In vivo validation of numerical prediction for turbulence intensity in an aortic coarctation

Advancement in computational simulation and validation of congenital heart disease: a review

The improvement in congenital heart disease (CHD) treatment and management has increased the life expectancy in infants. However, the long-term efficacy is difficult to assess and thus, computational modelling has been applied for evaluating this. Here, we provide an overview of the applications of computational modelling in CHD based on three categories; CHD involving large blood vessels only, heart chambers only, and CHD that occurs at multiple heart structures. We highlight the advancement of computational simulation of CHD that uses multiscale and multiphysics modelling to ensure a complete representation of the heart and circulation. We provide a brief future direction of computational modelling of CHD such as to include growth and remodelling, detailed conduction system, and occurrence of myocardial infarction. We also proposed validation technique using advanced three-dimensional (3D) printing and particle image velocimetry (PIV) technologies to improve the model accuracy.

Turbulent Intensity of Blood Flow in the Healthy Aorta Increases With Dobutamine Stress and is Related to Cardiac Output

Introduction: The blood flow in the normal cardiovascular system is predominately laminar but operates close to the threshold to turbulence. Morphological distortions such as vascular and valvular stenosis can cause transition into turbulent blood flow, which in turn may cause damage to tissues in the cardiovascular system. A growing number of studies have used magnetic resonance imaging (MRI) to estimate the extent and degree of turbulent flow in different cardiovascular diseases. However, the way in which heart rate and inotropy affect turbulent flow has not been investigated. In this study we hypothesized that dobutamine stress would result in higher turbulence intensity in the healthy thoracic aorta.

Method: 4D flow MRI data were acquired in twelve healthy subjects at rest and with dobutamine, which was infused until the heart rate increased by 60% when compared to rest. A semi-automatic segmentation method was used to segment the thoracic aorta in the 4D flow MR images. Subsequently, flow velocity and several turbulent kinetic energy (TKE) parameters were calculated in the ascending aorta, aortic arch, descending aorta and whole thoracic aorta.

Results: With dobutamine infusion there was an increase in heart rate (66 ± 9 vs. 108 ± 13 bpm, p < 0.001) and stroke volume (88 ± 13 vs. 102 ± 25 ml, p < 0.01). Additionally, there was an increase in Peak Average velocity (0.7 ± 0.1 vs. 1.2 ± 0.2 m/s, p < 0.001, Peak Max velocity (1.3 ± 0.1 vs. 2.0 ± 0.2 m/s, p < 0.001), Peak Total TKE (2.9 ± 0.7 vs. 8.0 ± 2.2 mJ, p < 0.001), Peak Median TKE (36 ± 7 vs. 93 ± 24 J/m3, p = 0.002) and Peak Max TKE (176 ± 33 vs. 334 ± 69 J/m3, p < 0.001). The relation between cardiac output and Peak Total TKE in the whole thoracic aorta was very strong (R² = 0.90, p < 0.001).

Conclusion: TKE of blood flow in the healthy thoracic aorta increases with dobutamine stress and is strongly related to cardiac output. Quantification of such turbulence intensity parameters with cardiac stress may serve as a risk assessment of aortic disease development.

Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases

Hemodynamic factors, induced by pulsatile blood flow, play a crucial role in vascular health and diseases, such as the initiation and progression of atherosclerosis. Computational fluid dynamics, finite element analysis, and fluid-structure interaction simulations have been widely used to quantify detailed hemodynamic forces based on vascular images commonly obtained from computed tomography angiography, magnetic resonance imaging, ultrasound, and optical coherence tomography. In this review, we focus on methods for obtaining accurate hemodynamic factors that regulate the structure and function of vascular endothelial and smooth muscle cells. We describe the multiple steps and recent advances in a typical patient-specific simulation pipeline, including medical imaging, image processing, spatial discretization to generate computational mesh, setting up boundary conditions and solver parameters, visualization and extraction of hemodynamic factors, and statistical analysis. These steps have not been standardized and thus have unavoidable uncertainties that should be thoroughly evaluated. We also discuss the recent development of combining patient-specific models with machine-learning methods to obtain hemodynamic factors faster and cheaper than conventional methods. These critical advances widen the use of biomechanical simulation tools in the research and potential personalized care of vascular diseases.

Scale-Resolving Simulations of Steady and Pulsatile Flow Through Healthy and Stenotic Heart Valves

Blood-flow downstream of stenotic and healthy aortic valves exhibits intermittent random fluctuations in the velocity field which are associated with turbulence. Such flows warrant the use of computationally demanding scale-resolving models. The aim of this work was to compute and quantify this turbulent flow in healthy and stenotic heart valves for steady and pulsatile flow conditions. Large eddy simulations (LESs) and Reynolds-averaged Navier-Stokes (RANS) simulations were used to compute the flow field at inlet Reynolds numbers of 2700 and 5400 for valves with an opening area of 70 mm2 and 175 mm2 and their projected orifice-plate type counterparts. Power spectra and turbulent kinetic energy were quantified on the centerline. Projected geometries exhibited an increased pressure-drop (>90%) and elevated turbulent kinetic energy levels (>147%). Turbulence production was an order of magnitude higher in stenotic heart valves compared to healthy valves. Pulsatile flow stabilizes flow in the acceleration phase, whereas onset of deceleration triggered (healthy valve) or amplified (stenotic valve) turbulence. Simplification of the aortic valve by projecting the orifice area should be avoided in computational fluid dynamics (CFD). RANS simulations may be used to predict the transvalvular pressure-drop, but scale-resolving models are recommended when detailed information of the flow field is required.

Applications of computational fluid dynamics to congenital heart diseases: a practical review for cardiovascular professionals

INTRODUCTION

The increased survival rate of patients with congenital heart disease (CHD) has made it likely that 70%-95% of infants with CHDs surviving into adulthood often require careful follow-up and (repeat) interventions. Patients with CHDs often have abnormal blood flow patterns, due to both primary cardiac defect and the consequent surgical or endovascular repair.

AREA COVERED

Computational fluid dynamics (CFD) alone or coupled with advanced imaging tools can assess blood flow patterns of CHDs to both understand their pathophysiology and anticipate the results of surgical or interventional repair.

