Patient-specific modeling of cardiovascular mechanics

Mechanical, structural, and morphological differences in the iliac arteries

Iliac arteries play a crucial role in peripheral blood circulation. They are susceptible to various diseases, including aneurysms and atherosclerosis. Structure, material properties, and biomechanical forces acting on different regions of the iliac vasculature may contribute to the localization and progression of these pathologies. We examined 33 arterial specimens from common iliac (CI), external iliac (EI), and internal iliac (II) arteries obtained from 11 human donors (62 ± 12 years). We conducted morphometric, mechanical, and structural analyses using planar biaxial tests, constitutive modeling, and bi-directional histology on transverse and axial sections. The iliac arteries exhibited increased tortuosity and varying disease distribution with age. CI and II arteries displayed non-uniform age-related disease progression around their circumference, while EI remained healthy even in older individuals. Trends in load-free and stress-free thickness varied along the iliac vasculature. Longitudinally, EI exhibited the highest compliance compared to other iliac vessels. In contrast, CI was stiffest longitudinally, and EI was the stiffest circumferentially. Material parameters for all iliac vessels are reported for four common constitutive relations. Elastin near the internal elastic lamina displayed greater waviness in EI and II compared to CI. Also, EI had the least glycosaminoglycans (GAGs) and the highest elastin content. Our findings highlight variations in the morphological, mechanical, and structural properties of iliac arteries along their length. This data can inform vascular disease development and computational studies, and guide the development of biomimetic repair materials and devices tailored to specific iliac locations, improving vascular repair strategies.

Influence of material parameter variability on the predicted coronary artery biomechanical environment via uncertainty quantification

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.

Virtual Shape-Editing of Patient-Specific Vascular Models Using Regularized Kelvinlets

OBJECTIVE

Cardiovascular diseases, and the interventions performed to treat them, can lead to changes in the shape of patient vasculatures and their hemodynamics. Computational modeling and simulations of patient-specific vascular networks are increasingly used to quantify these hemodynamic changes, but they require modifying the shapes of the models. Existing methods to modify these shapes include editing 2D lumen contours prescribed along vessel centerlines and deforming meshes with geometry-based approaches. However, these methods can require extensive by-hand prescription of the desired shapes and often do not work robustly across a range of vascular anatomies. To overcome these limitations, we develop techniques to modify vascular models using physics-based principles that can automatically generate smooth deformations and readily apply them across different vascular anatomies.

METHODS

We adapt Regularized Kelvinlets, analytical solutions to linear elastostatics, to perform elastic shape-editing of vascular models. The Kelvinlets are packaged into three methods that allow us to artificially create aneurysms, stenoses, and tortuosity.

RESULTS

Our methods are able to generate such geometric changes across a wide range of vascular anatomies. We demonstrate their capabilities by creating sets of aneurysms, stenoses, and tortuosities with varying shapes and sizes on multiple patient-specific models.

CONCLUSION

Our Kelvinlet-based deformers allow us to edit the shape of vascular models, regardless of their anatomical locations, and parametrically vary the size of the geometric changes.

SIGNIFICANCE

These methods will enable researchers to more easily perform virtual-surgery-like deformations, computationally explore the impact of vascular shape on patient hemodynamics, and generate synthetic geometries for data-driven research.

Integration of Febio as an Instructional Tool in the Undergraduate Biomechanics Curriculum

Computer simulations play an important role in a range of biomedical engineering applications. Thus, it is important that biomedical engineering students engage with modeling in their undergraduate education and establish an understanding of its practice. In addition, computational tools enhance active learning and complement standard pedagogical approaches to promote student understanding of course content. Herein, we describe the development and implementation of learning modules for computational modeling and simulation (CM&S) within an undergraduate biomechanics course. We developed four CM&S learning modules that targeted predefined course goals and learning outcomes within the febio studio software. For each module, students were guided through CM&S tutorials and tasked to construct and analyze more advanced models to assess learning and competency and evaluate module effectiveness. Results showed that students demonstrated an increased interest in CM&S through module progression and that modules promoted the understanding of course content. In addition, students exhibited increased understanding and competency in finite element model development and simulation software use. Lastly, it was evident that students recognized the importance of coupling theory, experiments, and modeling and understood the importance of CM&S in biomedical engineering and its broad application. Our findings suggest that integrating well-designed CM&S modules into undergraduate biomedical engineering education holds much promise in supporting student learning experiences and introducing students to modern engineering tools relevant to professional development.

Computational approaches for mechanobiology in cardiovascular development and diseases

The cardiovascular development in vertebrates evolves in response to genetic and mechanical cues. The dynamic interplay among mechanics, cell biology, and anatomy continually shapes the hydraulic networks, characterized by complex, non-linear changes in anatomical structure and blood flow dynamics. To better understand this interplay, a diverse set of molecular and computational tools has been used to comprehensively study cardiovascular mechanobiology. With the continual advancement of computational capacity and numerical techniques, cardiovascular simulation is increasingly vital in both basic science research for understanding developmental mechanisms and disease etiologies, as well as in clinical studies aimed at enhancing treatment outcomes. This review provides an overview of computational cardiovascular modeling. Beginning with the fundamental concepts of computational cardiovascular modeling, it navigates through the applications of computational modeling in investigating mechanobiology during cardiac development. Second, the article illustrates the utility of computational hemodynamic modeling in the context of treatment planning for congenital heart diseases. It then delves into the predictive potential of computational models for elucidating tissue growth and remodeling processes. In closing, we outline prevailing challenges and future prospects, underscoring the transformative impact of computational cardiovascular modeling in reshaping cardiovascular science and clinical practice.

Influence of Material Parameter Variability on the Predicted Coronary Artery Biomechanical Environment via Uncertainty Quantification

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.

Effect of rheological models on pulsatile hemodynamics in a multiply afflicted descending human aortic network

In the cardiovascular diseased (CVD) conditions, it is essential to choose a suitable rheological model for capturing the correct physics behind the hemodynamic in the multiply afflicted diseased arterial network. This study investigates the effect of blood rheology on hemodynamics in a blood vessel with abdominal aortic aneurysm (AAA) and right internal iliac stenosis (RIIAS). A model with AAA and RIIAS is reconstructed from a human subject's computed tomography (CT) data. Localized mesh generation and pulsatile inflow condition are considered. Non-Newtonian models such as the Power-law, Carreau, Cross, and Herschel Berkley models are used in simulations. The outcome from a validated computational model is compared with the Newtonian model to identify the suitable model for dealing with pathological complications under consideration. The capabilities and significance of various rheological models are also examined via Wall Pressure (WP), Wall Shear Stress (WSS), velocity, Global non-Newtonian importance factor (IG), Vorticity Streamlines, and Swirling Strength. It is noted that during the entire cardiac cycle, the IG factor of the cross model is found to be relatively more significant. Power Law depicts larger IG factor during peak systole and early diastole. Also, the cross model depicts larger WSS, WPS, swirling strength distribution and vorticity during the peak systolic and diastolic phases It is noted that IG ∼0.02 is an appropriate non-Newtonian blood activity cut-off value in the descending abdominal artery having AAA and RIIAS. The critical important WSS values are in the range of 0-9 Pa which is stated in WSS contour plot.

