Geometry-based pressure drop prediction in mildly diseased human coronary arteries

Rapid prediction of wall shear stress in stenosed coronary arteries based on deep learning

There is increasing evidence that coronary artery wall shear stress (WSS) measurement provides useful prognostic information that allows prediction of adverse cardiovascular events. Computational Fluid Dynamics (CFD) has been extensively used in research to measure vessel physiology and examine the role of the local haemodynamic forces on the evolution of atherosclerosis. Nonetheless, CFD modelling remains computationally expensive and time-consuming, making its direct use in clinical practice inconvenient. A number of studies have investigated the use of deep learning (DL) approaches for fast WSS prediction. However, in these reports, patient data were limited and most of them used synthetic data generation methods for developing the training set. In this paper, we implement 2 approaches for synthetic data generation and combine their output with real patient data in order to train a DL model with a U-net architecture for prediction of WSS in the coronary arteries. The model achieved 6.03% Normalised Mean Absolute Error (NMAE) with inference taking only 0.35 s; making this solution time-efficient and clinically relevant.

Estimating pulmonary arterial remodeling via an animal-specific computational model of pulmonary artery stenosis

Pulmonary artery stenosis (PAS) often presents in children with congenital heart disease, altering blood flow and pressure during critical periods of growth and development. Variability in stenosis onset, duration, and severity result in variable growth and remodeling of the pulmonary vasculature. Computational fluid dynamics (CFD) models enable investigation into the hemodynamic impact and altered mechanics associated with PAS. In this study, a one-dimensional (1D) fluid dynamics model was used to simulate hemodynamics throughout the pulmonary arteries of individual animals. The geometry of the large pulmonary arteries was prescribed by animal-specific imaging, whereas the distal vasculature was simulated by a three-element Windkessel model at each terminal vessel outlet. Remodeling of the pulmonary vasculature, which cannot be measured in vivo, was estimated via model-fitted parameters. The large artery stiffness was significantly higher on the left side of the vasculature in the left pulmonary artery (LPA) stenosis group, but neither side differed from the sham group. The sham group exhibited a balanced distribution of total distal vascular resistance, whereas the left side was generally larger in the LPA stenosis group, with no significant differences between groups. In contrast, the peripheral compliance on the right side of the LPA stenosis group was significantly greater than the corresponding side of the sham group. Further analysis indicated the underperfused distal vasculature likely moderately decreased in radius with little change in stiffness given the increase in thickness observed with histology. Ultimately, our model enables greater understanding of pulmonary arterial adaptation due to LPA stenosis and has potential for use as a tool to noninvasively estimate remodeling of the pulmonary vasculature.

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.

Beyond CFD: Emerging methodologies for predictive simulation in cardiovascular health and disease

Physics-based computational models of the cardiovascular system are increasingly used to simulate hemodynamics, tissue mechanics, and physiology in evolving healthy and diseased states. While predictive models using computational fluid dynamics (CFD) originated primarily for use in surgical planning, their application now extends well beyond this purpose. In this review, we describe an increasingly wide range of modeling applications aimed at uncovering fundamental mechanisms of disease progression and development, performing model-guided design, and generating testable hypotheses to drive targeted experiments. Increasingly, models are incorporating multiple physical processes spanning a wide range of time and length scales in the heart and vasculature. With these expanded capabilities, clinical adoption of patient-specific modeling in congenital and acquired cardiovascular disease is also increasing, impacting clinical care and treatment decisions in complex congenital heart disease, coronary artery disease, vascular surgery, pulmonary artery disease, and medical device design. In support of these efforts, we discuss recent advances in modeling methodology, which are most impactful when driven by clinical needs. We describe pivotal recent developments in image processing, fluid-structure interaction, modeling under uncertainty, and reduced order modeling to enable simulations in clinically relevant timeframes. In all these areas, we argue that traditional CFD alone is insufficient to tackle increasingly complex clinical and biological problems across scales and systems. Rather, CFD should be coupled with appropriate multiscale biological, physical, and physiological models needed to produce comprehensive, impactful models of mechanobiological systems and complex clinical scenarios. With this perspective, we finally outline open problems and future challenges in the field.