EXPERT OPINION

CFD is a mathematical technique that quantifies and describes the characteristics of fluid flow using the laws of physics. Through dedicated software based on virtual reconstruction and simulation and patients' real data coming from computed tomography, magnetic resonance imaging, and 3/4 D-ultrasound, reconstruction of models of circulation of most CHD can be accomplished. CFD can provide insights about the pathophysiology of coronary artery anomalies, interatrial shunts, coarctation of the aorta and aortic bicuspid valve, tetralogy of Fallot and univentricular heart, with the capability in some cases of simulating different types of surgical or interventional repair and tailoring the treatment on the basis of these findings.

Mathematical modeling of shear-activated targeted nanoparticle drug delivery for the treatment of aortic diseases

The human aorta is a high-risk area for vascular diseases, which are commonly restored by thoracic endovascular aortic repair. In this paper, we report a promising shear-activated targeted nanoparticle drug delivery strategy to assist in the treatment of coarctation of the aorta and aortic aneurysm. Idealized three-dimensional geometric models of coarctation of the aorta and aortic aneurysm are designed, respectively. The unique hemodynamic environment of the diseased aorta is used to improve nanoparticle drug delivery. Micro-carriers with nanoparticle drugs would be targeting activated to release nanoparticle drugs by local abnormal shear stress rate (SSR). Coarctation of the aorta provides a high SSR hemodynamic environment, while the aortic aneurysm is exposed to low SSR. We propose a method to calculate the SSR thresholds for the diseased aorta. Results show that the upstream near-wall area of the diseased location is an ideal injection location for the micro-carriers, which could be activated by the abnormal SSR. Released nanoparticle drugs would be successfully targeted delivered to the aortic diseased wall. Besides, the high diffusivity of the micro-carriers and nanoparticle drugs has a significant impact on the surface drug concentrations of the diseased aortic walls, especially for aortic aneurysms. This study preliminary demonstrates the feasibility of shear-activated targeted nanoparticle drug delivery in the treatment of aortic diseases and provides a theoretical basis for developing the drug delivery system and novel therapy.

Effects of Uncertainty of Outlet Boundary Conditions in a Patient-Specific Case of Aortic Coarctation

Computational Fluid Dynamics (CFD) simulations of blood flow are widely used to compute a variety of hemodynamic indicators such as velocity, time-varying wall shear stress, pressure drop, and energy losses. One of the major advances of this approach is that it is non-invasive. The accuracy of the cardiovascular simulations depends directly on the level of certainty on input parameters due to the modelling assumptions or computational settings. Physiologically suitable boundary conditions at the inlet and outlet of the computational domain are needed to perform a patient-specific CFD analysis. These conditions are often affected by uncertainties, whose impact can be quantified through a stochastic approach. A methodology based on a full propagation of the uncertainty from clinical data to model results is proposed here. It was possible to estimate the confidence associated with model predictions, differently than by deterministic simulations. We evaluated the effect of using three-element Windkessel models as the outflow boundary conditions of a patient-specific aortic coarctation model. A parameter was introduced to calibrate the resistances of the Windkessel model at the outlets. The generalized Polynomial Chaos method was adopted to perform the stochastic analysis, starting from a few deterministic simulations. Our results show that the uncertainty of the input parameter gave a remarkable variability on the volume flow rate waveform at the systolic peak simulating the conditions before the treatment. The same uncertain parameter had a slighter effect on other quantities of interest, such as the pressure gradient. Furthermore, the results highlight that the fine-tuning of Windkessel resistances is not necessary to simulate the post-stenting scenario.

Assessment of turbulent blood flow and wall shear stress in aortic coarctation using image-based simulations

In this study, we analyzed turbulent flows through a phantom (a 180[Formula: see text] bend with narrowing) at peak systole and a patient-specific coarctation of the aorta (CoA), with a pulsating flow, using magnetic resonance imaging (MRI) and computational fluid dynamics (CFD). For MRI, a 4D-flow MRI is performed using a 3T scanner. For CFD, the standard [Formula: see text], shear stress transport [Formula: see text], and Reynolds stress (RSM) models are applied. A good agreement between measured and simulated velocity is obtained for the phantom, especially for CFD with RSM. The wall shear stress (WSS) shows significant differences between CFD and MRI in absolute values, due to the limited near-wall resolution of MRI. However, normalized WSS shows qualitatively very similar distributions of the local values between MRI and CFD. Finally, a direct comparison between in vivo 4D-flow MRI and CFD with the RSM turbulence model is performed in the CoA. MRI can properly identify regions with locally elevated or suppressed WSS. If the exact values of the WSS are necessary, CFD is the preferred method. For future applications, we recommend the use of the combined MRI/CFD method for analysis and evaluation of the local flow patterns and WSS in the aorta.

On the relevance of boundary conditions and viscosity models in blood flow simulations in patient-specific aorto-coronary bypass models

Physiologically realistic results are the aim of every blood flow simulation. This is not different in aorto-coronary bypasses where the properties of the coronary circulation may significantly affect the relevance of the performed simulations. By considering three patient-specific bypass geometries, the present article focuses on two aspects of the coronary blood flow - its phasic flow pattern and its behaviour affected by blood rheology. For the phasic flow property, a multiscale modelling approach is chosen as a means to assess the ability of five different types of coronary boundary conditions (mean arterial pressure, Windkessel model and three lumped parameter models) to attain realistic coronary haemodynamics. From the analysed variants of boundary conditions, the best option in terms of physiological characteristics and its potential for use in patient-based simulations, is utilised to account for the effect of shear-dependent viscosity on the resulting haemodynamics and wall shear stress stimulation. Aside from the Newtonian model, the blood rheology is approximated by two non-Newtonian models in order to determine whether the choice of a viscosity model is important in simulations involving coronary circulation. A comprehensive analysis of obtained results demonstrated notable superiority of all lumped parameter models, especially in comparison to the constant outlet pressure, which regardless of bypass type gave overestimated and physiologically misleading results. In terms of rheology, it was noted that blood in undamaged coronary arteries behaves as a Newtonian fluid, whereas in vessels with atypical lumen geometry, such as that of anastomosis or stenosis, its shear-thinning behaviour should not be ignored.