Decoding thrombosis through code: a review of computational models

From the molecular level up to a blood vessel, thrombosis and hemostasis involves many interconnected biochemical and biophysical processes over a wide range of length and time scales. Computational modeling has gained eminence in offering insights into these processes beyond what can be obtained from in vitro or in vivo experiments, or clinical measurements. The multiscale and multiphysics nature of thrombosis has inspired a wide range of modeling approaches that aim to address how a thrombus forms and dismantles. Here, we review recent advances in computational modeling with a focus on platelet-based thrombosis. We attempt to summarize the diverse range of modeling efforts straddling the wide-spectrum of physical phenomena, length scales, and time scales; highlighting key advancements and insights from existing studies. Potential information gleaned from models is discussed, ranging from identification of thrombus-prone regions in patient-specific vasculature to modeling thrombus deformation and embolization in response to fluid forces. Furthermore, we highlight several limitations of current models, future directions in the field, and opportunities for clinical translation, to illustrate the state-of-the-art. There are a plethora of opportunity areas for which models can be expanded, ranging from topics of thromboinflammation to platelet production and clearance. Through successes demonstrated in existing studies described here, as well as continued advancements in computational methodologies and computer processing speeds and memory, in silico investigations in thrombosis are poised to bring about significant knowledge growth in the years to come.

Effects of Mechanical Stress on Endothelial Cells In Situ and In Vitro

Endothelial cells lining blood vessels are essential for maintaining vascular homeostasis and mediate several pathological and physiological processes. Mechanical stresses generated by blood flow and other biomechanical factors significantly affect endothelial cell activity. Here, we review how mechanical stresses, both in situ and in vitro, affect endothelial cells. We review the basic principles underlying the cellular response to mechanical stresses. We also consider the implications of these findings for understanding the mechanisms of mechanotransducer and mechano-signal transduction systems by cytoskeletal components.

A time-consistent stabilized finite element method for fluids with applications to hemodynamics

Several finite element methods for simulating incompressible flows rely on the streamline upwind Petrov-Galerkin stabilization (SUPG) term, which is weighted by [Formula: see text]. The conventional formulation of [Formula: see text] includes a constant that depends on the time step size, producing an overall method that becomes exceedingly less accurate as the time step size approaches zero. In practice, such method inconsistency introduces significant error in the solution, especially in cardiovascular simulations, where small time step sizes may be required to resolve multiple scales of the blood flow. To overcome this issue, we propose a consistent method that is based on a new definition of [Formula: see text]. This method, which can be easily implemented on top of an existing streamline upwind Petrov-Galerkin and pressure stabilizing Petrov-Galerkin method, involves the replacement of the time step size in [Formula: see text] with a physical time scale. This time scale is calculated in a simple operation once every time step for the entire computational domain from the ratio of the L2-norm of the acceleration and the velocity. The proposed method is compared against the conventional method using four cases: a steady pipe flow, a blood flow through vascular anatomy, an external flow over a square obstacle, and a fluid-structure interaction case involving an oscillatory flexible beam. These numerical experiments, which are performed using linear interpolation functions, show that the proposed formulation eliminates the inconsistency issue associated with the conventional formulation in all cases. While the proposed method is slightly more costly than the conventional method, it significantly reduces the error, particularly at small time step sizes. For the pipe flow where an exact solution is available, we show the conventional method can over-predict the pressure drop by a factor of three. This large error is almost completely eliminated by the proposed formulation, dropping to approximately 1% for all time step sizes and Reynolds numbers considered.

Artificial intelligence in cardiac computed tomography

Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern application of AI encompasses intelligent models and algorithms for automated data analysis and processing, data generation, and prediction with applications in visual perception, speech understanding, and language translation. AI in healthcare uses machine learning (ML) and other predictive analytical techniques to help sort through vast amounts of data and generate outputs that aid in diagnosis, clinical decision support, workflow automation, and prognostication. Coronary computed tomography angiography (CCTA) is an ideal union for these applications due to vast amounts of data generation and analysis during cardiac segmentation, coronary calcium scoring, plaque quantification, adipose tissue quantification, peri-operative planning, fractional flow reserve quantification, and cardiac event prediction. In the past 5 years, there has been an exponential increase in the number of studies exploring the use of AI for cardiac computed tomography (CT) image acquisition, de-noising, analysis, and prognosis. Beyond image processing, AI has also been applied to improve the imaging workflow in areas such as patient scheduling, urgent result notification, report generation, and report communication. In this review, we discuss algorithms applicable to AI and radiomic analysis; we then present a summary of current and emerging clinical applications of AI in cardiac CT. We conclude with AI's advantages and limitations in this new field.

Calibration of the Mechanical Boundary Conditions for a Patient-Specific Thoracic Aorta Model Including the Heart Motion Effect

OBJECTIVE

We propose a procedure for calibrating 4 parameters governing the mechanical boundary conditions (BCs) of a thoracic aorta (TA) model derived from one patient with ascending aortic aneurysm. The BCs reproduce the visco-elastic structural support provided by the soft tissue and the spine and allow for the inclusion of the heart motion effect.

METHODS

We first segment the TA from magnetic resonance imaging (MRI) angiography and derive the heart motion by tracking the aortic annulus from cine-MRI. A rigid-wall fluid-dynamic simulation is performed to derive the time-varying wall pressure field. We build the finite element model considering patient-specific material properties and imposing the derived pressure field and the motion at the annulus boundary. The calibration, which involves the zero-pressure state computation, is based on purely structural simulations. After obtaining the vessel boundaries from the cine-MRI sequences, an iterative procedure is performed to minimize the distance between them and the corresponding boundaries derived from the deformed structural model. A strongly-coupled fluid-structure interaction (FSI) analysis is finally performed with the tuned parameters and compared to the purely structural simulation.

RESULTS AND CONCLUSION

The calibration with structural simulations allows to reduce maximum and mean distances between image-derived and simulation-derived boundaries from 8.64 mm to 6.37 mm and from 2.24 mm to 1.83 mm, respectively. The maximum root mean square error between the deformed structural and FSI surface meshes is 0.19 mm. This procedure may prove crucial for increasing the model fidelity in replicating the real aortic root kinematics.

An ultrasound imaging and computational fluid dynamics protocol to assess hemodynamics in iliac vein compression syndrome

OBJECTIVE

Elevated shear rates are known to play a role in arterial thrombosis; however, shear rates have not been thoroughly investigated in patients with iliac vein compression syndrome (IVCS) owing to imaging limitations and assumptions on the low shear nature of venous flows. This study was undertaken to develop a standardized protocol that quantifies IVCS shear rates and can aid in the diagnosis and treatment of patients with moderate yet symptomatic compression.

METHODS

Study patients with and without IVCS had their iliac vein hemodynamics measured via duplex ultrasound (US) at two of the following three vessel locations: infrarenal inferior vena cava (IVC), right common iliac vein, and left common iliac vein, in addition to acquiring data at the right and left external iliac veins. US velocity spectra were multiplied by a weighted cross-sectional area calculated from US and computed tomography (CT) data to create flow waveforms. Flow waveforms were then scaled to enforce conservation of flow across the IVC and common iliac veins. A three-dimensional (3D), patient-specific model of the iliac vein anatomy was constructed from CT and US examination. Flow waveforms and the 3D model were used as a basis to run a computational fluid dynamics (CFD) simulation. Owing to collateral vessel flow and discrepancies between CT and US area measurements, flows in internal iliac veins and cross-sectional areas of the common iliac veins were calibrated iteratively against target common iliac flow. Simulation results on mean velocity were validated against US data at measurement locations. Simulation results were postprocessed to derive spatial and temporal values of quantities such as velocity and shear rate.

RESULTS

Using our modeling protocol, we were able to build CFD models of the iliac veins that matched common iliac flow splits within 2% and measured US velocities within 10%. Proof-of-concept analyses (1 subject, 1 control) have revealed that patients with IVCS may experience elevated shear rates in the compressed left common iliac vein, more typical of the arterial rather than the venous circulation. These results encourage us to extend this protocol to a larger group of patients with IVCS and controls.