Automated generation of 0D and 1D reduced-order models of patient-specific blood flow

Three-dimensional (3D) cardiovascular fluid dynamics simulations typically require hours to days of computing time on a high-performance computing cluster. One-dimensional (1D) and lumped-parameter zero-dimensional (0D) models show great promise for accurately predicting blood bulk flow and pressure waveforms with only a fraction of the cost. They can also accelerate uncertainty quantification, optimization, and design parameterization studies. Despite several prior studies generating 1D and 0D models and comparing them to 3D solutions, these were typically limited to either 1D or 0D and a singular category of vascular anatomies. This work proposes a fully automated and openly available framework to generate and simulate 1D and 0D models from 3D patient-specific geometries, automatically detecting vessel junctions and stenosis segments. Our only input is the 3D geometry; we do not use any prior knowledge from 3D simulations. All computational tools presented in this work are implemented in the open-source software platform SimVascular. We demonstrate the reduced-order approximation quality against rigid-wall 3D solutions in a comprehensive comparison with N = 72 publicly available models from various anatomies, vessel types, and disease conditions. Relative average approximation errors of flows and pressures typically ranged from 1% to 10% for both 1D and 0D models, measured at the outlets of terminal vessel branches. In general, 0D model errors were only slightly higher than 1D model errors despite requiring only a third of the 1D runtime. Automatically generated ROMs can significantly speed up model development and shift the computational load from high-performance machines to personal computers.

The quantitative relationship between coronary microcirculatory resistance and myocardial ischemia in patients with coronary artery disease

It was hypothesized that the microcirculatory resistance of resting state (R_m-res) might be a good predictor for ischemia. In this study, the quantitative relationship between R_m-res and myocardial ischemia in different stenosed degrees was explored and verified through retrospective analysis, and the diagnostic performance was evaluated. 136 patients were screened and divided into a training set (90 patients) and a validation set (46 patients). In the training set, R_m-res was calculated, and thresholds were determined by exploring the relationship between R_m-res and myocardial ischemia in different stenosed degrees. In the validation set, the diagnostic performance of the thresholds was verified. It was found that the 90 data mean difference (95%CI) of R_m-res between the ischemic group and the non-ischemic group was 63.03 (95 %CI: 25.72-100.34), p < 0.05. In the training set with stenosed degree 41-60%, 61-70%, 71-80%, and >81%, the average of R_m-res in the ischemic and non-ischemic groups were (80.79, 136.87), (96.41, 172.62), (128.99, 198.94) and (175.95, 310.79) mmHg/s/ml. The R_m-res thresholds were 87.18, 118.96, 142.35, and 177.39 mmHg/s/ml. In the validation set, the overall sensitivity, specificity, PPV, NPV, and accuracy were 73.3%, 77.4%, 61.1%, 85.7%, and 76.1%. In conclusion, R_m-res had a significant predictor on myocardial ischemia. As a smaller R_m-res represents greater myocardial mass perfusion, it is more likely that a stenosis will have a functional impact. Threshold analysis showed that R_m-res of different stenosed degrees was a quantitative predictor of myocardial ischemia, which could assist physicians with clinical treatment strategies.

Effect of guidewire insertion in fractional flow reserve procedure for real geometry using computational fluid dynamics

BACKGROUND

Coronary artery disease is an abnormal contraction of the heart supply blood vessel. It limits the oxygenated blood flow to the heart. Thus, diagnosing its severity helps physicians to select the appropriate treatment plan. Fractional flow reserve (FFR) is the most accurate method to pinpoint the stenosis severity. However, inserting the guidewire across stenosis may cause a false overestimation of severity.

METHODS

To estimate the errors due to guidewire insertion, reconstructed three-dimensional coronary artery geometry from a patient-specific scan is used. A comprehensive three-dimensional blood flow model is developed. Blood is considered non-Newtonian and the flow is pulsatile. The model is numerically simulated using realistic boundary conditions.

