using computational fluid dynamics (CFD) on cardiac CT scans. Aortic arch angles in severe (median 72.3, range: 68.5-77.2) and mild dilation (76.6, 71.1-85.2) groups are significantly smaller than the normal group (97.3, 87.4-99.0). In the normal and mild dilatation groups, Wall Shear Stress (WSS) exhibits a consistent pattern: it is lowest at the aortic root, gradually increases until just before the bend in the aortic arch, peaks, and then subsequently decreases. However, severe dilation shows disrupted WSS patterns, notably lower in the distal ascending aorta, attributed to local recirculation. This unique WSS pattern observed in severely dilated patients, especially in the transverse aorta. CFD plays an essential role in comprehensively studying the pathophysiology underlying aortic dilation in this population.

Computational Fluid Dynamic Assessment of Patients with Congenital Heart Disease from 3D Rotational Angiography

For congenital heart disease patients, multiple imaging modalities are needed to discern anatomy and functional information such as differential blood flow. During cardiac catheterization, 3D rotational angiography (3DRA) can provide CTA-like images, enabling anatomical information and intraprocedural guidance. We seek to establish whether unique aspects of this technique can also generate quantitative functional blood flow information. We propose that systematic integration of 3DRA imaging, catheter hemodynamic information, and computational fluid dynamics (CFD), can provide quantitative information regarding blood flow dynamics and energetics, without additional imaging or procedures. We report a single center retrospective feasibility study comprising four patients with 3DRA imaging and a complete set of hemodynamic data. 3DRA was processed and segmented to reconstruct vascular regions of interest (ROI), and a computational grid for CFD modeling of blood flow through the ROI was generated. Blood flow was simulated by integrating catheter hemodynamic data to devise boundary conditions at vascular ROI inlets and outlets. The 3DRA-based workflow successfully generated key computational outputs commonly used for cardiovascular applications, including flow patterns, distribution fractions, wall shear stress. Computational outputs obtained were as detailed and resolved as those obtained from more commonly used CT or MR angiography. Accuracy was confirmed by comparing computed flow distributions with measurements for 2 cases, showing less than 2.0% error from the measured data. Systematic integration of catheter hemodynamic information, 3DRA imaging, and CFD modeling, provides an effective and feasible alternative to obtain important quantitative blood flow information and visualization, without additional imaging.

Sensitivity of coronary hemodynamics to vascular structure variations in health and disease

Local hemodynamics play an essential role in the initiation and progression of coronary artery disease. While vascular geometry alters local hemodynamics, the relationship between vascular structure and hemodynamics is poorly understood. Previous computational fluid dynamics (CFD) studies have explored how anatomy influences plaque-promoting hemodynamics. For example, areas exposed to low wall shear stress (ALWSS) can indicate regions of plaque growth. However, small sample sizes, idealized geometries, and simplified boundary conditions have limited their scope. We generated 230 synthetic models of left coronary arteries and simulated coronary hemodynamics with physiologically realistic boundary conditions. We measured the sensitivity of hemodynamic metrics to changes in bifurcation angles, positions, diameter ratios, tortuosity, and plaque topology. Our results suggest that the diameter ratio between left coronary branches plays a substantial role in generating adverse hemodynamic phenotypes and can amplify the effect of other geometric features such as bifurcation position and angle, and vessel tortuosity. Introducing mild plaque in the models did not change correlations between structure and hemodynamics. However, certain vascular structures can induce ALWSS at the trailing edge of the plaque. Our analysis demonstrates that coronary artery vascular structure can provide key insight into the hemodynamic environments conducive to plaque formation and growth.

SeqSeg: Learning Local Segments for Automatic Vascular Model Construction

Computational modeling of cardiovascular function has become a critical part of diagnosing, treating and understanding cardiovascular disease. Most strategies involve constructing anatomically accurate computer models of cardiovascular structures, which is a multistep, time-consuming process. To improve the model generation process, we herein present SeqSeg (sequential segmentation): a novel deep learning based automatic tracing and segmentation algorithm for constructing image-based vascular models. SeqSeg leverages local U-Net-based inference to sequentially segment vascular structures from medical image volumes. We tested SeqSeg on CT and MR images of aortic and aortofemoral models and compared the predictions to those of benchmark 2D and 3D global nnU-Net models, which have previously shown excellent accuracy for medical image segmentation. We demonstrate that SeqSeg is able to segment more complete vasculature and is able to generalize to vascular structures not annotated in the training data.

Standardizing plaque impact on overall coronary hemodynamics using a binomial equation based on the equivalent energy loss

This study proposes a novel method for evaluating the risk of adverse events (AE) in patients with coronary stenosis based on the morphology and hemodynamics along a whole coronary artery. Twenty-eight specific coronary artery tree models with different stenotic degrees are established from the CCTA images and divided into AE group and Non-AE group. Pressures are obtained by computational fluid dynamics method. The left anterior descending branches are divided equidistantly along the centerline. The characteristic diameters of each segment are measured and normalized to standard the patient-specific coronary arteries as characteristic straight pipes with variable cross-sections. Based on the energy loss theory, the normalized characteristic diameters ( [Formula: see text] ) and pressure drops (Δpi) of each segment are fitted to a binomial equation. The differences of binomial coefficients between the two groups are compared. The results show that: [Formula: see text] changes suddenly in the lesions part and Δpi fluctuates in the posterior half of lesions part and its upstream and downstream. There is a significant difference in the ratio of two binomial equation coefficients, which represents the combination of local resistance coefficient, length, flow rate and maximum characteristic diameter of the standard pipe (0.93 ± 0.16 vs. 1.42 ± 0.58, p = 0.0003). This method emphasizes the influences of stenosis on the whole coronary artery, and reflects the cardiac function requirements of the stenotic coronary artery from the patient itself. The ratio of two binomial equation coefficients can supplement the information obtained by existing detection methods and may help evaluate the risk of AEs.