CONCLUSIONS

We developed a protocol that obtains hemodynamic measurements of the IVC and iliac veins from US, creates patient-specific 3D reconstructions of the venous anatomy using CT and US examinations, and computes shear rates using calibrated CFD methods. Proof-of-concept results have indicated that patients with IVCS may experience elevated shear rates in the compressed left common iliac vein. Larger cohorts are needed to assess the relationship between venous compression and shear rates in patients with IVCS as compared with controls with noncompressed iliac veins. Further studies using this protocol may also give promising insights into whether or not to treat patients with moderate, yet symptomatic compression.

Evaluating the Arteriotomy Size of a New Sutureless Coronary Anastomosis Using a Finite Volume Approach

OBJECTIVES

The ELANA® Heart Bypass creates a standardized sutureless anastomosis. Hereby, we investigate the influence of arteriotomy and graft size on coronary hemodynamics.

METHODS

A computational fluid dynamics (CFD) model was developed. Arteriotomy size (standard 1.43 mm2; varied 0.94 - 3.6 mm2) and graft diameter (standard 2.5 mm; varied 1.5 - 5.0 mm) were independent parameters. Outcome parameters were coronary pressure and flow, and fractional flow reserve (FFR).

RESULTS

The current size ELANA (arteriotomy 1.43 mm2) presented an estimated FFR 0.65 (39 mL/min). Enlarging arteriotomy increased FFR, coronary pressure, and flow. All reached a maximum once the arteriotomy (2.80 mm2) surpassed the coronary cross-sectional area (2.69 mm2, i.e. 1.85 mm diameter), presenting an estimated FFR 0.75 (46 mL/min). Increasing graft diameter was positively related to FFR, coronary pressure, and flow.

CONCLUSION

The ratio between the required minimal coronary diameter for application and the ELANA arteriotomy size effectuates a pressure drop that could be clinically relevant. Additional research and eventual lengthening of the anastomosis is advised.

The Relation Between Viscous Energy Dissipation and Pulsation for Aortic Hemodynamics Driven by a Left Ventricular Assist Device

Left ventricular assist device (LVAD) provides mechanical circulatory support for patients with advanced heart failure. Treatment using LVAD is commonly associated with complications such as stroke and gastro-intestinal bleeding. These complications are intimately related to the state of hemodynamics in the aorta, driven by a jet flow from the LVAD outflow graft that impinges into the aorta wall. Here we conduct a systematic analyses of hemodynamics driven by an LVAD with a specific focus on viscous energy transport and dissipation. We conduct a complementary set of analysis using idealized cylindrical tubes with diameter equivalent to common carotid artery and aorta, and a patient-specific model of 27 different LVAD configurations. Results from our analysis demonstrate how energy dissipation is governed by key parameters such as frequency and pulsation, wall elasticity, and LVAD outflow graft surgical anastomosis. We find that frequency, pulsation, and surgical angles have a dominant effect, while wall elasticity has a weaker effect, in determining the state of energy dissipation. For the patient-specific scenario, we also find that energy dissipation is higher in the aortic arch and lower in the abdominal aorta, when compared to the baseline flow without an LVAD. This further illustrates the key hemodynamic role played by the LVAD outflow jet impingement, and subsequent aortic hemodynamics during LVAD operation.

Mapping the use of computational modelling and simulation in clinics: A survey

In silico medicine describes the application of computational modelling and simulation (CM&S) to the study, diagnosis, treatment or prevention of a disease. Tremendous research advances have been achieved to facilitate the use of CM&S in clinical applications. Nevertheless, the uptake of CM&S in clinical practice is not always timely and accurately reflected in the literature. A clear view on the current awareness, actual usage and opinions from the clinicians is needed to identify barriers and opportunities for the future of in silico medicine. The aim of this study was capturing the state of CM&S in clinics by means of a survey toward the clinical community. Responses were collected online using the Virtual Physiological Human institute communication channels, engagement with clinical societies, hospitals and individual contacts, between 2020 and 2021. Statistical analyses were done with R. Participants (n = 163) responded from all over the world. Clinicians were mostly aged between 35 and 64 years-old, with heterogeneous levels of experience and areas of expertise (i.e., 48% cardiology, 13% musculoskeletal, 8% general surgery, 5% paediatrics). The CM&S terms "Personalised medicine" and "Patient-specific modelling" were the most well-known within the respondents. "In silico clinical trials" and "Digital Twin" were the least known. The familiarity with different methods depended on the medical specialty. CM&S was used in clinics mostly to plan interventions. To date, the usage frequency is still scarce. A well-recognized benefit associated to CM&S is the increased trust in planning procedures. Overall, the recorded level of trust for CM&S is high and not proportional to awareness level. The main barriers appear to be access to computing resources, perception that CM&S is slow. Importantly, clinicians see a role for CM&S expertise in their team in the future. This survey offers a snapshot of the current situation of CM&S in clinics. Although the sample size and representativity could be increased, the results provide the community with actionable data to build a responsible strategy for accelerating a positive uptake of in silico medicine. New iterations and follow-up activities will track the evolution of responses over time and contribute to strengthen the engagement with the medical community.

A Computational Study of Dynamic Obstruction in Type B Aortic Dissection

A serious complication in aortic dissection is dynamic obstruction of the true lumen (TL). Dynamic obstruction results in malperfusion, a blockage of blood flow to a vital organ. Clinical data reveal that increases in central blood pressure promote dynamic obstruction. However, the mechanisms by which high pressures result in TL collapse are underexplored and poorly understood. Here, we developed a computational model to investigate biomechanical and hemodynamical factors involved in Dynamic obstruction. We hypothesize that relatively small pressure gradient between TL and false lumen (FL) are sufficient to displace the flap and induce obstruction. An idealized fluid-structure interaction model of type B aortic dissection was created. Simulations were performed under mean cardiac output while inducing dynamic changes in blood pressure by altering FL outflow resistance. As FL resistance increased, central aortic pressure increased from 95.7 to 115.3 mmHg. Concurrent with blood pressure increase, flap motion was observed, resulting in TL collapse, consistent with clinical findings. The maximum pressure gradient between TL and FL over the course of the dynamic obstruction was 4.5 mmHg, consistent with our hypothesis. Furthermore, the final stage of dynamic obstruction was very sudden in nature, occurring over a short time (<1 s) in our simulation, consistent with the clinical understanding of this dramatic event. Simulations also revealed sudden drops in flow and pressure in the TL in response to the flap motion, consistent with first stages of malperfusion. To our knowledge, this study represents the first computational analysis of potential mechanisms driving dynamic obstruction in aortic dissection.

svMorph: Interactive Geometry-Editing Tools for Virtual Patient-Specific Vascular Anatomies

We propose svMorph, a framework for interactive virtual sculpting of patient-specific vascular anatomic models. Our framework includes three tools for the creation of tortuosity, aneurysms, and stenoses in tubular vascular geometries. These shape edits are performed via geometric operations on the surface mesh and vessel centerline curves of the input model. The tortuosity tool also uses the physics-based Oriented Particles method, coupled with linear blend skinning, to achieve smooth, elastic-like deformations. Our tools can be applied separately or in combination to produce simulation-suitable morphed models. They are also compatible with popular vascular modeling software, such as simvascular. To illustrate our tools, we morph several image-based, patient-specific models to create a range of shape changes and simulate the resulting hemodynamics via three-dimensional, computational fluid dynamics. We also demonstrate the ability to quickly estimate the hemodynamic effects of the shape changes via the automated generation of associated zero-dimensional lumped-parameter models.