RESULTS

The FFR value is calculated and compared with the actual flow ratio. Additionally, the ratio between pressure drop and distal dynamic pressure (CDP) is studied. The obtained results for each case are compared and analyzed with the case without a guidewire. It was found that placing the guidewire leads to overestimating the severity of moderate stenosis. It reduces the FFR value from 0.43 to 0.33 with a 23.26% error compared to 0.44 actual flow ratio and the CDP increases from 5.31 to 7.2 with a 35.6% error. FFR value in mild stenosis does not have a significant change due to placing the guidewire. The FFR value decreases from 0.83 to 0.82 compared to the 0.83 actual flow ratio.

CONCLUSION

Consequently, physicians should consider these errors while deciding the treatment plan.

Comparison of LBM and FVM in the estimation of LAD stenosis

The finite volume method (FVM)-based computational fluid dynamics (CFD) technology has been applied in the non-invasive diagnosis of coronary artery stenosis. Nonetheless, FVM is a time-consuming process. In addition to FVM, the lattice Boltzmann method (LBM) is used in fluid flow simulation. Unlike FVM solving the Navier-Stokes equations, LBM directly solves the simplified Boltzmann equation, thus saving computational time. In this study, 12 patients with left anterior descending (LAD) stenosis, diagnosed by CTA, are analysed using FVM and LBM. The velocities, pressures, and wall shear stress (WSS) predicted using FVM and LBM for each patient is compared. In particular, the ratio of the average and maximum speed at the stenotic part characterising the degree of stenosis is compared. Finally, the golden standard of LAD stenosis, invasive fractional flow reserve (FFR), is applied to justify the simulation results. Our results show that LBM and FVM are consistent in blood flow simulation. In the region with a high degree of stenosis, the local flow patterns in those two solvers are slightly different, resulting in minor differences in local WSS estimation and blood speed ratio estimation. Notably, these differences do not result in an inconsistent estimation. Comparison with invasive FFR shows that, in most cases, the non-invasive diagnosis is consistent with FFR measurements. However, in some cases, the non-invasive diagnosis either underestimates or overestimates the degree of stenosis. This deviation is caused by the difference between physiological and simulation conditions that remains the biggest challenge faced by all CFD-based non-invasive diagnostic methods.

Coronary arterial geometry: A comprehensive comparison of two imaging modalities

The characterization of vascular geometry is a fundamental step towards the correct interpretation of coronary artery disease. In this work, we report a comprehensive comparison of the geometry featured by coronary vessels as obtained from coronary computed tomography angiography (CCTA) and the combination of intravascular ultrasound (IVUS) with bi-plane angiography (AX) modalities. We analyzed 34 vessels from 28 patients with coronary disease, which were deferred to CCTA and IVUS procedures. We discuss agreement and discrepancies between several geometric indexes extracted from vascular geometries. Such an analysis allows us to understand to which extent the coronary vascular geometry can be reliable in the interpretation of geometric risk factors, and as a surrogate to characterize coronary artery disease.

A Hemodynamic Comparison of Myocardial Bridging and Coronary Atherosclerotic Stenosis: A Computational Model With Experimental Evaluation