Multiscale Computational Fluid Dynamics Modeling for Personalized Liver Cancer Radioembolization Dosimetry

Yttrium-90 (90Y) radioembolization is a minimally invasive procedure increasingly used for advanced liver cancer treatment. In this method, radioactive microspheres are injected into the hepatic arterial bloodstream to target, irradiate, and kill cancer cells. Accurate and precise treatment planning can lead to more efficient and safer treatment by delivering a higher radiation dose to the tumor while minimizing the exposure of the surrounding liver parenchyma. Treatment planning primarily relies on the estimated radiation dose delivered to tissue. However, current methods used to estimate the dose are based on simplified assumptions that make the dosimetry results unreliable. In this work, we present a computational model to predict the radiation dose from the 90Y activity in different liver segments to provide a more realistic and personalized dosimetry. Computational fluid dynamics (CFD) simulations were performed in a 3D hepatic arterial tree model segmented from cone-beam CT angiographic data obtained from a patient with hepatocellular carcinoma (HCC). The microsphere trajectories were predicted from the velocity field. 90Y dose distribution was then calculated from the volumetric distribution of the microspheres. Two injection locations were considered for the microsphere administration, a lobar and a selective injection. Results showed that 22% and 82% of the microspheres were delivered to the tumor, after each injection, respectively, and the combination of both injections ultimately delivered 49% of the total administered 90Y microspheres to the tumor. Results also illustrated the nonhomogeneous distribution of microspheres between liver segments, indicating the importance of developing patient-specific dosimetry methods for effective radioembolization treatment.

An Introduction to Biomedical Computational Fluid Dynamics

Computational fluid dynamics (CFD) is a tool that has been used by engineers for over 50 years to analyse heat transfer and fluid flow phenomena. In recent years, there have been rapid developments in biomedical and health research applications of CFD. It has been used to evaluate drug delivery systems, analyse physiological flows (e.g. laryngeal jet flow), facilitate surgical planning (e.g. management of intracranial aneurysms), and develop medical devices (e.g. vascular stents and valve prostheses). Due to the complexity of these fluid flows, it demands an interdisciplinary approach consisting of engineers, computer scientists, and mathematicians to develop the computer programs and software used to solve the mathematical equations. Advances in technology and decreases in computational cost are allowing CFD to be more widely accessible and therefore used in more varied contexts. Cardiovascular medicine is the most common area of biomedical research in which CFD is currently being used, followed closely by upper and lower respiratory tract medicine. CFD is also being used in research investigating cerebrospinal fluid, synovial joints, and intracellular fluid. Although CFD can provide meaningful and aesthetically pleasing outputs, interpretation of the data can be challenging for those without a strong understanding of mathematical and engineering principles. Future development and evolution of computational medicine will therefore require close collaboration between experts in engineering, computer science, and biomedical research. This chapter aims to introduce computational fluid dynamics and present the reader with the basics of biological fluid properties, the CFD method, and its applications within biomedical research through published examples, in hope of bridging knowledge gaps in this rapidly emerging method of biomedical analysis.

Model Verification and Error Sensitivity of Turbulence-Related Tensor Characteristics in Pulsatile Blood Flow Simulations

Model verification, validation, and uncertainty quantification are essential procedures to estimate errors within cardiovascular flow modeling, where acceptable confidence levels are needed for clinical reliability. While more turbulent-like studies are frequently observed within the biofluid community, practical modeling guidelines are scarce. Verification procedures determine the agreement between the conceptual model and its numerical solution by comparing for example, discretization and phase-averaging-related errors of specific output parameters. This computational fluid dynamics (CFD) study presents a comprehensive and practical verification approach for pulsatile turbulent-like blood flow predictions by considering the amplitude and shape of the turbulence-related tensor field using anisotropic invariant mapping. These procedures were demonstrated by investigating the Reynolds stress tensor characteristics in a patient-specific aortic coarctation model, focusing on modeling-related errors associated with the spatiotemporal resolution and phase-averaging sampling size. Findings in this work suggest that attention should also be put on reducing phase-averaging related errors, as these could easily outweigh the errors associated with the spatiotemporal resolution when including too few cardiac cycles. Also, substantially more cycles are likely needed than typically reported for these flow regimes to sufficiently converge the phase-instant tensor characteristics. Here, higher degrees of active fluctuating directions, especially of lower amplitudes, appeared to be the most sensitive turbulence characteristics.

Super-resolution and denoising of 4D-Flow MRI using physics-Informed deep neural nets

BACKGROUND AND OBJECTIVE

Time resolved three-dimensional phase contrast magnetic resonance imaging (4D-Flow MRI) has been used to non-invasively measure blood velocities in the human vascular system. However, issues such as low spatio-temporal resolution, acquisition noise, velocity aliasing, and phase-offset artifacts have hampered its clinical application. In this research, we developed a purely data-driven method for super-resolution and denoising of 4D-Flow MRI.

METHODS

The flow velocities, pressure, and the MRI image magnitude are modeled as a patient-specific deep neural net (DNN). For training, 4D-Flow MRI images in the complex Cartesian space are used to impose data-fidelity. Physics of fluid flow is imposed through regularization. Creative loss function terms have been introduced to handle noise and super-resolution. The trained patient-specific DNN can be sampled to generate noise-free high-resolution flow images. The proposed method has been implemented using the TensorFlow DNN library and tested on numerical phantoms and validated in-vitro using high-resolution particle image velocitmetry (PIV) and 4D-Flow MRI experiments on transparent models subjected to pulsatile flow conditions.

RESULTS

In case of numerical phantoms, we were able to increase spatial resolution by a factor of 100 and temporal resolution by a factor of 5 compared to the simulated 4D-Flow MRI. There is an order of magnitude reduction of velocity normalized root mean square error (vNRMSE). In case of the in-vitro validation tests with PIV as reference, there is similar improvement in spatio-temporal resolution. Although the vNRMSE is reduced by 50%, the method is unable to negate a systematic bias with respect to the reference PIV that is introduced by the 4D-Flow MRI measurement.

CONCLUSIONS

This work has demonstrated the feasibility of using the readily available machinery of deep learning to enhance 4D-Flow MRI using a purely data-driven method. Unlike current state-of-the-art methods, the proposed method is agnostic to geometry and boundary conditions and therefore eliminates the need for tedious tasks such as accurate image segmentation for geometry, image registration, and estimation of boundary flow conditions. Arbitrary regions of interest can be selected for processing. This work will lead to user-friendly analysis tools that will enable quantitative hemodynamic analysis of vascular diseases in a clinical setting.