Modeling Flow in an In Vitro Anatomical Cerebrovascular Model with Experimental Validation

Acute ischemic stroke (AIS) is a leading cause of mortality that occurs when an embolus becomes lodged in the cerebral vasculature and obstructs blood flow in the brain. The severity of AIS is determined by the location and how extensively emboli become lodged, which are dictated in large part by the cerebral flow and the dynamics of embolus migration which are difficult to measure in vivo in AIS patients. Computational fluid dynamics (CFD) can be used to predict the patient-specific hemodynamics and embolus migration and lodging in the cerebral vasculature to better understand the underlying mechanics of AIS. To be relied upon, however, the computational simulations must be verified and validated. In this study, a realistic in vitro experimental model and a corresponding computational model of the cerebral vasculature are established that can be used to investigate flow and embolus migration and lodging in the brain. First, the in vitro anatomical model is described, including how the flow distribution in the model is tuned to match physiological measurements from the literature. Measurements of pressure and flow rate for both normal and stroke conditions were acquired and corresponding CFD simulations were performed and compared with the experiments to validate the flow predictions. Overall, the CFD simulations were in relatively close agreement with the experiments, to within ±7% of the mean experimental data with many of the CFD predictions within the uncertainty of the experimental measurement. This work provides an in vitro benchmark data set for flow in a realistic cerebrovascular model and is a first step towards validating a computational model of AIS.

Direct 0D-3D coupling of a lattice Boltzmann methodology for fluid-structure aortic flow simulations

This work introduces a numerical approach and implementation for the direct coupling of arbitrary complex ordinary differential equation- (ODE-)governed zero-dimensional (0D) boundary conditions to three-dimensional (3D) lattice Boltzmann-based fluid-structure systems for hemodynamics studies. In particular, a most complex configuration is treated by considering a dynamic left ventricle- (LV-)elastance heart model which is governed by (and applied as) a nonlinear, non-stationary hybrid ODE-Dirichlet system. Other ODE-based boundary conditions, such as lumped parameter Windkessel models for truncated vasculature, are also considered. Performance studies of the complete 0D-3D solver, including its treatment of the lattice Boltzmann fluid equations and elastodynamics equations as well as their interactions, is conducted through a variety of benchmark and convergence studies that demonstrate the ability of the coupled 0D-3D methodology in generating physiological pressure and flow waveforms-ultimately enabling the exploration of various physical and physiological parameters for hemodynamics studies of the coupled LV-arterial system. The methods proposed in this paper can be easily applied to other ODE-based boundary conditions as well as to other fluid problems that are modeled by 3D lattice Boltzmann equations and that require direct coupling of dynamic 0D boundary conditions.

Flow-based method demonstrates improved accuracy for calculating wall shear stress in arterial flows from 4D flow MRI data

Four-dimensional flow magnetic resonance imaging (i.e., 4D flow MRI) has become a valuable tool for the in vivo assessment of blood flow within large vessels and cardiac chambers. As wall shear stress (WSS) has been correlated with the development and progression of cardiovascular disease, focus has been directed at developing techniques to quantify WSS directly from 4D flow MRI data. The goal of this study was to compare the accuracy of two such techniques - termed the velocity and flow-based methods - in the setting of simplified and complex flow scenarios. Synthetic MR data were created from exact solutions to the Navier-Stokes equations for the steady and pulsatile flow of an incompressible, Newtonian fluid through a rigid cylinder. In addition, synthetic MR data were created from the predicted velocity fields derived from a fluid-structure interaction (FSI) model of pulsatile flow through a thick-walled, multi-layered model of the carotid bifurcation. Compared to the analytical solutions for steady and pulsatile flow, the flow-based method demonstrated greater accuracy than the velocity-based method in calculating WSS across all changes in fluid velocity/flow rate, tube radius, and image signal-to-noise (p < 0.001). Furthermore, the velocity-based method was more sensitive to boundary segmentation than the flow-based method. When compared to results from the FSI model, the flow-based method demonstrated greater accuracy than the velocity-based method with average differences in time-averaged WSS of 0.31 ± 1.03 Pa and 0.45 ± 1.03 Pa, respectively (p <0.005). These results have implications on the utility, accuracy, and clinical translational of methods to determine WSS from 4D flow MRI.

Computer Model-Driven Design in Cardiovascular Regenerative Medicine

Continuing advances in genomics, molecular and cellular mechanobiology and immunobiology, including transcriptomics and proteomics, and biomechanics increasingly reveal the complexity underlying native tissue and organ structure and function. Identifying methods to repair, regenerate, or replace vital tissues and organs remains one of the greatest challenges of modern biomedical engineering, one that deserves our very best effort. Notwithstanding the continuing need for improving standard methods of investigation, including cell, organoid, and tissue culture, biomaterials development and fabrication, animal models, and clinical research, it is increasingly evident that modern computational methods should play increasingly greater roles in advancing the basic science, bioengineering, and clinical application of regenerative medicine. This brief review focuses on the development and application of computational models of tissue and organ mechanobiology and mechanics for purposes of designing tissue engineered constructs and understanding their development in vitro and in situ. Although the basic approaches are general, for illustrative purposes we describe two recent examples from cardiovascular medicine-tissue engineered heart valves (TEHVs) and tissue engineered vascular grafts (TEVGs)-to highlight current methods of approach as well as continuing needs.

Modeling flow in an in vitro anatomical cerebrovascular model with experimental validation

Acute ischemic stroke (AIS) is a leading cause of mortality that occurs when an embolus becomes lodged in the cerebral vasculature and obstructs blood flow in the brain. The severity of AIS is determined by the location and how extensively emboli become lodged, which are dictated in large part by the cerebral flow and the dynamics of embolus migration which are difficult to measure in vivo in AIS patients. Computational fluid dynamics (CFD) can be used to predict the patient-specific hemodynamics and embolus migration and lodging in the cerebral vasculature to better understand the underlying mechanics of AIS. To be relied upon, however, the computational simulations must be verified and validated. In this study, a realistic in vitro experimental model and a corresponding computational model of the cerebral vasculature are established that can be used to investigate flow and embolus migration and lodging in the brain. First, the in vitro anatomical model is described, including how the flow distribution in the model is tuned to match physiological measurements from the literature. Measurements of pressure and flow rate for both normal and stroke conditions were acquired and corresponding CFD simulations were performed and compared with the experiments to validate the flow predictions. Overall, the CFD simulations were in relatively close agreement with the experiments, to within ±7% of the mean experimental data with many of the CFD predictions within the uncertainty of the experimental measurement. This work provides an in vitro benchmark data set for flow in a realistic cerebrovascular model and is a first step towards validating a computational model of AIS.