Hybrid physics-based and data-driven modeling of vascular bifurcation pressure differences

Reduced-order models allow for the simulation of blood flow in patient-specific vasculatures. They offer a significant reduction in computational cost and wait time compared to traditional computational fluid dynamics models. Unfortunately, due to the simplifications made in their formulations, reduced-order models can suffer from significantly reduced accuracy. One common simplifying assumption is that of continuity of static or total pressure over vascular bifurcations. In many cases, this assumption has been shown to introduce significant errors in pressure predictions. We propose a model to account for this pressure difference, with the ultimate goal of increasing the accuracy of cardiovascular reduced-order models. Our model successfully uses a structure common in existing reduced-order models in conjunction with machine-learning techniques to predict the pressure difference over a vascular bifurcation. We analyze the performance of our model on steady and transient flows, testing it on three bifurcation cohorts representing three different bifurcation geometric types. We find that our model makes significantly more accurate predictions than other models for approximating bifurcation pressure losses commonly used in the reduced-order cardiovascular modeling community. We also compare the efficacy of different machine-learning techniques and observe that a neural network performs most robustly. Additionally, we consider two different model modalities: one in which the model is fit using both steady and transient flows, and one in which it is optimized for performance in transient flows. We discuss the trade-off between the physical interpretability associated with the first option and the improved accuracy in transient flows associated with the latter option. We also demonstrate the model's ability to generalize by testing it on a combined dataset containing two different bifurcation types. This work marks a step towards improving the accuracy of cardiovascular reduced-order models, thereby increasing their utility for cardiovascular flow modeling.

Automatic Laplacian-based shape optimization for patient-specific vascular grafts

Cognitional heart disease is one of the leading causes of mortality among newborns. Tissue-engineered vascular grafts offer the potential to help treat cognitional heart disease through patient-specific vascular grafts. However, current methods often rely on non-personalized designs or involve significant human intervention. This paper presents a computational framework for the automatic shape optimization of patient-specific tissue-engineered vascular grafts for repairing the aortic arch, aimed at reducing the need for manual input and improving current treatment outcomes, which either use non-patient-specific geometry or require extensive human intervention to design the vascular graft. The paper's core innovation lies in an automatic shape optimization pipeline that combines Bayesian optimization techniques with the open-source finite volume solver, OpenFOAM, and a novel graft deformation algorithm. Specifically, our framework begins with Laplacian mode computation and the approximation of a computationally low-cost Gaussian process surrogate model to capture the minimum weighted combination of inlet-outlet pressure drop (PD) and maximum wall shear stress (WSS). Bayesian Optimization then performs a limited number of OpenFOAM simulations to identify the optimal patient-specific shape. We use imaging and flow data obtained from six patients diagnosed with cognitional heart disease to evaluate our approach. Our results showcase the potential of online training and hemodynamic surrogate model optimization for providing optimal graft shapes. These results show how our framework successfully reduces inlet-outlet PD and maximum WSS compared to pre-lofted models that include both the native geometry and human-designed grafts. Furthermore, we compare how the performance of each design optimized under steady-state simulation compares to that design's performance under transient simulation, and to what extent the optimal design remains similar under both conditions. Our findings underscore that the automated designs achieve at least a 16% reduction in blood flow pressure drop in comparison to geometries optimized by humans.

Modeling Fibrous Tissue in Vascular Fluid-Structure Interaction: A Morphology-Based Pipeline and Biomechanical Significance

Modeling fibrous tissue for vascular fluid-structure interaction analysis poses significant challenges due to the lack of effective tools for preparing simulation data from medical images. This limitation hinders the physiologically realistic modeling of vasculature and its use in clinical settings. Leveraging an established lumen modeling strategy, we propose a comprehensive pipeline for generating thick-walled artery models. A specialized mesh generation procedure is developed to ensure mesh continuity across the lumen and wall interface. Exploiting the centerline information, a series of procedures are introduced for generating local basis vectors within the arterial wall. The procedures are tailored to handle thick-walled tissues where basis vectors may exhibit transmural variations. Additionally, we propose methods for accurately identifying the centerline in multi-branched vessels and bifurcating regions. These modeling approaches are algorithmically implementable, rendering them readily integrable into mainstream cardiovascular modeling software. The developed fiber generation method is evaluated against the strategy using linear elastostatics analysis, demonstrating that the proposed approach yields satisfactory fiber definitions in the considered benchmark. Finally, we examine the impact of anisotropic arterial wall models on the vascular fluid-structure interaction analysis through numerical examples, employing the neo-Hookean model for comparative purposes. The first case involves an idealized curved geometry, while the second studies an image-based abdominal aorta model. Our numerical results reveal that the deformation and stress distribution are critically related to the constitutive model of the wall, whereas hemodynamic factors are less sensitive to the wall model. This work paves the way for more accurate image-based vascular modeling and enhances the prediction of arterial behavior under physiologically realistic conditions.