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

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

Roudsari B, Vu CT, Roncali E. Computational Modeling of the Liver Arterial Blood Flow for Microsphere Therapy: Effect of Boundary Conditions

Transarterial embolization is a minimally invasive treatment for advanced liver cancer using microspheres loaded with a chemotherapeutic drug or radioactive yttrium-90 (90Y) that are injected into the hepatic arterial tree through a catheter. For personalized treatment, the microsphere distribution in the liver should be optimized through the injection volume and location. Computational fluid dynamics (CFD) simulations of the blood flow in the hepatic artery can help estimate this distribution if carefully parameterized. An important aspect is the choice of the boundary conditions imposed at the inlet and outlets of the computational domain. In this study, the effect of boundary conditions on the hepatic arterial tree hemodynamics was investigated. The outlet boundary conditions were modeled with three-element Windkessel circuits, representative of the downstream vasculature resistance. Results demonstrated that the downstream vasculature resistance affected the hepatic artery hemodynamics such as the velocity field, the pressure field and the blood flow streamline trajectories. Moreover, the number of microspheres received by the tumor significantly changed (more than 10% of the total injected microspheres) with downstream resistance variations. These findings suggest that patient-specific boundary conditions should be used in order to achieve a more accurate drug distribution estimation with CFD in transarterial embolization treatment planning.

Towards non-invasive computational-mechanics and imaging-based diagnostic framework for personalized cardiology for coarctation

Coarctation of the aorta (COA) is a congenital narrowing of the proximal descending aorta. Although accurate and early diagnosis of COA hinges on blood flow quantification, proper diagnostic methods for COA are still lacking because fluid-dynamics methods that can be used for accurate flow quantification are not well developed yet. Most importantly, COA and the heart interact with each other and because the heart resides in a complex vascular network that imposes boundary conditions on its function, accurate diagnosis relies on quantifications of the global hemodynamics (heart-function metrics) as well as the local hemodynamics (detailed information of the blood flow dynamics in COA). In this study, to enable the development of new non-invasive methods that can quantify local and global hemodynamics for COA diagnosis, we developed an innovative fast computational-mechanics and imaging-based framework that uses Lattice Boltzmann method and lumped-parameter modeling that only need routine non-invasive clinical patient data. We used clinical data of patients with COA to validate the proposed framework and to demonstrate its abilities to provide new diagnostic analyses not possible with conventional diagnostic methods. We validated this framework against clinical cardiac catheterization data, calculations using the conventional finite-volume method and clinical Doppler echocardiographic measurements. The diagnostic information, that the framework can provide, is vitally needed to improve clinical outcomes, to assess patient risk and to plan treatment.

A Re-Engineered Software Interface and Workflow for the Open-Source SimVascular Cardiovascular Modeling Package

Patient-specific simulation plays an important role in cardiovascular disease research, diagnosis, surgical planning and medical device design, as well as education in cardiovascular biomechanics. simvascular is an open-source software package encompassing an entire cardiovascular modeling and simulation pipeline from image segmentation, three-dimensional (3D) solid modeling, and mesh generation, to patient-specific simulation and analysis. SimVascular is widely used for cardiovascular basic science and clinical research as well as education, following increased adoption by users and development of a GATEWAY web portal to facilitate educational access. Initial efforts of the project focused on replacing commercial packages with open-source alternatives and adding increased functionality for multiscale modeling, fluid-structure interaction (FSI), and solid modeling operations. In this paper, we introduce a major SimVascular (SV) release that includes a new graphical user interface (GUI) designed to improve user experience. Additional improvements include enhanced data/project management, interactive tools to facilitate user interaction, new boundary condition (BC) functionality, plug-in mechanism to increase modularity, a new 3D segmentation tool, and new computer-aided design (CAD)-based solid modeling capabilities. Here, we focus on major changes to the software platform and outline features added in this new release. We also briefly describe our recent experiences using SimVascular in the classroom for bioengineering education.

Characterization and estimation of turbulence-related wall shear stress in patient-specific pulsatile blood flow

Disturbed, turbulent-like blood flow promotes chaotic wall shear stress (WSS) environments, impairing essential endothelial functions and increasing the susceptibility and progression of vascular diseases. These flow characteristics are today frequently detected at various anatomical, lesion and intervention-related sites, while their role as a pathological determinant is less understood. To present-day, numerous WSS-based descriptors have been proposed to characterize the spatiotemporal nature of the WSS disturbances, however, without differentiation between physiological laminar oscillations and turbulence-related WSS (tWSS) fluctuations. Also, much attention has been focused on magnetic resonance (MR) WSS estimations, so far with limited success; promoting the need of a near-wall surrogate marker. In this study, a new approach is explored to characterize the tWSS, by taking advantage of the tensor characteristics of the fluctuating WSS correlations, providing both a magnitude and an anisotropy measure of the disturbances. These parameters were studied in two patient-specific coarctation models (sever and mild), using large eddy simulations, and correlated against near-wall reciprocal Reynolds stress parameters. Collectively, results showed distinct regions of differing tWSS characteristics, features which were sensitive to changes in flow conditions. Generally, the post-stenotic tWSS was governed by near axisymmetric fluctuations, findings that where not consistent with conventional WSS disturbance predictors. At the 2-3 mm wall-offset range, a strong linear correlation was found between tWSS magnitude and near-wall turbulence kinetic energy (TKE), in contrast to the anisotropy indices, suggesting that MR-measured TKE can be used to assess elevated tWSS regions while tWSS anisotropy estimates request well-resolved simulation methods.

Review of Biomedical Applications of Contactless Imaging of Neonates Using Infrared Thermography and Beyond

The sick preterm infant monitoring is an intriguing job that medical staff in Neonatal Intensive Care Units (NICU) must deal with on a daily basis. As a standards monitoring procedure, preterm infants are monitored via sensors and electrodes that are firmly attached to their fragile and delicate skin and connected to processing monitors. However, an alternative exists in contactless imaging to record such physiological signals (we call it as Physio-Markers), detecting superficial changes and internal structures activities which can be used independently of, or aligned with, conventional monitors. Countless advantages can be gained from unobtrusive monitoring not limited to: (1) quick data generation; (2) decreasing physical and direct contact with skin, which reduces skin breakdown and minimizes risk of infection; and (3) reduction of electrodes and probes connected to clinical monitors and attached to the skin, which allows greater body surface-area for better care. This review is an attempt to build a solid ground for and to provide a clear perspective of the potential clinical applications of technologies inside NICUs that use contactless imaging modalities such as Visible Light Imaging (VLI), Near Infrared Spectroscopy (NIRS), and Infrared Thermography (IRT).