Patient-Specific Computational Modeling of Different Cannulation Strategies for Extracorporeal Membrane Oxygenation

Institution of extracorporeal membrane oxygenation (ECMO) results in unique blood flow characteristics to the end-organ vascular beds. We studied the interplay between cardiac-driven and extracorporeal membrane oxygenation (ECMO)-driven flow to vascular beds in different ECMO configurations using a patient-specific computational fluid dynamics (CFD) analysis. A computational ECMO model (femoral artery cannulation [FAC]) was constructed using patient-specific imaging and hemodynamic data. Following model calibration, we augmented the 3D geometrical model to represent alternative ECMO configurations (ascending aorta cannulation [AAC] and subclavian artery cannulation [SAC]). We performed CFD analyses, including a novel virtual color-dye analysis to compare global and regional blood flow and pressure characteristics as well as contributions of cardiac and ECMO-derived flow to the various vascular beds. Flow waveforms at all the aortic branch vessels were pulsatile, despite low cardiac output and predominant nonpulsatile ECMO-driven hemodynamics. Virtual color-dye analysis revealed differential contribution of cardiac and ECMO-derived flow to the end-organ vascular beds in the FAC model, while this was more evenly distributed in the AAC and SAC models. While global hemodynamics were relatively similar between various ECMO configurations, several distinct hemodynamic indices, in particular wall shear stress and oscillatory shear patterns, as well as differential contribution of ECMO-derived flow to various vascular beds, showed remarkable differences. The clinical impact of this study highlighting the relevance of CFD modeling in assessment of complex hemodynamics in ECMO warrants further evaluation.

Patient-specific stomach biomechanics before and after laparoscopic sleeve gastrectomy

BACKGROUND

Obesity has become a global epidemic. Bariatric surgery is considered the most effective therapeutic weapon in terms of weight loss and improvement of quality of life and comorbidities. Laparoscopic sleeve gastrectomy (LSG) is one of the most performed procedures worldwide, although patients carry a nonnegligible risk of developing post-operative GERD and BE.

OBJECTIVES

The aim of this work is the development of computational patient-specific models to analyze the changes induced by bariatric surgery, i.e., the volumetric gastric reduction, the mechanical response of the stomach during an inflation process, and the related elongation strain (ES) distribution at different intragastric pressures.

METHODS

Patient-specific pre- and post-surgical models were extracted from Magnetic Resonance Imaging (MRI) scans of patients with morbid obesity submitted to LSG. Twenty-three patients were analyzed, resulting in forty-six 3D-geometries and related computational analyses.

RESULTS

A significant difference between the mechanical behavior of pre- and post-surgical stomach subjected to the same internal gastric pressure was observed, that can be correlated to a change in the global stomach stiffness and a minor gastric wall tension, resulting in unusual activations of mechanoreceptors following food intake and satiety variation after LSG.

CONCLUSIONS

Computational patient-specific models may contribute to improve the current knowledge about anatomical and physiological changes induced by LSG, aiming at reducing post-operative complications and improving quality of life in the long run.

Patient specific approach to analysis of shear-induced platelet activation in haemodialysis arteriovenous fistula

Shear-induced platelet activation (SIPAct) is an important mechanism of thrombosis initiation under high blood flow. This mechanism relies on the interaction of platelets with the von Willebrand factor (VWF) capable of unfolding under high shear stress. High shear stress occurs in the arteriovenous fistula (AVF) commonly used for haemodialysis. A novel patient-specific approach for the modelling of SIPAct in the AVF was proposed. This enabled us to estimate the SIPAct level via computational fluid dynamics. The suggested approach was applied for the SIPAct analysis in AVF geometries reconstructed from medical images. The approach facilitates the determination of the SIPAct level dependence on both biomechanical (AVF flow rate) and biochemical factors (VWF multimer size). It was found that the dependence of the SIPAct level on the AVF flow rate can be approximated by a power law. The critical flow rate was a decreasing function of the VWF multimer size. Moreover, the critical AVF flow rate highly depended on patient-specific factors, e.g., the vessel geometry. This indicates that the approach may be adopted to elucidate patient-specific thrombosis risk factors in haemodialysis patients.

Data-driven computational models of ventricular-arterial hemodynamics in pediatric pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a complex disease involving increased resistance in the pulmonary arteries and subsequent right ventricular (RV) remodeling. Ventricular-arterial interactions are fundamental to PAH pathophysiology but are rarely captured in computational models. It is important to identify metrics that capture and quantify these interactions to inform our understanding of this disease as well as potentially facilitate patient stratification. Towards this end, we developed and calibrated two multi-scale high-resolution closed-loop computational models using open-source software: a high-resolution arterial model implemented using CRIMSON, and a high-resolution ventricular model implemented using FEniCS. Models were constructed with clinical data including non-invasive imaging and invasive hemodynamic measurements from a cohort of pediatric PAH patients. A contribution of this work is the discussion of inconsistencies in anatomical and hemodynamic data routinely acquired in PAH patients. We proposed and implemented strategies to mitigate these inconsistencies, and subsequently use this data to inform and calibrate computational models of the ventricles and large arteries. Computational models based on adjusted clinical data were calibrated until the simulated results for the high-resolution arterial models matched within 10% of adjusted data consisting of pressure and flow, whereas the high-resolution ventricular models were calibrated until simulation results matched adjusted data of volume and pressure waveforms within 10%. A statistical analysis was performed to correlate numerous data-derived and model-derived metrics with clinically assessed disease severity. Several model-derived metrics were strongly correlated with clinically assessed disease severity, suggesting that computational models may aid in assessing PAH severity.

Computational fluid dynamics investigation on aortic hemodynamics in double aortic arch before and after ligation surgery

Double aortic arch (DAA) malformation is one of the reasons for symptomatic vascular rings, the hemodynamics of which is still poorly understood. This study aims to investigate the blood flow characteristics in patient-specific double aortic arches using computational fluid dynamics (CFD). Seven cases of infantile patients with DAA were collected and their computed tomography images were used to reconstruct 3D computational models. A modified Carreau model was used to consider the non-Newtonian effect of blood and a three-element Windkessel model taking the effect of the age of patients into account was applied to reproduce physiological pressure waveforms. Numerical results show that blood flow distribution and energy loss of DAA depends on relative sizes of the two aortic arches and their angles with the ascending aorta. Ligation of either aortic arch increases the energy loss of blood in the DAA, leading to the increase in cardiac workload. Generally, the rising rate of energy loss before and after the surgery is almost linear with the area ratio between the aortic arch without ligation and the ascending aorta.

A modified method of noninvasive computed tomography derived fractional flow reserve based on the microvascular growth space

OBJECTIVE

To establish a modified method for optimizing outlet boundary conditions (BC) of computed tomography-derived fractional flow reserve (CT-FFR), considering the myocardium as a growth space for microcirculation. The feasibility and diagnostic performance of the modified method in stable coronary artery disease (CAD) were compared with invasive fractional flow reserve (FFR).

METHODS

Nineteen patients (19 lesions) underwent coronary computed tomography angiography (CCTA) and following invasive FFR were included. The microcirculation resistance model generated based on patient-specific anatomical structures and physiological principles was used as the outlet BC, considering the myocardium as a growth space. Brachial artery pressure (BAP) plus or minus 10 mmHg was used as the inlet pressure BC to investigate the effect of the circadian rhythm. After simulation, CT-FFR was compared with invasive FFR with a threshold of 0.80.

RESULTS

Compared with invasive FFR, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of CT-FFR with an optimal threshold of 0.80 were 100%, 100%, 100%, 100%, 100%, respectively. There were a good correlation and consistency between CT-FFR and invasive FFR. Little effect of the circadian fluctuation of BAP was found on the simulation.

CONCLUSIONS

A modified method for CT-FFR with high diagnostic accuracy compared with invasive FFR was established, considering the whole myocardial as the growth space for microcirculation. Circadian fluctuations in BAP could be ignored when it was used as the inlet BC.