Myocardial bridging (MB) and coronary atherosclerotic stenosis can impair coronary blood flow and may cause myocardial ischemia or even heart attack. It remains unclear how MB and stenosis are similar or different regarding their impacts on coronary hemodynamics. The purpose of this study was to compare the hemodynamic effects of coronary stenosis and MB using experimental and computational fluid dynamics (CFD) approaches. For CFD modeling, three MB patients with different levels of lumen obstruction, mild, moderate, and severe were selected. Patient-specific left anterior descending (LAD) coronary artery models were reconstructed from biplane angiograms. For each MB patient, the virtually healthy and stenotic models were also simulated for comparison. In addition, an in vitro flow-loop was developed, and the pressure drop was measured for comparison. The CFD simulations results demonstrated that the difference between MB and stenosis increased with increasing MB/stenosis severity and flowrate. Experimental results showed that increasing the MB length (by 140%) only had significant impact on the pressure drop in the severe MB (39% increase at the exercise), but increasing the stenosis length dramatically increased the pressure drop in both moderate and severe stenoses at all flow rates (31% and 93% increase at the exercise, respectively). Both CFD and experimental results confirmed that the MB had a higher maximum and a lower mean pressure drop in comparison with the stenosis, regardless of the degree of lumen obstruction. A better understanding of MB and atherosclerotic stenosis may improve the therapeutic strategies in coronary disease patients and prevent acute coronary syndromes.

A zero-dimensional predictive model for the pressure drop in the stenotic coronary artery based on its geometric characteristics

The diameter- or area-reduction ratio measured from coronary angiography, commonly used in clinical practice, is not accurate enough to represent the functional significance of the stenosis, i.e., the pressure drop across the stenosis. We propose a new zero-dimensional model for the pressure drop across the stenosis considering its geometric characteristics and flow rate. To identify the geometric parameters affecting the pressure drop, we perform three-dimensional numerical simulations for thirty-three patient-specific coronary stenoses. From these numerical simulations, we show that the pressure drop is mostly determined by the curvature as well as the area-reduction ratio of the stenosis before the minimal luminal area (MLA), but heavily depends on the area-expansion ratio after the MLA due to flow separation. Based on this result, we divide the stenosis into the converging and diverging parts in the present zero-dimensional model. The converging part is segmented into a series of straight and curved pipes with curvatures, and the loss of each pipe is estimated by an empirical relation between the total pressure drop, flow rate, and pipe geometric parameters (length, diameter, and curvature). The loss in the diverging part is predicted by a relation among the total pressure drop, Reynolds number, and area expansion ratio with the coefficients determined by a machine learning method. The pressure drops across the stenoses predicted by the present zero-dimensional model agree very well with those obtained from three-dimensional numerical simulations.

Reduced order models for transstenotic pressure drop in the coronary arteries

The efficacy of reduced order modeling for transstenotic pressure drop in the coronary arteries is presented. Coronary artery disease is a leading cause of death worldwide and the computation of fractional flow reserve from computed tomography (FFRct) has become a standard for evaluating the functional significance of a coronary stenosis. FFRct uses 3D computational fluid dynamics to simulate coronary blood flow in order to compute transstenotic pressure drop during simulated hyperemia. In this study, we evaluate different fidelity hydrodynamic models and their ability to compute transstenotic pressure drop and FFRct in the coronary arteries. Models range from simple algebraic formulae to 1D, 2D and 3D time-dependent computational fluid dynamic simulations. Although several algebraic pressure-drop formulae have been proposed in the literature, these models were found to exhibit wide variation in predictions. Nonetheless, we demonstrate an algebraic formula that provides reliable predictions over a range of stenosis severity, morphology, location and flow rate when compared to the current standard for FFRct. The accounting of viscous dissipation, flow separation and pulsatile inertial effects were found to be the most significant contributions to accurate reduce order modeling of transstenotic coronary hemodynamics.

Prediction of atheromatic plaque evolution in carotids using features extracted from the arterial geometry

Knowing the arterial geometry might be helpful in the assessment of a plaque rupture event. We present a proof of concept study implementing a novel method which can predict the evolution in time of the atheromatic plaque in carotids using only statistical features which are extracted from the arterial geometry. Four feature selection methods were compared: Quadratic Programming Feature Selection (QPFS), Minimal Redundancy Maximal Relevance (mRMR), Mutual Information Quotient (MIQ) and Spectral Conditional Mutual Information (SPECCMI). The classifier used is the Support Vector Machines (SVM) with linear and Gaussian kernels. The maximum accuracy that was achieved in predicting the variation in the mean value of the Lumen distance from the centerline and the thickness was 71.2% and 70.7% respectively.