Age-Related Vascular Changes Affect Turbulence in Aortic Blood Flow

Turbulent blood flow is implicated in the pathogenesis of several aortic diseases but the extent and degree of turbulent blood flow in the normal aorta is unknown. We aimed to quantify the extent and degree of turbulece in the normal aorta and to assess whether age impacts the degree of turbulence. 22 young normal males (23.7 ± 3.0 y.o.) and 20 old normal males (70.9 ± 3.5 y.o.) were examined using four dimensional flow magnetic resonance imaging (4D Flow MRI) to quantify the turbulent kinetic energy (TKE), a measure of the intensity of turbulence, in the aorta. All healthy subjects developed turbulent flow in the aorta, with total TKE of 3–19 mJ. The overall degree of turbulence in the entire aorta was similar between the groups, although the old subjects had about 73% more total TKE in the ascending aorta compared to the young subjects (young = 3.7 ± 1.8 mJ, old = 6.4 ± 2.4 mJ, p < 0.001). This increase in ascending aorta TKE in old subjects was associated with age-related dilation of the ascending aorta which increases the volume available for turbulence development. Conversely, age-related dilation of the descending and abdominal aorta decreased the average flow velocity and suppressed the development of turbulence. In conclusion, turbulent blood flow develops in the aorta of normal subjects and is impacted by age-related geometric changes. Non-invasive assessment enables the determination of normal levels of turbulent flow in the aorta which is a prerequisite for understanding the role of turbulence in the pathophysiology of cardiovascular disease.

Use of the FDA nozzle model to illustrate validation techniques in computational fluid dynamics (CFD) simulations

A "credible" computational fluid dynamics (CFD) model has the potential to provide a meaningful evaluation of safety in medical devices. One major challenge in establishing "model credibility" is to determine the required degree of similarity between the model and experimental results for the model to be considered sufficiently validated. This study proposes a "threshold-based" validation approach that provides a well-defined acceptance criteria, which is a function of how close the simulation and experimental results are to the safety threshold, for establishing the model validity. The validation criteria developed following the threshold approach is not only a function of Comparison Error, E (which is the difference between experiments and simulations) but also takes in to account the risk to patient safety because of E. The method is applicable for scenarios in which a safety threshold can be clearly defined (e.g., the viscous shear-stress threshold for hemolysis in blood contacting devices). The applicability of the new validation approach was tested on the FDA nozzle geometry. The context of use (COU) was to evaluate if the instantaneous viscous shear stress in the nozzle geometry at Reynolds numbers (Re) of 3500 and 6500 was below the commonly accepted threshold for hemolysis. The CFD results ("S") of velocity and viscous shear stress were compared with inter-laboratory experimental measurements ("D"). The uncertainties in the CFD and experimental results due to input parameter uncertainties were quantified following the ASME V&V 20 standard. The CFD models for both Re = 3500 and 6500 could not be sufficiently validated by performing a direct comparison between CFD and experimental results using the Student's t-test. However, following the threshold-based approach, a Student's t-test comparing |S-D| and |Threshold-S| showed that relative to the threshold, the CFD and experimental datasets for Re = 3500 were statistically similar and the model could be considered sufficiently validated for the COU. However, for Re = 6500, at certain locations where the shear stress is close the hemolysis threshold, the CFD model could not be considered sufficiently validated for the COU. Our analysis showed that the model could be sufficiently validated either by reducing the uncertainties in experiments, simulations, and the threshold or by increasing the sample size for the experiments and simulations. The threshold approach can be applied to all types of computational models and provides an objective way of determining model credibility and for evaluating medical devices.

Estimating the irreversible pressure drop across a stenosis by quantifying turbulence production using 4D Flow MRI

The pressure drop across a stenotic vessel is an important parameter in medicine, providing a commonly used and intuitive metric for evaluating the severity of the stenosis. However, non-invasive estimation of the pressure drop under pathological conditions has remained difficult. This study demonstrates a novel method to quantify the irreversible pressure drop across a stenosis using 4D Flow MRI by calculating the total turbulence production of the flow. Simulation MRI acquisitions showed that the energy lost to turbulence production can be accurately quantified with 4D Flow MRI within a range of practical spatial resolutions (1-3 mm; regression slope = 0.91, R2 = 0.96). The quantification of the turbulence production was not substantially influenced by the signal-to-noise ratio (SNR), resulting in less than 2% mean bias at SNR > 10. Pressure drop estimation based on turbulence production robustly predicted the irreversible pressure drop, regardless of the stenosis severity and post-stenosis dilatation (regression slope = 0.956, R2 = 0.96). In vitro validation of the technique in a 75% stenosis channel confirmed that pressure drop prediction based on the turbulence production agreed with the measured pressure drop (regression slope = 1.15, R2 = 0.999, Bland-Altman agreement = 0.75 ± 3.93 mmHg).

SimVascular: An Open Source Pipeline for Cardiovascular Simulation

Patient-specific cardiovascular simulation has become a paradigm in cardiovascular research and is emerging as a powerful tool in basic, translational and clinical research. In this paper we discuss the recent development of a fully open-source SimVascular software package, which provides a complete pipeline from medical image data segmentation to patient-specific blood flow simulation and analysis. This package serves as a research tool for cardiovascular modeling and simulation, and has contributed to numerous advances in personalized medicine, surgical planning and medical device design. The SimVascular software has recently been refactored and expanded to enhance functionality, usability, efficiency and accuracy of image-based patient-specific modeling tools. Moreover, SimVascular previously required several licensed components that hindered new user adoption and code management and our recent developments have replaced these commercial components to create a fully open source pipeline. These developments foster advances in cardiovascular modeling research, increased collaboration, standardization of methods, and a growing developer community.

Assessment of turbulent flow effects on the vessel wall using four-dimensional flow MRI

PURPOSE

To explore the use of MR-estimated turbulence quantities for the assessment of turbulent flow effects on the vessel wall.

METHODS

Numerical velocity data for two patient-derived models was obtained using computational fluid dynamics (CFD) for two physiological flow rates. The four-dimensional (4D) Flow MRI measurements were simulated at three different spatial resolutions and used to investigate the estimation of turbulent wall shear stress (tWSS) using the intravoxel standard deviation (IVSD) of velocity and turbulent kinetic energy (TKE) estimated near the vessel wall.