Volumetric lattice Boltzmann method for wall stresses of image-based pulsatile flows

Image-based computational fluid dynamics (CFD) has become a new capability for determining wall stresses of pulsatile flows. However, a computational platform that directly connects image information to pulsatile wall stresses is lacking. Prevailing methods rely on manual crafting of a hodgepodge of multidisciplinary software packages, which is usually laborious and error-prone. We present a new computational platform, to compute wall stresses in image-based pulsatile flows using the volumetric lattice Boltzmann method (VLBM). The novelty includes: (1) a unique image processing to extract flow domain and local wall normality, (2) a seamless connection between image extraction and VLBM, (3) an en-route calculation of strain-rate tensor, and (4) GPU acceleration (not included here). We first generalize the streaming operation in the VLBM and then conduct application studies to demonstrate its reliability and applicability. A benchmark study is for laminar and turbulent pulsatile flows in an image-based pipe (Reynolds number: 10 to 5000). The computed pulsatile velocity and shear stress are in good agreements with Womersley's analytical solutions for laminar pulsatile flows and concurrent laboratory measurements for turbulent pulsatile flows. An application study is to quantify the pulsatile hemodynamics in image-based human vertebral and carotid arteries including velocity vector, pressure, and wall-shear stress. The computed velocity vector fields are in reasonably well agreement with MRA (magnetic resonance angiography) measured ones. This computational platform is good for image-based CFD with medical applications and pore-scale porous media flows in various natural and engineering systems.

A computational analysis of atrial fibrillation effects on coronary perfusion across the different myocardial layers

Patients with atrial fibrillation (AF) may present ischemic chest pain in the absence of classical obstructive coronary disease. Among the possible causes, the direct hemodynamic effect exerted by the irregular arrhythmia has not been studied in detail. We performed a computational fluid dynamics analysis by means of a 1D-0D multiscale model of the entire human cardiovascular system, enriched by a detailed mathematical modeling of the coronary arteries and their downstream distal microcirculatory districts (subepicardial, midwall and subendocardial layers). Three mean ventricular rates were simulated (75, 100, 125 bpm) in both sinus rhythm (SR) and atrial fibrillation, and an inter-layer and inter-frequency analysis was conducted focusing on the ratio between mean beat-to-beat blood flow in AF compared to SR. Our results show that AF exerts direct hemodynamic consequences on the coronary microcirculation, causing a reduction in microvascular coronary flow particularly at higher ventricular rates; the most prominent reduction was seen in the subendocardial layers perfused by left coronary arteries (left anterior descending and left circumflex arteries).

Inlet and Outlet Boundary Conditions and Uncertainty Quantification in Volumetric Lattice Boltzmann Method for Image-Based Computational Hemodynamics

Inlet and outlet boundary conditions (BCs) play an important role in newly emerged image-based computational hemodynamics for blood flows in human arteries anatomically extracted from medical images. We developed physiological inlet and outlet BCs based on patients’ medical data and integrated them into the volumetric lattice Boltzmann method. The inlet BC is a pulsatile paraboloidal velocity profile, which fits the real arterial shape, constructed from the Doppler velocity waveform. The BC of each outlet is a pulsatile pressure calculated from the three-element Windkessel model, in which three physiological parameters are tuned by the corresponding Doppler velocity waveform. Both velocity and pressure BCs are introduced into the lattice Boltzmann equations through Guo’s non-equilibrium extrapolation scheme. Meanwhile, we performed uncertainty quantification for the impact of uncertainties on the computation results. An application study was conducted for six human aortorenal arterial systems. The computed pressure waveforms have good agreement with the medical measurement data. A systematic uncertainty quantification analysis demonstrates the reliability of the computed pressure with associated uncertainties in the Windkessel model. With the developed physiological BCs, the image-based computation hemodynamics is expected to provide a computation potential for the noninvasive evaluation of hemodynamic abnormalities in diseased human vessels.

Impact of Respiratory Fluctuation on Hemodynamics in Human Cardiovascular System: A 0-1D Multiscale Model

To explore hemodynamic interaction between the human respiratory system (RS) and cardiovascular system (CVS), here we propose an integrated computational model to predict the CVS hemodynamics with consideration of the respiratory fluctuation (RF). A submodule of the intrathoracic pressure (ITP) adjustment is developed and incorporated in a 0-1D multiscale hemodynamic model of the CVS specified for infant, adolescent, and adult individuals. The model is verified to enable reasonable estimation of the blood pressure waveforms accounting for the RF-induced pressure fluctuations in comparison with clinical data. The results show that the negative ITP caused by respiration increases the blood flow rates in superior and inferior vena cavae; the deep breathing improves the venous return in adolescents but has less influence on infants. It is found that a marked reduction in ITP under pathological conditions can excessively increase the flow rates in cavae independent of the individual ages, which may cause the hemodynamic instability and hence increase the risk of heart failure. Our results indicate that the present 0-1D multiscale CVS model incorporated with the RF effect is capable of providing a useful and effective tool to explore the physiological and pathological mechanisms in association with cardiopulmonary interactions and their clinical applications.

Image-based computational fluid dynamics for estimating pressure drop and fractional flow reserve across iliac artery stenosis: A comparison with in-vivo measurements

Computational Fluid Dynamics (CFD) and time-resolved phase-contrast magnetic resonance imaging (PC-MRI) are potential non-invasive methods for the assessment of the severity of arterial stenoses. Fractional flow reserve (FFR) is the current "gold standard" for determining stenosis severity in the coronary arteries but is an invasive method requiring insertion of a pressure wire. CFD derived FFR (vFFR) is an alternative to traditional catheter derived FFR now available commercially for coronary artery assessment, however, it can potentially be applied to a wider range of vulnerable vessels such as the iliac arteries. In this study CFD simulations are used to assess the ability of vFFR in predicting the stenosis severity in a patient with a stenosis of 77% area reduction (>50% diameter reduction) in the right iliac artery. Variations of vFFR, overall pressure drop and flow split between the vessels were observed by using different boundary conditions. Correlations between boundary condition parameters and resulting flow variables are presented. The study concludes that vFFR has good potential to characterise iliac artery stenotic disease.

Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning

Probabilistic estimation of cardiac electrophysiological model parameters serves an important step toward model personalization and uncertain quantification. The expensive computation associated with these model simulations, however, makes direct Markov Chain Monte Carlo (MCMC) sampling of the posterior probability density function (pdf) of model parameters computationally intensive. Approximated posterior pdfs resulting from replacing the simulation model with a computationally efficient surrogate, on the other hand, have seen limited accuracy. In this study, we present a Bayesian active learning method to directly approximate the posterior pdf function of cardiac model parameters, in which we intelligently select training points to query the simulation model in order to learn the posterior pdf using a small number of samples. We integrate a generative model into Bayesian active learning to allow approximating posterior pdf of high-dimensional model parameters at the resolution of the cardiac mesh. We further introduce new acquisition functions to focus the selection of training points on better approximating the shape rather than the modes of the posterior pdf of interest. We evaluated the presented method in estimating tissue excitability in a 3D cardiac electrophysiological model in a range of synthetic and real-data experiments. We demonstrated its improved accuracy in approximating the posterior pdf compared to Bayesian active learning using regular acquisition functions, and substantially reduced computational cost in comparison to existing standard or accelerated MCMC sampling.