openBF: an open-source finite volume 1D blood flow solver

Computational simulations are widely adopted in cardiovascular biomechanics because of their capability of producing physiological data otherwise impossible to measure with non-invasive modalities.Objective.This study presents openBF, a computational library for simulating the blood dynamics in the cardiovascular system.Approach.openBF adopts a one-dimensional viscoelastic representation of the arterial system, and is coupled with zero-dimensional windkessel models at the outlets. Equations are solved by means of the finite-volume method and the code is written in Julia. We assess its predictions by performing a multiscale validation study on several domains available from the literature.Main results.At all scales, which range from individual arteries to a population of virtual subjects, openBF's solution show excellent agreement with the solutions from existing software. For reported simulations, openBF requires low computational times.Significance.openBF is easy to install, use, and deploy on multiple platforms and architectures, and gives accurate prediction of blood dynamics in short time-frames. It is actively maintained and available open-source on GitHub, which favours contributions from the biomechanical community.

An automated and time-efficient framework for simulation of coronary blood flow under steady and pulsatile conditions

BACKGROUND AND OBJECTIVE

Invasive fractional flow reserve (FFR) measurement is the gold standard method for coronary artery disease (CAD) diagnosis. FFR-CT exploits computational fluid dynamics (CFD) for non-invasive evaluation of FFR, simulating coronary flow in virtual geometries reconstructed from computed tomography (CT), but suffers from cost-intensive computing process and uncertainties in the definition of patient specific boundary conditions (BCs). In this work, we investigated the use of time-averaged steady BCs, compared to pulsatile to reduce the computational time and deployed a self-adjusting method for the tuning of BCs to patient-specific clinical data.

METHODS

133 coronary arteries were reconstructed form CT images of patients suffering from CAD. For each vessel, invasive FFR was measured. After segmentation, the geometries were prepared for CFD simulation by clipping the outlets and discretizing into tetrahedral mesh. Steady BCs were defined in two steps: (i) rest BCs were extrapolated from clinical and image-derived data; (ii) hyperemic BCs were computed from resting conditions. Flow rate was iteratively adjusted during the simulation, until patient's aortic pressure was matched. Pulsatile BCs were defined exploiting the convergence values of steady BCs. After CFD simulation, lesion-specific hemodynamic indexes were computed and compared between group of patients for which surgery was indicated and not. The whole pipeline was implemented as a straightforward process, in which each single step is performed automatically.

RESULTS

Steady and pulsatile FFR-CT yielded a strong correlation (r = 0.988, p < 0.001) and correlated with invasive FFR (r = 0.797, p < 0.001). The per-point difference between the pressure and FFR-CT field predicted by the two methods was below 1 % and 2 %, respectively. Both approaches exhibited a good diagnostic performance: accuracy was 0.860 and 0.864, the AUC was 0.923 and 0.912, for steady and pulsatile case, respectively. The computational time required by steady BCs CFD was approximatively 30-folds lower than pulsatile case.

CONCLUSIONS

This work shows the feasibility of using steady BCs CFD for computing the FFR-CT in coronary arteries, as well as its computational and diagnostic performance within a fully automated pipeline.

Effective property method for efficient modeling of non-uniform tissue support in fluid-structure interaction simulation of blood flows

BACKGROUND AND OBJECTIVE

Incorporating tissue support in fluid-structure interaction analysis of cardiovascular flows is crucial for accurately representing physiological constraints, achieving realistic vessel wall motion, and minimizing artificial oscillations. The generalized Robin boundary condition, which models tissue support with a spring-damper-type force, uses elastic and damping parameters to represent the viscoelastic behavior of perivascular tissues. Using spatially distributed parameters for tissue support, rather than uniform ones, is more realistic and aligns with the varying properties of vessel walls. However, considering the spatial distribution of both can increase the complexity of preprocessing and numerical implementation. In this work, we develop an effective property method for efficient modeling of non-uniform tissue support and quantifying the contribution of tissue support to the mechanical behaviors of vessel walls.

METHODS

Based on the theory of linear viscoelasticity, we derive the mathematical formulas for the effective property method, integrating the parameters of generalized Robin boundary condition into vessel wall properties. The pulse wave velocity incorporating the influence of tissue support is also analyzed. Furthermore, we modify the coupled momentum method, originally formulated for elastic problems, to account for the viscoelastic properties of the vessel wall.

RESULTS

The method is verified with three-dimensional fluid-structure interaction simulations, achieving a maximum relative error of less than 2.2% for flow rate and less than 0.7% for pressure. This method shows that tissue support parameters can be integrated into vessel wall properties, resulting in increased apparent wall stiffness and viscosity, and further changing pressure, flow rate, and wave propagation.

CONCLUSION

In this study, we develop an effective property method for quantitatively assessing the impact of tissue support and for efficiently modeling non-uniform tissue support. Moreover, this method offers further insights into clinically measured pulse wave velocity, demonstrating that it reflects the combined influence of both vessel wall properties and tissue support.