RESULTS

Accurate estimation of tWSS using the IVSD is limited by the spatial resolution achievable with 4D Flow MRI. TKE, estimated near the wall, has a strong linear relationship to the tWSS (mean R²  = 0.84). Near-wall TKE estimates from MR simulations have good agreement to CFD-derived ground truth (mean R²  = 0.90). Maps of near-wall TKE have strong visual correspondence to tWSS.

CONCLUSION

Near-wall estimation of TKE permits assessment of relative maps of tWSS, but direct estimation of tWSS is challenging due to limitations in spatial resolution. Assessment of tWSS and near-wall TKE may open new avenues for analysis of different pathologies. Magn Reson Med 77:2310-2319, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Transport physics and biorheology in the setting of hemostasis and thrombosis

The biophysics of blood flow can dictate the function of molecules and cells in the vasculature with consequent effects on hemostasis, thrombosis, embolism, and fibrinolysis. Flow and transport dynamics are distinct for (i) hemostasis vs. thrombosis and (ii) venous vs. arterial episodes. Intraclot transport changes dramatically the moment hemostasis is achieved or the moment a thrombus becomes fully occlusive. With platelet concentrations that are 50- to 200-fold greater than platelet-rich plasma, clots formed under flow have a different composition and structure compared with blood clotted statically in a tube. The platelet-rich, core/shell architecture is a prominent feature of self-limiting hemostatic clots formed under flow. Importantly, a critical threshold concentration of surface tissue factor is required for fibrin generation under flow. Once initiated by wall-derived tissue factor, thrombin generation and its spatial propagation within a clot can be modulated by γ'-fibrinogen incorporated into fibrin, engageability of activated factor (FIXa)/activated FVIIIa tenase within the clot, platelet-derived polyphosphate, transclot permeation, and reduction of porosity via platelet retraction. Fibrin imparts tremendous strength to a thrombus to resist embolism up to wall shear stresses of 2400 dyne cm(-2) . Extreme flows, as found in severe vessel stenosis or in mechanical assist devices, can cause von Willebrand factor self-association into massive fibers along with shear-induced platelet activation. Pathological von Willebrand factor fibers are A Disintegrin And Metalloprotease with ThromboSpondin-1 domain 13 resistant but are a substrate for fibrin generation due to FXIIa capture. Recently, microfluidic technologies have enhanced the ability to interrogate blood in the context of stenotic flows, acquired von Willebrand disease, hemophilia, traumatic bleeding, and drug action.

Effects of Variations of Flow and Heart Rate on Intra-Aneurysmal Hemodynamics in a Ruptured Internal Carotid Artery Aneurysm During Exercise

BACKGROUND

Hemodynamics is thought to play an important role in the mechanisms responsible for initiation, growth, and rupture of intracranial aneurysms. Computational fluid dynamic (CFD) analysis is used to assess intra-aneurysmal hemodynamics.

OBJECTIVES

This study aimed to investigate the effects of variations in heart rate and internal carotid artery (ICA) flow rate on intra-aneurysmal hemodynamics, in an ICA aneurysm, by using computational fluid dynamics.

PATIENTS AND METHODS

Computed tomography angiography (CTA) was performed in a 55 years old female case, with a saccular ICA aneurysm, to create a patient-specific geometrical anatomic model of the aneurysm. The intra-aneurysmal hemodynamic environments for three states with different flow and heart rates were analyzed using patient-specific image-based CFD modeling.

RESULTS

Results showed significant changes for the three simulated states. For a proportion of the states examined, results were counterintuitive. Systolic and time-averaged wall shear stress and pressure on the aneurysm wall showed a proportional evolution with the mainstream flow rate.

CONCLUSION

Results reinforced the pivotal role of vascular geometry, with respect to hemodynamics, together with the importance of performing patient-specific CFD analyses, through which the effect of different blood flow conditions on the aneurysm hemodynamics could be evaluated.

Turbulent Kinetic Energy Measurement Using Phase Contrast MRI for Estimating the Post-Stenotic Pressure Drop: In Vitro Validation and Clinical Application

BACKGROUND

Although the measurement of turbulence kinetic energy (TKE) by using magnetic resonance imaging (MRI) has been introduced as an alternative index for quantifying energy loss through the cardiac valve, experimental verification and clinical application of this parameter are still required.

OBJECTIVES

The goal of this study is to verify MRI measurements of TKE by using a phantom stenosis with particle image velocimetry (PIV) as the reference standard. In addition, the feasibility of measuring TKE with MRI is explored.

METHODS

MRI measurements of TKE through a phantom stenosis was performed by using clinical 3T MRI scanner. The MRI measurements were verified experimentally by using PIV as the reference standard. In vivo application of MRI-driven TKE was explored in seven patients with aortic valve disease and one healthy volunteer. Transvalvular gradients measured by MRI and echocardiography were compared.

RESULTS

MRI and PIV measurements of TKE are consistent for turbulent flow (0.666 < R2 < 0.738) with a mean difference of -11.13 J/m3 (SD = 4.34 J/m3). Results of MRI and PIV measurements differ by 2.76 ± 0.82 cm/s (velocity) and -11.13 ± 4.34 J/m3 (TKE) for turbulent flow (Re > 400). The turbulence pressure drop correlates strongly with total TKE (R2 = 0.986). However, in vivo measurements of TKE are not consistent with the transvalvular pressure gradient estimated by echocardiography.

CONCLUSIONS

These results suggest that TKE measurement via MRI may provide a potential benefit as an energy-loss index to characterize blood flow through the aortic valve. However, further clinical studies are necessary to reach definitive conclusions regarding this technique.