Design and Evaluation of Personalized Percutaneous Coronary Intervention Surgery Simulation System

In recent years, medical simulators have been widely applied to a broad range of surgery training tasks. However, most of the existing surgery simulators can only provide limited immersive environments with a few pre-processed organ models, while ignoring the instant modeling of various personalized clinical cases, which brings substantive differences between training experiences and real surgery situations. To this end, we present a virtual reality (VR) based surgery simulation system for personalized percutaneous coronary intervention (PCI). The simulation system can directly take patient-specific clinical data as input and generate virtual 3D intervention scenarios. Specially, we introduce a fiber-based patient-specific cardiac dynamic model to simulate the nonlinear deformation among the multiple layers of the cardiac structure, which can well respect and correlate the atriums, ventricles and vessels, and thus gives rise to more effective visualization and interaction. Meanwhile, we design a tracking and haptic feedback hardware, which can enable users to manipulate physical intervention instruments and interact with virtual scenarios. We conduct quantitative analysis on deformation precision and modeling efficiency, and evaluate the simulation system based on the user studies from 16 cardiologists and 20 intervention trainees, comparing it to traditional desktop intervention simulators. The results confirm that our simulation system can provide a better user experience, and is a suitable platform for PCI surgery training and rehearsal.

An optimal control approach to determine resistance-type boundary conditions from in-vivo data for cardiovascular simulations

The choice of appropriate boundary conditions is a fundamental step in computational fluid dynamics (CFD) simulations of the cardiovascular system. Boundary conditions, in fact, highly affect the computed pressure and flow rates, and consequently haemodynamic indicators such as wall shear stress (WSS), which are of clinical interest. Devising automated procedures for the selection of boundary conditions is vital to achieve repeatable simulations. However, the most common techniques do not automatically assimilate patient-specific data, relying instead on expensive and time-consuming manual tuning procedures. In this work, we propose a technique for the automated estimation of outlet boundary conditions based on optimal control. The values of resistive boundary conditions are set as control variables and optimized to match available patient-specific data. Experimental results on four aortic arches demonstrate that the proposed framework can assimilate 4D-Flow MRI data more accurately than two other common techniques based on Murray's law and Ohm's law.

Characterization of Flow Dynamics in the Pulmonary Bifurcation of Patients With Repaired Tetralogy of Fallot: A Computational Approach

The hemodynamic environment of the pulmonary bifurcation is of great importance for adult patients with repaired tetralogy of Fallot (rTOF) due to possible complications in the pulmonary valve and narrowing of the left pulmonary artery (LPA). The aim of this study was to computationally investigate the effect of geometrical variability and flow split on blood flow characteristics in the pulmonary trunk of patient-specific models. Data from a cohort of seven patients was used retrospectively and the pulmonary hemodynamics was investigated using averaged and MRI-derived patient-specific boundary conditions on the individualized models, as well as a statistical mean geometry. Geometrical analysis showed that curvature and tortuosity are higher in the LPA branch, compared to the right pulmonary artery (RPA), resulting in complex flow patterns in the LPA. The computational analysis also demonstrated high time-averaged wall shear stress (TAWSS) at the outer wall of the LPA and the wall of the RPA proximal to the junction. Similar TAWSS patterns were observed for averaged boundary conditions, except for a significantly modified flow split assigned at the outlets. Overall, this study enhances our understanding about the flow development in the pulmonary bifurcation of rTOF patients and associates some morphological characteristics with hemodynamic parameters, highlighting the importance of patient-specificity in the models. To confirm these findings, further studies are required with a bigger cohort of patients.

Effect of Subject-Specific, Spatially Reduced, and Idealized Boundary Conditions on the Predicted Hemodynamic Environment in the Murine Aorta

Mouse models of atherosclerosis have become effective resources to study atherogenesis, including the relationship between hemodynamics and lesion development. Computational methods aid the prediction of the in vivo hemodynamic environment in the mouse vasculature, but careful selection of inflow and outflow boundary conditions (BCs) is warranted to promote model accuracy. Herein, we investigated the impact of animal-specific versus reduced/idealized flow boundary conditions on predicted blood flow patterns in the mouse thoracic aorta. Blood velocities were measured in the aortic root, arch branch vessel, and descending aorta in ApoE^-/- mice using phase-contrast MRI. Computational geometries were derived from micro-CT imaging and combinations of high-fidelity or reduced/idealized MR-derived BCs were applied to predict the bulk flow field and hemodynamic metrics (e.g., wall shear stress, WSS; cross-flow index, CFI). Results demonstrate that pressure-free outlet BCs significantly overestimate outlet flow rates as compared to measured values. When compared to models that incorporate 3-component inlet velocity data [[Formula: see text]] and time-varying outlet mass flow splits [[Formula: see text]] (i.e., high-fidelity model), neglecting in-plane inlet velocity components (i.e., [Formula: see text])) leads to errors in WSS and CFI values ranging from 10 to 30% across the model domain whereas the application of a steady outlet mass flow splits results in negligible differences in these hemodynamics metrics. This investigation highlights that 3-component inlet velocity data and at least steady mass flow splits are required for accurate predictions of flow patterns in the mouse thoracic aorta.

Interventional Planning for Endovascular Revision of a Lateral Tunnel Fontan: A Patient-Specific Computational Analysis

Introduction

A 2-year-old female with hypoplastic left heart syndrome (HLHS)-variant, a complex congenital heart defect (CHD) characterized by the underdevelopment of the left ventricle, presented with complications following single ventricle palliation. Diagnostic work-up revealed elevated Fontan pathway pressures, as well as significant dilation of the inferior Fontan pathway with inefficient swirling flow and hepatic venous reflux. Due to the frail condition of the patient, the clinical team considered an endovascular revision of the Fontan pathway. In this work, we performed a computational fluid dynamics (CFD) analysis informed by data on anatomy, flow, and pressure to investigate the hemodynamic effect of the endovascular Fontan revision.

Methods

A patient-specific anatomical model of the Fontan pathway was constructed from magnetic resonance imaging (MRI) data using the cardiovascular modeling software CardiovasculaR Integrated Modeling and SimulatiON (CRIMSON). We first created and calibrated a pre-intervention 3D-0D multi-scale model of the patient’s circulation using fluid-structure interaction (FSI) analyses and custom lumped parameter models (LPMs), including the Fontan pathway, the single ventricle, arterial and venous systemic, and pulmonary circulations. Model parameters were iteratively tuned until simulation results matched clinical data on flow and pressure. Following calibration of the pre-intervention model, a custom bifurcated endograft was introduced into the anatomical model to virtually assess post-intervention hemodynamics.

Results

The pre-intervention model successfully reproduced the clinical hemodynamic data on regional flow splits, pressures, and hepatic venous reflux. The proposed endovascular repair model revealed increases of mean and pulse pressure at the inferior vena cava (IVC) of 6 and 29%, respectively. Inflows at the superior vena cava (SVC) and IVC were each reduced by 5%, whereas outflows at the left pulmonary artery (LPA) and right pulmonary artery (RPA) increased by 4%. Hepatic venous reflux increased by 6%.

Conclusion

Our computational analysis indicated that the proposed endovascular revision would lead to unfavorable hemodynamic conditions. For these reasons, the clinical team decided to forgo the proposed endovascular repair and to reassess the management of this patient. This study confirms the relevance of CFD modeling as a beneficial tool in surgical planning for single ventricle CHD patients.

Hemodynamics and remodeling of the portal confluence in patients with malignancies of the pancreatic head: a pilot study towards planned and circumferential vein resections

BACKGROUND

We designed a retrospective computational study to evaluate the effects of hemodynamics on portal confluence remodeling in real models of patients with malignancies of the pancreatic head.

METHODS

Patient-specific models were created according to computed tomography data. Fluid dynamics was simulated by using finite-element methods. Computational results were compared to morphological findings.

RESULTS

Five patients underwent total pancreatectomy, one had duodenopancreatectomy. Vein resection was performed en-bloc with the specimen. Histopathological findings showed that in patients without a vein stenosis and a normal hemodynamics, the three-layered wall of the vein was preserved. In patients with a stenosis > 70% of vein diameter and modified hemodynamics, the three-layered structure of the resected vein was replaced by a dense inflammatory infiltrate in absence of tumor infiltration.