Effects of Inlet Geometry on Perfusion and Ischemia in Anomalous Aortic Origin of the Right Coronary Artery (AAORCA)

Anomalous aortic origin of a coronary artery (AAOCA) comprises a set of rare congenital abnormalities in the origin or path of the coronary arteries with highly variable clinical implications. This is a pilot feasibility study where we investigated the influence of the anomalous coronary artery inlet architecture on coronary perfusion using coronary blood flow computational simulations to help predict the risk for coronary ischemia in patients with anomalous aortic origin of the right coronary artery (AAORCA) with these types of anomalous coronary artery inlet architectures. We developed a protocol for generating 3D models of patient coronary artery anatomies from an IRB-approved dataset of cardiac CT images of patients with AAORCA at our institution. Coronary blood flow simulations and analysis were performed. Instantaneous flow reserve (iFR), a parameter used clinically in coronary CT analysis to determine risk for ischemia, was compared between models as a measure of ischemia. Comparing the median iFR of the coronary outlets between the four inlet variants and baseline architecture showed important differences. We observed a possible association between the proportion of the semi-minor axis and the semi-major axis of the elliptical AAORCA inlet and iFR. These observations suggest that the elliptical axis quotient may be a significant risk factor for evaluation of AAORCA severity.

Comparative analysis of Zero Pressure Geometry and prestress methods in cardiovascular Fluid-Structure Interaction

BACKGROUND AND OBJECTIVE

Modelling patient-specific aortic biomechanics with advanced computational techniques, such as Fluid-Structure Interaction (FSI), can be crucial to provide effective decision-making indices to enhance current clinical practices. To effectively simulate Ascending Thoracic Aortic Aneurysms (ATAA), the stress-free configuration must be defined. The Zero Pressure Geometry (ZPG) and the Prestress Tensor (PT) are two of the main approaches to tackle this issue. However, their impact on the numerical results is yet to be analysed. Computed Tomography Angiography (CTA) and Magnetic Resonance Imaging (MRI) data were used to develop patient-specific 2-way FSI frameworks.

METHODS

Three models were developed considering different tissue prestressing approaches to account for the reference configuration and their numerical results were compared. The selected approaches were: (i) ZPG, (ii) PT and (iii) a combination of the PT approach with a regional mapping of material properties (PTCAL).

RESULTS

The pressure fields estimated by all models were equivalent. The estimation of Wall Shear Stress (WSS) based metrics revealed good correspondence between all models except the Relative Residence Time (RRT). Regarding ATAA wall mechanics, the proposed extension to the PT approach presented a closer agreement with the ZPG model than its counterpart. Additionally, the PT and PTCAL approaches required around 60% fewer iterations to achieve cycle-to-cycle convergence than the ZPG algorithm.

CONCLUSION

Using a regional mapping of material properties in combination with the PT method presented a better correspondence with the ZPG approach. The outcomes of this study can pave the way for advancing the accuracy and convergence of ATAA numerical models using the PT methodology.

Deforming Patient-Specific Models of Vascular Anatomies to Represent Stent Implantation via Extended Position Based Dynamics

PURPOSE

Angioplasty with stent placement is a widely used treatment strategy for patients with stenotic blood vessels. However, it is often challenging to predict the outcomes of this procedure for individual patients. Image-based computational fluid dynamics (CFD) is a powerful technique for making these predictions. To perform CFD analysis of a stented vessel, a virtual model of the vessel must first be created. This model is typically made by manipulating two-dimensional contours of the vessel in its pre-stent state to reflect its post-stent shape. However, improper contour-editing can cause invalid geometric artifacts in the resulting mesh that then distort the subsequent CFD predictions. To address this limitation, we have developed a novel shape-editing method that deforms surface meshes of stenosed vessels to create stented models.

METHODS

Our method uses physics-based simulations via Extended Position Based Dynamics to guide these deformations. We embed an inflating stent inside a vessel and apply collision-generated forces to deform the vessel and expand its cross-section.

RESULTS

We demonstrate that this technique is feasible and applicable for a wide range of vascular anatomies, while yielding clinically compatible results. We also illustrate the ability to parametrically vary the stented shape and create models allowing CFD analyses.

CONCLUSION

Our stenting method will help clinicians predict the hemodynamic results of stenting interventions and adapt treatments to achieve target outcomes for patients. It will also enable generation of synthetic data for data-intensive applications, such as machine learning, to support cardiovascular research endeavors.

The Association Between the Hemodynamics in Anomalous Origins of Coronary Arteries and Atherosclerosis: A Preliminary Case Study Based on Computational Fluid Dynamics

Patients with anomalous coronary artery origins (AOCA) exhibit a higher risk of atherosclerosis, where even minimal stenosis may lead to adverse cardiovascular events. However, the factors contributing to this heightened risk in AOCA patients remain unclear. This study aimed to investigate whether an AOCA patient is more prone to stenosis occurrence and its progression in view of hemodynamics. A patient whose left circumflex artery originated from the right coronary sinus with a mild stenosis in the left anterior descending (LAD) artery and a healthy individual were included in this study. Two additional models were developed by removing stenosis from the patient model and adding a corresponding stenosis to the healthy model. Additionally, the inlet flow waveforms for the left and right coronary arteries were swapped in both the patient and healthy models. Results indicated that the AOCA patient without stenosis demonstrated higher wall pressure (LAD: 95.57 ± 0.73 vs. 93.86 ± 0.50 mmHg; LCX: 94.97 ± 0.98 vs. 93.47 ± 0.56 mmHg; RCA: 96.23 ± 0.30 vs. 93.86 ± 0.46 mmHg) and TAWSS (LAD: 24.41 ± 19.53 vs. 13.82 ± 9.87 dyne/cm2, p < 0.0001; LCX: 27.21 ± 14.51 vs. 19.33 ± 8.78 dyne/cm2) compared to the healthy individual, with similar trends also observed in stenotic conditions. Significant changes in the LCX flow distribution were also noted under varying pulsatile conditions (LCX: 18.28% vs. 9.16%) compared to the healthy individual. The high-pressure, high-shear hemodynamic environment in AOCA patients predisposes them to atherosclerosis, and the unique geometry exacerbates hemodynamic abnormalities when stenosis occurs. Clinicians should closely monitor AOCA patients with stenosis to prevent adverse cardiovascular events.

