Computational fluid dynamics with application of different theoretical flow models for the evaluation of coronary artery stenosis on CT angiography: comparison with invasive fractional flow reserve

Computational fluid dynamics modeling of coronary artery blood flow using OpenFOAM: Validation with the food and drug administration benchmark nozzle model

Cardiovascular disease (CVD), a global health concern, particularly coronary artery disease (CAD), poses a significant threat to well-being. Seeking safer and cost-effective diagnostic alternatives to invasive coronary angiography, noninvasive coronary computed tomography angiography (CCTA) gains prominence. This study employed OpenFOAM, an open-source Computational Fluid Dynamics (CFD) software, to analyze hemodynamic parameters in coronary arteries with serial stenoses. Patient-specific three-dimensional (3D) models from CCTA images offer insights into hemodynamic changes. OpenFOAM breaks away from traditional commercial software, validated against the FDA benchmark nozzle model for reliability. Applying this refined methodology to seventeen coronary arteries across nine patients, the study evaluates parameters like fractional flow reserve computed tomography simulation (FFRCTS), fluid velocity, and wall shear stress (WSS) over time. Findings include FFRCTS values exceeding 0.8 for grade 0 stenosis and falling below 0.5 for grade 5 stenosis. Central velocity remains nearly constant for grade 1 stenosis but increases 3.4-fold for grade 5 stenosis. This research innovates by utilizing OpenFOAM, departing from previous reliance on commercial software. Combining qualitative stenosis grading with quantitative FFRCTS and velocity measurements offers a more comprehensive assessment of coronary artery conditions. The study introduces 3D renderings of wall shear stress distribution across stenosis grades, providing an intuitive visualization of hemodynamic changes for valuable insights into coronary stenosis diagnosis.

A novel physics-based model for fast computation of blood flow in coronary arteries

Blood flow and pressure calculated using the currently available methods have shown the potential to predict the progression of pathology, guide treatment strategies and help with postoperative recovery. However, the conspicuous disadvantage of these methods might be the time-consuming nature due to the simulation of virtual interventional treatment. The purpose of this study is to propose a fast novel physics-based model, called FAST, for the prediction of blood flow and pressure. More specifically, blood flow in a vessel is discretized into a number of micro-flow elements along the centerline of the artery, so that when using the equation of viscous fluid motion, the complex blood flow in the artery is simplified into a one-dimensional (1D) steady-state flow. We demonstrate that this method can compute the fractional flow reserve (FFR) derived from coronary computed tomography angiography (CCTA). 345 patients with 402 lesions are used to evaluate the feasibility of the FAST simulation through a comparison with three-dimensional (3D) computational fluid dynamics (CFD) simulation. Invasive FFR is also introduced to validate the diagnostic performance of the FAST method as a reference standard. The performance of the FAST method is comparable with the 3D CFD method. Compared with invasive FFR, the accuracy, sensitivity and specificity of FAST is 88.6%, 83.2% and 91.3%, respectively. The AUC of FFR_FAST is 0.906. This demonstrates that the FAST algorithm and 3D CFD method show high consistency in predicting steady-state blood flow and pressure. Meanwhile, the FAST method also shows the potential in detecting lesion-specific ischemia.

Investigation on aortic hemodynamics based on physics-informed neural network

Pressure in arteries is difficult to measure non-invasively. Although computational fluid dynamics (CFD) provides high-precision numerical solutions according to the basic physical equations of fluid mechanics, it relies on precise boundary conditions and complex preprocessing, which limits its real-time application. Machine learning algorithms have wide applications in hemodynamic research due to their powerful learning ability and fast calculation speed. Therefore, we proposed a novel method for pressure estimation based on physics-informed neural network (PINN). An ideal aortic arch model was established according to the geometric parameters from human aorta, and we performed CFD simulation with two-way fluid-solid coupling. The simulation results, including the space-time coordinates, the velocity and pressure field, were obtained as the dataset for the training and validation of PINN. Nondimensional Navier-Stokes equations and continuity equation were employed for the loss function of PINN, to calculate the velocity and relative pressure field. Post-processing was proposed to fit the absolute pressure of the aorta according to the linear relationship between relative pressure, elastic modulus and displacement of the vessel wall. Additionally, we explored the sensitivity of the PINN to the vascular elasticity, blood viscosity and blood velocity. The velocity and pressure field predicted by PINN yielded good consistency with the simulated values. In the interested region of the aorta, the relative errors of maximum and average absolute pressure were 7.33% and 5.71%, respectively. The relative pressure field was found most sensitive to blood velocity, followed by blood viscosity and vascular elasticity. This study has proposed a method for intra-vascular pressure estimation, which has potential significance in the diagnosis of cardiovascular diseases.

Hemodynamic and fluid flow analysis of a cerebral aneurysm: a CFD simulation

In this study, we investigate the hemodynamics parameters and their impact on the aneurysm rupture. The simulations are performed on an ideal (benchmark) and realistic model for the intracranial aneurysm that appears at the anterior communicating artery. The realistic geometry was reconstructed from patient-specific cerebral arteries. The computational fluid dynamics simulations are utilized to investigate the hemodynamic parameters such as flow recirculation, wall shear stress, and wall pressure. The boundary conditions are measured from the patient using ultrasonography. The solution of the governing equations is obtained by using the ANSYS-FLUENT 19.2 package. The CFD results indicate that the flow recirculation appears in the aneurysms zone. The effect of the flow recirculation on the bulge hemodynamics wall parameters is discussed to identify the rupture zone.