CONCLUSIONS

The portal confluence involved by malignancies of the pancreatic head undergoes a remodeling that is not mainly due to a wall infiltration by the tumor but instead to a persistent pathological hemodynamics that disrupts the balance between eutrophic remodeling and degradative process of the vein wall that can lead to the complete upheaval of the three-layered vein wall. This finding can have useful surgical application in planning resection of the vein involved by tumor growth.

Sequential numerical simulation of vascular remodeling and thrombosis in unconventional hybrid repair of ruptured middle aortic syndrome

Unconventional surgical procedures may be utilized in treating complicated middle aortic syndrome (MAS), the outcome and prognosis of which remain largely undetermined due to limited numbers and significant heterogeneity of this population. Using computational fluid dynamics (CFD) analysis, this study aimed to assess the dynamic changes of postoperative aortic flow in seeking to unveil the relationship between hemodynamics and vascular remodeling and thrombotic events. One patient with middle aortic syndrome complicated with aortic rupture was treated with hybrid repair of extra-anatomic bypass and fenestrated endovascular aortic repair. The patient was followed-up for 8 months by computational tomography angiography and Doppler ultrasound. Thoracoabdominal aortic blood flow and locations with ongoing thrombosis at 1, 3, and 6 months postoperatively were simulated and analyzed. Remodeling processes, including low wall shear-mediated constrictive remodeling of non-stented aorta, neointimal hyperplasia at suture lines, and minimal thrombosis at various locations, were evident. Meanwhile, abdominal blood flow was tri-phasic at 1 month after surgery, and was reversed and stabilized at 6 months. The distribution of newly formed thrombus vary at different follow-up stages, which were in line with the numerical simulation of thrombosis from different postoperative time points. CFD-based sequential monitoring is of promising value in capturing dynamic changes of vascular outcome.

Extended finite element method for fluid-structure interaction in wave membrane blood pump

Numerical simulations of cardiac blood pump systems are integral to the optimization of device design, hydraulic performance and hemocompatibility. In wave membrane blood pumps, blood propulsion arises from the wave propagation along an oscillating immersed membrane, which generates small pockets of fluid that are pushed towards the outlet against an adverse pressure gradient. We studied the Fluid-Structure Interaction between the oscillating membrane and the blood flow via three-dimensional simulations using the Extended Finite Element Method (XFEM), an unfitted numerical technique that avoids remeshing by using a fluid fixed mesh. Our three-dimensional numerical simulations in a realistic pump geometry highlighted, for the first time in this field of application, that XFEM is a reliable strategy to handle complex industrial problems. Moreover, they showed the role of the membrane deformation in promoting a blood flow towards the outlet despite an adverse pressure gradient. We also simulated the pump system at different pressure conditions and we validated the numerical results against in-vitro experimental data.

Assessing the methodology used to study the ascending aorta haemodynamics in bicuspid aortic valve

Aims

Modern imaging techniques provide evermore-detailed anatomical and physiological information for use in computational fluid dynamics to predict the behaviour of physiological phenomena. Computer modelling can help plan suitable interventions. Our group used magnetic resonance imaging and computational fluid dynamics to study the haemodynamic variables in the ascending aorta in patients with bicuspid aortic valve before and after isolated tissue aortic valve replacement. Computer modelling requires turning a physiological model into a mathematical one, solvable by equations that undergo multiple iterations in four dimensions. Creating these models involves several steps with manual inputs, making the process prone to errors and limiting its inter- and intra-operator reproducibility. Despite these challenges, we created computational models for each patient to study ascending aorta blood flow before and after surgery.

Methods and results

Magnetic resonance imaging provided the anatomical and velocity data required for the blood flow simulation. Patient-specific in- and outflow boundary conditions were used for the computational fluid dynamics analysis. Haemodynamic variables pertaining to blood flow pattern and derived from the magnetic resonance imaging data were calculated. However, we encountered problems in our multi-step methodology, most notably processing the flow data. This meant that other variables requiring computation with computational fluid dynamics could not be calculated.

Conclusion

Creating a model for computational fluid dynamics analysis is as complex as the physiology under scrutiny. We discuss some of the difficulties associated with creating such models, along with suggestions for improvements in order to yield reliable and beneficial results.

Ischemic Heart Disease: Noninvasive Imaging Techniques and Findings

Ischemic heart disease is a leading cause of death worldwide and comprises a large proportion of annual health care expenditure. Management of ischemic heart disease is now best guided by the physiologic significance of coronary artery stenosis. Invasive coronary angiography is the standard for diagnosing coronary artery stenosis. However, it is expensive and has risks including vascular access site complications and contrast material–induced nephropathy. Invasive coronary angiography requires fractional flow reserve (FFR) measurement to determine the physiologic significance of a coronary artery stenosis. Multiple noninvasive cardiac imaging modalities can also anatomically delineate or functionally assess for significant coronary artery stenosis, as well as detect the presence of myocardial infarction (MI). While coronary CT angiography can help assess the degree of anatomic stenosis, its inability to assess the physiologic significance of lesions limits its specificity. Physiologic significance of coronary artery stenosis can be determined by cardiac MR vasodilator or dobutamine stress imaging, CT stress perfusion imaging, FFR CT, PET myocardial perfusion imaging (MPI), SPECT MPI, and stress echocardiography. Clinically unrecognized MI, another clear indicator of physiologically significant coronary artery disease, is relatively common and is best evaluated with cardiac MRI. The authors illustrate the spectrum of imaging findings of ischemic heart disease (coronary artery disease, myocardial ischemia, and MI); highlight the advantages and disadvantages of the various noninvasive imaging methods used to assess ischemic heart disease, as illustrated by recent clinical trials; and summarize current indications and contraindications for noninvasive imaging techniques for detection of ischemic heart disease.

Online supplemental material is available for this article.

Published under a CC BY 4.0 license.

Synthetic Database of Aortic Morphometry and Hemodynamics: Overcoming Medical Imaging Data Availability

Modeling of hemodynamics and artificial intelligence have great potential to support clinical diagnosis and decision making. While hemodynamics modeling is extremely time- and resource-consuming, machine learning (ML) typically requires large training data that are often unavailable. The aim of this study was to develop and evaluate a novel methodology generating a large database of synthetic cases with characteristics similar to clinical cohorts of patients with coarctation of the aorta (CoA), a congenital heart disease associated with abnormal hemodynamics. Synthetic data allows use of ML approaches to investigate aortic morphometric pathology and its influence on hemodynamics. Magnetic resonance imaging data (154 patients as well as of healthy subjects) of aortic shape and flow were used to statistically characterize the clinical cohort. The methodology generating the synthetic cohort combined statistical shape modeling of aortic morphometry and aorta inlet flow fields and numerical flow simulations. Hierarchical clustering and non-linear regression analysis were successfully used to investigate the relationship between morphometry and hemodynamics and to demonstrate credibility of the synthetic cohort by comparison with a clinical cohort. A database of 2652 synthetic cases with realistic shape and hemodynamic properties was generated. Three shape clusters and respective differences in hemodynamics were identified. The novel model predicts the CoA pressure gradient with a root mean square error of 4.6 mmHg. In conclusion, synthetic data for anatomy and hemodynamics is a suitable means to address the lack of large datasets and provide a powerful basis for ML to gain new insights into cardiovascular diseases.

Multiscale modeling meets machine learning: What can we learn?

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.