Investigating the role of thrombosis and false lumen orbital orientation in the hemodynamics of Type B aortic dissection

While much about the fundamental mechanisms behind the initiation and progression of Type B aortic dissection (TBAD) is still unknown, predictive models based on patient-specific fluid-structure interaction (FSI) simulations can help in risk stratification and optimal clinical decision-making. Aiming at the development of personalized treatment, FSI models can be leveraged to investigate the interplay between complex aortic flow patterns and anatomical features, while considering the deformation of the arterial wall and the dissection flap. In this study, the hemodynamics of false lumen thrombosis, a large fenestration, and the orbital orientation of the false lumen is studied through image-based FSI simulations on three TBAD patient-specific geometries. A new pipeline is developed leveraging the open-source software SimVascular and ParaView to analyze multiple patients simultaneously and to achieve large-scale parallelization in FSI results based on patients' computed tomography (CT) images. The results of this study suggest that the internal orbital orientation of the false lumen contributes to maintaining a positive luminal pressure difference Δ P T L - F L =P TL - P FL between the true lumen (TL) and the false lumen (FL), despite an impingement area in the false lumen near the entry tear. A positive and high luminal pressure difference is thought to promote TL expansion and FL compression. Moreover, it was also found that FL thrombosis at the entry tear region reduce the magnitude of the negative luminal pressure difference, which in turn may reduce FL expansion and the risk of unstable aortic growth. Finally, this FSI study suggests that the aortic wall and dissection flap stiffness determines the effects of a large fenestration in the descending thoracic aorta on the luminal pressure difference.

FSGe: A fast and strongly-coupled 3D fluid-solid-growth interaction method

Equilibrated fluid-solid-growth (FSGe) is a fast, open source, three-dimensional (3D) computational platform for simulating interactions between instantaneous hemodynamics and long-term vessel wall adaptation through mechanobiologically equilibrated growth and remodeling (G&R). Such models can capture evolving geometry, composition, and material properties in health and disease and following clinical interventions. In traditional G&R models, this feedback is modeled through highly simplified fluid solutions, neglecting local variations in blood pressure and wall shear stress (WSS). FSGe overcomes these inherent limitations by strongly coupling the 3D Navier-Stokes equations for blood flow with a 3D equilibrated constrained mixture model (CMMe) for vascular tissue G&R. CMMe allows one to predict long-term evolved mechanobiological equilibria from an original homeostatic state at a computational cost equivalent to that of a standard hyperelastic material model. In illustrative computational examples, we focus on the development of a stable aortic aneurysm in a mouse model to highlight key differences in growth patterns between FSGe and solid-only G&R models. We show that FSGe is especially important in blood vessels with asymmetric stimuli. Simulation results reveal greater local variation in fluid-derived WSS than in intramural stress (IMS). Thus, differences between FSGe and G&R models became more pronounced with the growing influence of WSS relative to pressure. Future applications in highly localized disease processes, such as for lesion formation in atherosclerosis, can now include spatial and temporal variations of WSS.

A generalized reduced-order model for trans-stenotic pressure drop with and without a guidewire

Guidewire-based pressure measurement is essential for diagnosing coronary artery disease. However, the impact of the guidewire on local hemodynamics and diagnostic outcomes is not fully understood. In this study, we propose a generalized reduced-order model (ROM) to accurately predict the trans-stenotic pressure drop in arteries. A key advantage of this model is that the viscous term does not rely on empirical parameters, making it applicable to both scenarios with and without guidewire insertion, and across varying stenosis severities. The proposed model demonstrates good accuracy compared to 3D idealized numerical models, achieving an average prediction error of 3.61% for cases without a guidewire and 4.53% for cases with a guidewire. Furthermore, when applied to a patient-specific model, it achieves comparable or better results than previously published ROMs. Finally, this ROM is employed to investigate the shifting relative importance of different components of the trans-stenotic pressure drop at various stenosis severities, and to provide further insights into the guidewire's influence on FFR measurements.

Quantification and Visualization of CT Myocardial Perfusion Imaging to Detect Ischemia-Causing Coronary Arteries

Coronary computed tomography angiography (cCTA) has poor specificity to identify coronary stenosis that limit blood flow to the myocardial tissue. Integration of dynamic CT myocardial perfusion imaging (CT-MPI) can potentially improve the diagnostic accuracy. We propose a method that integrates cCTA and CT-MPI to identify culprit coronary lesions that limit blood flow to the myocardium. Coronary arteries and left ventricle surfaces were segmented from cCTA and registered to CT-MPI. Myocardial blood flow (MBF) was derived from CT-MPI. A ray-casting approach was developed to project volumetric MBF onto the left ventricle surface. MBF volume were divided into coronary-specific territories based on proximity to the nearest coronary artery. MBF and normalized MBF were computed for the entire myocardium and each of the coronary artery. Projection of MBF onto cCTA allowed for direct visualization of perfusion defects. Normalized MBF had higher correlation with ischemic myocardial territory compared to MBF (MBF: R2=0.81 and Index MBF: R2=0.90). There were 18 vessels that showed angiographic disease (stenosis >50%); however, normalized MBF demonstrated only 5 coronary territories to be ischemic. These findings demonstrate that cCTA and CT-MPI can be integrated to visualize myocardial defects and detect culprit coronary arteries responsible for perfusion defects. These methods can allow for non-invasive detection of ischemia-causing coronary lesions and ultimately help guide clinicians to deliver more targeted coronary interventions.