4D flow cardiovascular magnetic resonance consensus statement

Pulsatile blood flow through the cavities of the heart and great vessels is time-varying and multidirectional. Access to all regions, phases and directions of cardiovascular flows has formerly been limited. Four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) has enabled more comprehensive access to such flows, with typical spatial resolution of 1.5×1.5×1.5 - 3×3×3 mm(3), typical temporal resolution of 30-40 ms, and acquisition times in the order of 5 to 25 min. This consensus paper is the work of physicists, physicians and biomedical engineers, active in the development and implementation of 4D Flow CMR, who have repeatedly met to share experience and ideas. The paper aims to assist understanding of acquisition and analysis methods, and their potential clinical applications with a focus on the heart and greater vessels. We describe that 4D Flow CMR can be clinically advantageous because placement of a single acquisition volume is straightforward and enables flow through any plane across it to be calculated retrospectively and with good accuracy. We also specify research and development goals that have yet to be satisfactorily achieved. Derived flow parameters, generally needing further development or validation for clinical use, include measurements of wall shear stress, pressure difference, turbulent kinetic energy, and intracardiac flow components. The dependence of measurement accuracy on acquisition parameters is considered, as are the uses of different visualization strategies for appropriate representation of time-varying multidirectional flow fields. Finally, we offer suggestions for more consistent, user-friendly implementation of 4D Flow CMR acquisition and data handling with a view to multicenter studies and more widespread adoption of the approach in routine clinical investigations.

Quantitative Assessment of Turbulence and Flow Eccentricity in an Aortic Coarctation: Impact of Virtual Interventions

Turbulence and flow eccentricity can be measured by magnetic resonance imaging (MRI) and may play an important role in the pathogenesis of numerous cardiovascular diseases. In the present study, we propose quantitative techniques to assess turbulent kinetic energy (TKE) and flow eccentricity that could assist in the evaluation and treatment of stenotic severities. These hemodynamic parameters were studied in a pre-treated aortic coarctation (CoA) and after several virtual interventions using computational fluid dynamics (CFD), to demonstrate the effect of different dilatation options on the flow field. Patient-specific geometry and flow conditions were derived from MRI data. The unsteady pulsatile flow was resolved by large eddy simulation including non-Newtonian blood rheology. Results showed an inverse asymptotic relationship between the total amount of TKE and degree of dilatation of the stenosis, where turbulent flow proximal the constriction limits the possible improvement by treating the CoA alone. Spatiotemporal maps of TKE and flow eccentricity could be linked to the characteristics of the jet, where improved flow conditions were favored by an eccentric dilatation of the CoA. By including these flow markers into a combined MRI-CFD intervention framework, CoA therapy has not only the possibility to produce predictions via simulation, but can also be validated pre- and immediate post treatment, as well as during follow-up studies.

MRI-based computational fluid dynamics for diagnosis and treatment prediction: clinical validation study in patients with coarctation of aorta

PURPOSE

To reduce the need for diagnostic catheterization and optimize treatment in a variety of congenital heart diseases, magnetic resonance imaging (MRI)-based computational fluid dynamics (CFD) is proposed. However, data about the accuracy of CFD in a clinical context are still sparse. To fill this gap, this study compares MRI-based CFD to catheterization in the coarctation of aorta (CoA) setting.

MATERIALS AND METHODS

Thirteen patients with CoA were investigated by routine MRI prior to catheterization. 3D whole-heart MRI was used to reconstruct geometries and 4D flow-sensitive phase-contrast MRI was used to acquire flows. Peak systolic flows were simulated using the program FLUENT.

RESULTS

Peak systolic pressure drops in CoA measured by catheterization and CFD correlated significantly for both pre- and posttreatment measurements (pre: r = 0.98, p = 0.00; post: r = 0.87, p = 0.00). The pretreatment bias was -0.5 ± 3.33 mmHg (95% confidence interval -2.55 to 1.47 mmHg). CFD predicted a reduction of the peak systolic pressure drop after treatment that ranged from 17.6 ± 5.56 mmHg to 6.7 ± 5.58 mmHg. The posttreatment bias was 3.0 ± 2.91 mmHg (95% CI -1.74 to 5.43 mmHg).

CONCLUSION

Peak systolic pressure drops can be reliably calculated using MRI-based CFD in a clinical setting. Therefore, CFD might be an attractive noninvasive alternative to diagnostic catheterization.

Variability of computational fluid dynamics solutions for pressure and flow in a giant aneurysm: the ASME 2012 Summer Bioengineering Conference CFD Challenge

Stimulated by a recent controversy regarding pressure drops predicted in a giant aneurysm with a proximal stenosis, the present study sought to assess variability in the prediction of pressures and flow by a wide variety of research groups. In phase I, lumen geometry, flow rates, and fluid properties were specified, leaving each research group to choose their solver, discretization, and solution strategies. Variability was assessed by having each group interpolate their results onto a standardized mesh and centerline. For phase II, a physical model of the geometry was constructed, from which pressure and flow rates were measured. Groups repeated their simulations using a geometry reconstructed from a micro-computed tomography (CT) scan of the physical model with the measured flow rates and fluid properties. Phase I results from 25 groups demonstrated remarkable consistency in the pressure patterns, with the majority predicting peak systolic pressure drops within 8% of each other. Aneurysm sac flow patterns were more variable with only a few groups reporting peak systolic flow instabilities owing to their use of high temporal resolutions. Variability for phase II was comparable, and the median predicted pressure drops were within a few millimeters of mercury of the measured values but only after accounting for submillimeter errors in the reconstruction of the life-sized flow model from micro-CT. In summary, pressure can be predicted with consistency by CFD across a wide range of solvers and solution strategies, but this may not hold true for specific flow patterns or derived quantities. Future challenges are needed and should focus on hemodynamic quantities thought to be of clinical interest.

The impact of MRI-based inflow for the hemodynamic evaluation of aortic coarctation

Aortic coarctation (CoA) accounting for 3-11% of congenital heart disease can be successfully treated. Long-term results, however, have revealed decreased life expectancy associated with abnormal hemodynamics. Accordingly, an assessment of hemodynamics is the key factor in treatment decisions and successful long-term results. In this study, 3D angiography whole heart (3DWH) and 4D phase-contrast magnetic resonance imaging (MRI) data were acquired. Geometries of the thoracic aorta with CoAs were reconstructed using ZIB-Amira software. X-ray angiograms were used to evaluate the post-treatment geometry. Computational fluid dynamics models in three patients were created to simulate pre- and post-treatment situations using the FLUENT program. The aim of the study was to investigate the impact of the inlet velocity profile (plug vs. MRI-based) with a focus on the peak systole pressure gradient and wall shear stress (WSS). Results show that helical flow at the aorta inlet can significantly affect the assessment of pressure drop and WSS. Simplified plug inlet velocity profiles significantly (p < 0.05) overestimate the pressure drop in pre- and post-treatment geometries and significantly (p < 0.05) underestimate surface-averaged WSS. We conclude that the use of the physiologically correct but time-expensive 4D MRI-based in vivo velocity profile in CFD studies may be an important step towards a patient-specific analysis of CoA hemodynamics.