Blood flow CFD simulation on a cerebral artery of a stroke patient

Abstract

The purpose of this paper is to conduct a numerical simulation of the stroke patient's cerebral arteries and investigate the flow parameters due to the presence of stenosis. The computational fluid dynamics (CFD) simulations are based on simplified and realistic cerebral artery models. The seven simplified models (benchmarks) include straight cylindrical vessels with idealized stenosis with variable d/D (0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1). The realistic model of the cerebral artery is based on magnetic resonance imaging (MRI) for patient-specific cerebral arteries. The simulation for the realistic model of the cerebral artery is performed at boundary conditions measured by ultrasonography of the input and the output flow profiles (velocity and pressure). The obtained CFD results of the benchmarks are validated with actual data from the literature. Furthermore, a previous vascular contraction is assumed to be exist and the effect of this contraction area ratio on the blood flow regime is discussed and highlighted. Furthermore, CFD results show that a certain vascular contraction area critically affects the blood flow which shows increasing the wall shear stress WSS at the stenosis site. An increase in the blood velocity and vortex appears after the contraction zone, this lead to vessel occlusion and strokes.

Article highlights

The effects of plaque morphological characteristics on the post-stenotic flow in left main coronary artery bifurcation

Local post-stenotic hemodynamics has critical influence in the atherosclerotic plaque progression occurring in susceptible arterial sites, in particular the left main coronary artery (LMCA) bifurcation. Understanding the effects of plaque morphological characteristics: stenosis severity (SS), eccentricity index (EI) and lesion length (LL) on the post-stenotic flow behavior can significantly improve treatment planning. In order to investigate these effects, we have employed computational fluid dynamics (CFD) simulations in twenty computer-generated and five patient-specific LMCA models and the hemodynamic parameters: velocity, pressure (P), wall pressure gradient (WPG), wall shear stress (WSS), time averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT) and helicity intensity (h₂) were analyzed. Our results revealed that the effect of stenosis eccentricity varied significantly for different values of stenosis severity and lesion length. Regions with low WSS, low TAWSS and high RRT were more prominent in models having higher stenosis severity. For smaller lesion length, at low and moderate stenosis severity, surface area with low TAWSS and high RRT decreased with increasing eccentricity index, whereas for high stenosis severity models, low TAWSS region and average RRT values increased with eccentricity. However, for models with longer lesion length, regions with high OSI and RRT overall increased gradually with eccentricity. The helicity intensity (h₂) of all models remained very low except at the most eccentric model with longer lesion length. The presence of very high helical flow in this model suggests the possibility of atheroprotective flow. It can be concluded that all plaque morphological characteristics covered under this investigation play an important role in plaque progression.

3D reconstruction of coronary artery bifurcations from coronary angiography and optical coherence tomography: feasibility, validation, and reproducibility

The three-dimensional (3D) representation of the bifurcation anatomy and disease burden is essential for better understanding of the anatomical complexity of bifurcation disease and planning of stenting strategies. We propose a novel methodology for 3D reconstruction of coronary artery bifurcations based on the integration of angiography, which provides the backbone of the bifurcation, with optical coherence tomography (OCT), which provides the vessel shape. Our methodology introduces several technical novelties to tackle the OCT frame misalignment, correct positioning of the OCT frames at the carina, lumen surface reconstruction, and merging of bifurcation lumens. The accuracy and reproducibility of the methodology were tested in n = 5 patient-specific silicone bifurcations compared to contrast-enhanced micro-computed tomography (µCT), which was used as reference. The feasibility and time-efficiency of the method were explored in n = 7 diseased patient bifurcations of varying anatomical complexity. The OCT-based reconstructed bifurcation models were found to have remarkably high agreement compared to the µCT reference models, yielding r² values between 0.91 and 0.98 for the normalized lumen areas, and mean differences of 0.005 for lumen shape and 0.004 degrees for bifurcation angles. Likewise, the reproducibility of our methodology was remarkably high. Our methodology successfully reconstructed all the patient bifurcations yielding favorable processing times (average lumen reconstruction time < 60 min). Overall, our method is an easily applicable, time-efficient, and user-friendly tool that allows accurate and reproducible 3D reconstruction of coronary bifurcations. Our technique can be used in the clinical setting to provide information about the bifurcation anatomy and plaque burden, thereby enabling planning, education, and decision making on bifurcation stenting.

Integrating Patient-Specific Electrocardiogram Signals and Image-Based Computational Fluid Dynamics Method to Analyze Coronary Blood Flow in Patients during Cardiac Arrhythmias

Purpose

The aim of this study was to use the computational fluid dynamics (CFD) method, patient-specific electrocardiogram (ECG) signals, and computed tomography three-dimensional image reconstruction technique to investigate the blood flow in coronary arteries during cardiac arrhythmia.

Methods

Two patients with premature ventricular contraction-type cardiac arrhythmia and one with atrial fibrillation-type cardiac arrhythmia were investigated. The inlet velocity of the coronary artery in simulation was applied with the measured velocity profile of the left ventricular outflow tract (LVOT) from the Doppler echocardiography. The measured patient central aortic blood pressure waveform was employed for the coronary artery outlet in simulation. The no-slip boundary condition was applied to the arterial wall.

Results

For the patient with irregular cardiac rhythms (Case I), the coronary blood flow rate under the shortened and lengthened cardiac rhythms were 0.66 and 0.96 mL/s, respectively. In Case II, the maximum velocity at the LVOT under a normal heartbeat was found to be 101 cm/s, whereas the average value was 73 cm/s. In Case III, the patient was also diagnosed with a congenital stenosis problem at the myocardial bridge (MCB) at the LAD. The measured blood flow rate at the MCB of the LAD for the three heartbeats in Case III was found to be 0.68, 1.08, and 1.14 mL/s.

Conclusion

The integration of patient-specific ECG signals and image-based CFD methods can clearly analyze hemodynamic information for patients during cardiac arrhythmia. The cardiac arrhythmia can reduce the blood flow in the coronary arteries.