Longitudinal investigation of aortic dissection in mice with computational fluid dynamics

Predicting late adverse events in aortic dissections is challenging. One commonly observed risk factor is partial thrombosis of the false lumen. In this study we investigated false lumen thrombus progression over 7 days in four mice with angiotensin II-induced aortic dissection. We performed computational fluid dynamic simulations with subject-specific boundary conditions from velocity and pressure measurements. We investigated endothelial cell activation potential, mean velocity, thrombus formation potential, and other hemodynamic factors. Our findings support the hypothesis that flow stagnation is the predominant hemodynamic factor driving a large thrombus ratio in false lumina, particularly those with a single fenestration.

Geometrical Factors Affect Wall Shear Stress in Saccular Aneurysms of the Infrarenal Abdominal Aorta

BACKGROUND

Low wall shear stress (WSS) is predictive of aortic aneurysm growth and rupture. Yet, estimating WSS in a clinical setting is impractical, whereas measuring aneurysm geometry is feasible. This study investigates the association between saccular aneurysm geometry of the infrarenal aorta and WSS.

METHODS

Starting with a nonaneurysmal, patient-specific, computational fluid dynamics model of the aorta, saccular aneurysms of varying geometry were created by incrementally increasing the neck width and sac depth from 1 cm to 4 cm. The aspect ratio (the ratio between sac depth and neck width) varied between 0.25 and 4. The peak WSS, time-averaged WSS (TAWSS), and oscillatory shear index (OSI) were measured within the aneurysm sac.

RESULTS

Decreasing the neck width from 4 cm to 1 cm decreased the peak WSS by 69% and the TAWSS by 83%. Increasing the sac depth from 1 cm to 4 cm decreased the peak WSS by 55% and the OSI by 37%. The aspect ratio was negatively correlated to peak WSS (Rs -0.85; P < 0.001).

CONCLUSIONS

In saccular aneurysms of the infrarenal aorta, a smaller neck width, deeper aneurysm sac, and larger aspect ratio are associated with lower peak WSS.

Optimizing distal and proximal splenic artery embolization with patient-specific computational fluid dynamics

Splenic artery embolization (SAE) has become a favored alternative to splenectomy, offering a less invasive intervention for injured spleens while preserving spleen function. However, our understanding of the role that hemodynamics plays during embolization remains limited. In this study, we utilized patient-specific computational fluid dynamics (CFD) simulations to study distal and proximal embolization strategies commonly used in SAE. Detailed 3D computer models were constructed considering the descending aorta, various major visceral arteries, and the iliac arteries. Subsequently, the blood flow and pressure associated with different coil placement locations in proximal embolization were studied considering the collateral vessels. Coil induced variations in pressure fields were quantified and compared to baseline. The coil induced flow stagnation was also quantified with particle residence time. Distal embolization was modeled with Lagrangian particle tracking and the effect of particle size, release location, and timing on embolization outcome was studied. Our findings highlight the crucial role of collateral vessels in maintaining blood supply to the spleen following proximal embolization. It was demonstrated that coil location can affect distal pressure and that strategic coil placement guided by patient-specific CFD simulations can further reduce this pressure as desired. Additionally, the results point to the critical roles that particle size, release timing, and location play in distal embolization. Our study provides an early attempt to use patient-specific computer modeling for optimizing embolization strategies and ultimately improving patient outcomes during SAE procedures.

A comparison of machine learning methods for recovering noisy and missing 4D flow MRI data

Experimental blood flow measurement techniques are invaluable for a better understanding of cardiovascular disease formation, progression, and treatment. One of the emerging methods is time-resolved three-dimensional phase-contrast magnetic resonance imaging (4D flow MRI), which enables noninvasive time-dependent velocity measurements within large vessels. However, several limitations hinder the usability of 4D flow MRI and other experimental methods for quantitative hemodynamics analysis. These mainly include measurement noise, corrupt or missing data, low spatiotemporal resolution, and other artifacts. Traditional filtering is routinely applied for denoising experimental blood flow data without any detailed discussion on why it is preferred over other methods. In this study, filtering is compared to different singular value decomposition (SVD)-based machine learning and autoencoder-type deep learning methods for denoising and filling in missing data (imputation). An artificially corrupted and voxelized computational fluid dynamics (CFD) simulation as well as in vitro 4D flow MRI data are used to test the methods. SVD-based algorithms achieve excellent results for the idealized case but severely struggle when applied to in vitro data. The autoencoders are shown to be versatile and applicable to all investigated cases. For denoising, the in vitro 4D flow MRI data, the denoising autoencoder (DAE), and the Noise2Noise (N2N) autoencoder produced better reconstructions than filtering both qualitatively and quantitatively. Deep learning methods such as N2N can result in noise-free velocity fields even though they did not use clean data during training. This work presents one of the first comprehensive assessments and comparisons of various classical and modern machine-learning methods for enhancing corrupt cardiovascular flow data in diseased arteries for both synthetic and experimental test cases.