Magnetic resonance measurement of turbulent kinetic energy for the estimation of irreversible pressure loss in aortic stenosis

OBJECTIVES

The authors sought to measure the turbulent kinetic energy (TKE) in the ascending aorta of patients with aortic stenosis and to assess its relationship to irreversible pressure loss.

BACKGROUND

Irreversible pressure loss caused by energy dissipation in post-stenotic flow is an important determinant of the hemodynamic significance of aortic stenosis. The simplified Bernoulli equation used to estimate pressure gradients often misclassifies the ventricular overload caused by aortic stenosis. The current gold standard for estimation of irreversible pressure loss is catheterization, but this method is rarely used due to its invasiveness. Post-stenotic pressure loss is largely caused by dissipation of turbulent kinetic energy into heat. Recent developments in magnetic resonance flow imaging permit noninvasive estimation of TKE.

METHODS

The study was approved by the local ethics review board and all subjects gave written informed consent. Three-dimensional cine magnetic resonance flow imaging was used to measure TKE in 18 subjects (4 normal volunteers, 14 patients with aortic stenosis with and without dilation). For each subject, the peak total TKE in the ascending aorta was compared with a pressure loss index. The pressure loss index was based on a previously validated theory relating pressure loss to measures obtainable by echocardiography.

RESULTS

The total TKE did not appear to be related to global flow patterns visualized based on magnetic resonance-measured velocity fields. The TKE was significantly higher in patients with aortic stenosis than in normal volunteers (p < 0.001). The peak total TKE in the ascending aorta was strongly correlated to index pressure loss (R(2) = 0.91).

CONCLUSIONS

Peak total TKE in the ascending aorta correlated strongly with irreversible pressure loss estimated by a well-established method. Direct measurement of TKE by magnetic resonance flow imaging may, with further validation, be used to estimate irreversible pressure loss in aortic stenosis.

Size-dependent predilections of cardiogenic embolic transport

While it is intuitively clear that aortic anatomy and embolus size could be important determinants for cardiogenic embolic stroke risk and stroke location, few data exist confirming or characterizing this hypothesis. The objective of this study is to use medical imaging and computational modeling to better understand if aortic anatomy and embolus size influence predilections for cardiogenic embolic transport and right vs. left hemisphere propensity. Anatomically accurate models of the human aorta and branch arteries to the head were reconstructed from computed tomography (CT) angiography of 10 patients. Blood flow was modeled by the Navier-Stokes equations using a well-validated flow solver with physiologic inflow and boundary conditions. Embolic particulate was released from the aortic root and tracked through the common carotid and vertebral arteries for a range of particle sizes. Cardiogenic emboli reaching the carotid and vertebral arteries appeared to have a strong size-destination relationship that varied markedly from expectations based on blood distribution. Observed trends were robust to modeling parameters. A patient's aortic anatomy appeared to significantly influence the probability a cardiogenic particle becomes embolic to the head. Right hemisphere propensity appeared dominant for cardiogenic emboli, which has been confirmed clinically. The predilections discovered through this modeling could represent an important mechanism underlying cardiogenic embolic stroke etiology.

Cardiovascular magnetic resonance: deeper insights through bioengineering

Heart disease is the main cause of morbidity and mortality worldwide, with coronary artery disease, diabetes, and obesity being major contributing factors. Cardiovascular magnetic resonance (CMR) can provide a wealth of quantitative information on the performance of the heart, without risk to the patient. Quantitative analyses of these data can substantially augment the diagnostic quality of CMR examinations and can lead to more effective characterization of disease and quantification of treatment benefit. This review provides an overview of the current state of the art in CMR with particular regard to the quantification of motion, both microscopic and macroscopic, and the application of bioengineering analysis for the evaluation of cardiac mechanics. We discuss the current clinical practice and the likely advances in the next 5-10 years, as well as the ways in which clinical examinations can be augmented by bioengineering analysis of strain, compliance, and stress.

Characterization of the transport topology in patient-specific abdominal aortic aneurysm models

Abdominal aortic aneurysm (AAA) is characterized by disturbed blood flow patterns that are hypothesized to contribute to disease progression. The transport topology in six patient-specific abdominal aortic aneurysms was studied. Velocity data were obtained by image-based computational fluid dynamics modeling, with magnetic resonance imaging providing the necessary simulation parameters. Finite-time Lyapunov exponent (FTLE) fields were computed from the velocity data, and used to identify Lagrangian coherent structures (LCS). The combination of FTLE fields and LCS was used to characterize topological flow features such as separation zones, vortex transport, mixing regions, and flow impingement. These measures offer a novel perspective into AAA flow. It was observed that all aneurysms exhibited coherent vortex formation at the proximal segment of the aneurysm. The evolution of the systolic vortex strongly influences the flow topology in the aneurysm. It was difficult to predict the vortex dynamics from the aneurysm morphology, motivating the application of image-based flow modeling.

Bayesian multipoint velocity encoding for concurrent flow and turbulence mapping

An approach to efficiently measure three-dimensional velocity vector fields and turbulent kinetic energy of blood flow is presented. Multipoint phase-contrast imaging is used in combination with Bayesian analysis to map both mean and fluctuating velocities over a large dynamic range and for practically relevant signal-to-noise ratios. It is demonstrated that the approach permits significant spatiotemporal undersampling to allow for clinically acceptable scan times. Using numerical simulations and in vitro measurements in aortic valve phantoms, it is shown that for given scan time, Bayesian multipoint velocity encoding provides consistently lower errors of velocity and turbulent kinetic energy over a larger dynamic range relative to previous methods. In vitro, significant differences in both peak velocity and turbulent kinetic energy between the aortic CoreValve (150 cm/s, 293 J/m3) and the St. Jude Medical mechanical valve (120 cm/s, 149 J/m3) were found. Comparison of peak turbulent kinetic energy measured in a patient with aortic stenosis (950 J/m3) and in a patient with an implanted aortic CoreValve (540 J/m3) revealed considerable differences relative to the values detected in healthy subjects (149±12 J/m3) indicating the potential of the method to provide a comprehensive hemodynamic assessment of valve performance in vivo.