Effects of pedicle subtraction osteotomy on aortic morphology and hemodynamics in ankylosing spondylitis with kyphosis: a finite element analysis study

Osteotomy can correct kyphosis, restore the spinal sequence, and restore the healthy appearance of a patient. However, the aorta is stretched during pedicle subtraction osteotomy (PSO), and some surgeons are concerned about aortic injury. We used finite element analysis to construct an aortic model to simulate hemodynamic changes during osteotomy. 16 patients with ankylosing spondylitis kyphosis who had undergone a two-level osteotomy at the L1 and L3 levels was included in this study. Aortic computed tomography angiography (CTA) was performed, and a 3D image model was constructed. The length, transverse diameter, and curvature of the aorta were used to evaluate morphological changes. Finite element analysis was used to analyze the changes in aortic fluid dynamics. Blood pressure, wall shear stress, and blood flow velocity were compared pre- and postoperatively. The overall length of the aorta before surgery was 424.3 ± 42.9 mm, and the overall length of the aorta after surgery was 436.2 ± 54.8 mm. The aortic curvature decreased from 0.27 ± 0.13 to 0.17 ± 0.09. The mean transverse diameter of the aorta did not change (19.3 ± 6.6 vs. 19.2 ± 7.4 mm, P > 0.05). The blood flow velocity (2.8 ± 1.1 vs. 1.5 ± 0.8 m/s, P < 0.05), blood pressure (6.6 ± 1.7 vs. 4.3 ± 1.2 Kpa, P < 0.05), and wall shear stress (47.6 ± 17.3 vs. 22.3 ± 8.6, P < 0.05) at the T10-L4 level decreased postoperatively. Changes in LL were significantly correlated with changes in ld, dc, blood flow velocity, blood pressure and wall shear stress (ld : r = 0.713, P < 0.001; dc: r = 0.626,P = 0.010; blood flow velocity: r= - 0.541, P = 0.041; blood pressure: r = - 0.601, P = 0.016; wall shear stress: r= - 0.594, P = 0.027). The aorta was stretched, and its curvature decreased. The mean transverse diameter of the aorta did not change. Blood flow velocity, blood pressure, and wall shear stress decreased after surgery. Our study provides hemodynamic support for the possible cardiovascular benefits of osteotomes.

Understanding Embolus Transport And Source To Destination Mapping Of Thromboemboli In Hemodynamics Driven By Left Ventricular Assist Device

Left Ventricular Assist Devices (LVADs) are a key treatment option for patients with advanced heart failure, but they carry a significant risk of thromboembolic complications. While improved LVAD design, and systemic anticoagulation regimen, have helped mitigate thromboembolic risks, ischemic stroke due to adverse thromboembolic events remains a major concern with current LVAD therapies. Improved understanding of embolic events, and embolus movement to the brain, is critical to develop techniques to minimize risks of occlusive embolic events such as a stroke after LVAD implantation. Here, we address this need, and devise a quantitative in silico framework to characterize thromboembolus transport and distrbution in hemodynamics driven by an operating LVAD. We conduct systematic numerical experiments to quantify the source-to-destination transport patterns of thromboemboli as a function of: LVAD outflow graft anastomosis, LVAD operating pulse modulation, thromboembolus sizes, and origin locations of emboli. Additionally, we demonstrate how the resulting embolus distribution patterns compare and correlate with descriptors based solely on hemodynamic patterns such as helicity, vorticity, and wall shear stress. Using the concepts of size-dependent embolus-hemodynamics interactions, and two jet flow model for hemodynamics under LVAD operation as established in our prior works, we gain valuable insights on departure of thromboembolus distribution from flow distribution, and establish that our in silico model can generate deep insights into embolus dynamics which is not otherwise available from standard of care imaging and clinical data.

Foundational Engineering of Artificial Blood Vessels' Biomechanics: The Impact of Wavy Geometric Designs

The design of wavy structures and their mechanical implications on artificial blood vessels (ABVs) have been insufficiently studied in the existing literature. This research aims to explore the influence of various wavy geometric designs on the mechanical properties of ABVs and to establish a foundational framework for advancing and applying these designs. Computer-aided design (CAD) and finite element method (FEM) simulations, in conjunction with physical sample testing, were utilized. A geometric model incorporating concave and convex curves was developed and analyzed with a symbolic mathematical tool. Subsequently, a total of ten CAD models were subjected to increasing internal pressures using a FEM simulation to evaluate the expansion of internal areas. Additionally, physical experiments were conducted further to investigate the expansion of ABV samples under pressure. The results demonstrated that increased wave numbers significantly enhance the flexibility of ABVs. Samples with 22 waves exhibited a 45% larger area under 24 kPa pressure than those with simple circles. However, the increased number of waves also led to undesirable high-pressure gradients at elevated pressures. Furthermore, a strong correlation was observed between the experimental outcomes and the simulation results, with a notably low error margin, ranging from 19.88% to 3.84%. Incorporating wavy designs into ABVs can effectively increase both vessel flexibility and the internal area under pressure. Finally, it was found that expansion depending on the wave number can be efficiently modeled with a simple linear equation, which could be utilized in future designs.

Role of the vessel morphology on the lenticulostriate arteries hemodynamics during atrial fibrillation: A CFD-based multivariate regression analysis

BACKGROUND AND OBJECTIVE

Atrial fibrillation (AF) is the most common cardiac arrhythmia, inducing accelerated and irregular beating. Beside well-known disabling symptoms - such as palpitations, reduced exercise tolerance, and chest discomfort - there is growing evidence that an alteration of deep cerebral hemodynamics due to AF increases the risk of vascular dementia and cognitive impairment, even in the absence of clinical strokes. The alteration of deep cerebral circulation in AF represents one of the least investigated among the possible mechanisms. Lenticulostriate arteries (LSAs) are small perforating arteries mainly departing from the middle cerebral artery (MCA) and susceptible to small vessel disease, which is one of the mechanisms of subcortical vascular dementia development. The purpose of this study is to investigate the impact of different LSAs morphologies on the cerebral hemodynamics during AF.

METHODS

By combining a computational fluid dynamics (CFD) analysis of LSAs with 7T high-resolution magnetic resonance imaging (MRI), we performed different CFD-based multivariate regression analyses to detect which geometrical and morphological vessel features mostly affect AF hemodynamics in terms of wall shear stress. We exploited 17 cerebral 7T-MRI derived LSA vascular geometries extracted from 10 subjects and internal carotid artery data from validated 0D cardiovascular-cerebral modeling as inflow conditions.

RESULTS

Our results revealed that few geometrical variables - namely the size of the MCA and the bifurcation angles between MCA and LSA - are able to satisfactorily predict the AF impact. In particular, the present study indicates that LSA morphologies exhibiting markedly obtuse LSA-MCA inlet angles and small MCA size downstream of the LSA-MCA bifurcation may be more prone to vascular damage induced by AF.

CONCLUSIONS

The present MRI-based computational study has been able for the first time to: (i) investigate the net impact of LSAs vascular morphologies on cerebral hemodynamics during AF events; (ii) detect which combination of morphological features worsens the hemodynamic response in the presence of AF. Awaiting necessary clinical confirmation, our analysis suggests that the local hemodynamics of LSAs is affected by their geometrical features and some LSA morphologies undergo greater hemodynamic alterations in the presence of AF.

Simulation-based design of bicuspidization of the aortic valve

OBJECTIVE

Severe congenital aortic valve pathology in the growing patient remains a challenging clinical scenario. Bicuspidization of the diseased aortic valve has proven to be a promising repair technique with acceptable durability. However, most understanding of the procedure is empirical and retrospective. This work seeks to design the optimal gross morphology associated with surgical bicuspidization with simulations based on the hypothesis that modifications to the free edge length cause or relieve stenosis.

METHODS

Model bicuspid valves were constructed with varying free edge lengths and gross morphology. Fluid-structure interaction simulations were conducted in a single patient-specific model geometry. The models were evaluated for primary targets of stenosis and regurgitation. Secondary targets were assessed and included qualitative hemodynamics, geometric height, effective height, orifice area, and billow.

RESULTS

Stenosis decreased with increasing free edge length and was pronounced with free edge length less than or equal to 1.3 times the annular diameter d. With free edge length 1.5d or greater, no stenosis occurred. All models were free of regurgitation. Substantial billow occurred with free edge length 1.7d or greater.

CONCLUSIONS

Free edge length 1.5d or greater was required to avoid aortic stenosis in simulations. Cases with free edge length 1.7d or greater showed excessive billow and other changes in gross morphology. Cases with free edge length 1.5d to 1.6d have a total free edge length approximately equal to the annular circumference and appeared optimal. These effects should be studied in vitro and in animal studies.

Hemodynamic influence of mild stenosis morphology in different coronary arteries: a computational fluid dynamic modelling study

Introduction: Mild stenosis [degree of stenosis (DS) < 50%] is commonly labeled as nonobstructive lesion. Some lesions remain stable for several years, while others precipitate acute coronary syndromes (ACS) rapidly. The causes of ACS and the factors leading to diverse clinical outcomes remain unclear. Method: This study aimed to investigate the hemodynamic influence of mild stenosis morphologies in different coronary arteries. The stenoses were modeled with different morphologies based on a healthy individual data. Computational fluid dynamics analysis was used to obtain hemodynamic characteristics, including flow waveforms, fractional flow reserve (FFR), flow streamlines, time-average wall shear stress (TAWSS), and oscillatory shear index (OSI). Results: Numerical simulation indicated significant hemodynamic differences among different DS and locations. In the 20%-30% range, significant large, low-velocity vortexes resulted in low TAWSS (<4 dyne/cm2) around stenoses. In the 30%-50% range, high flow velocity due to lumen area reduction resulted in high TAWSS (>40 dyne/cm2), rapidly expanding the high TAWSS area (averagely increased by 0.46 cm2) in left main artery and left anterior descending artery (LAD), where high OSI areas remained extensive (>0.19 cm2). Discussion: While mild stenosis does not pose any immediate ischemic risk due to a FFR > 0.95, 20%-50% stenosis requires attention and further subdivision based on location is essential. Rapid progression is a danger for lesions with 20%-30% DS near the stenoses and in the proximal LAD, while lesions with 30%-50% DS can cause plaque injury and rupture. These findings support clinical practice in early assessment, monitoring, and preventive treatment.