An improved reduced-order model for pressure drop across arterial stenoses

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 review of computational methodologies to predict the fractional flow reserve in coronary arteries with stenosis

Computational methodologies for predicting the fractional flow reserve (FFR) in coronary arteries with stenosis have gained significant attention due to their potential impact on healthcare outcomes. Coronary artery disease is a leading cause of mortality worldwide, prompting the need for accurate diagnostic and treatment approaches. The use of medical image-based anatomical vascular geometries in computational fluid dynamics (CFD) simulations to evaluate the hemodynamics has emerged as a promising tool in the medical field. This comprehensive review aims to explore the state-of-the-art computational methodologies focusing on the possible considerations. Key aspects include the rheology of blood, boundary conditions, fluid-structure interaction (FSI) between blood and the arterial wall, and multiscale modelling (MM) of stenosis. Through an in-depth analysis of the literature, the goal is to obtain an overview of the major achievements regarding non-invasive methods to compute FFR and to identify existing gaps and challenges that inform further advances in the field. This research has the major objective of improving the current diagnostic capabilities and enhancing patient care in the context of cardiovascular diseases.

Co-registered optical coherence tomography and X-ray angiography for the prediction of fractional flow reserve

Cardiovascular disease (CVD) stands as the leading global cause of mortality, and coronary artery disease (CAD) has the highest prevalence, contributing to 42% of these fatalities. Recognizing the constraints inherent in the anatomical assessment of CAD, Fractional Flow Reserve (FFR) has emerged as a pivotal functional diagnostic metric. Herein, we assess the potential of employing an ensemble approach with deep neural networks (DNN) to predict invasively measured Fractional Flow Reserve (FFR) using raw anatomical data extracted from both optical coherence tomography (OCT) and X-ray coronary angiography (XA). In this study, we used a challenging dataset, with 46% of the lesions falling within the FFR range of 0.75 to 0.85. Despite this complexity, our model achieved an accuracy of 84.3%, demonstrating a sensitivity of 87.5% and a specificity of 81.4%. Our results demonstrate that incorporating both OCT and XA signals, co-registered, as inputs for the DNN model leads to an important increase in overall accuracy.

Accuracy of analytically determined fractional flow reserve derived from coronary angiography for non-invasive assessment of coronary artery stenosis

BACKGROUND

Fractional flow reserve (FFR) determined invasively has been globally accepted as the gold standard for determining the functional significance of coronary artery stenoses. However, despite its great importance, the invasive method has certain disadvantages, including the risk of vascular injuries, the need for vasodilation, and significant medical costs. That is why great attention was paid to the development of non-invasive methods that would enable reliable diagnosis without exposing patients to the risk of unwanted consequences.

OBJECTIVE

This paper aimed to create and verify an alternative, less resource- and time-demanding, non-invasive solution.

METHODS

The determination of FFR is based on the application of the fundamental laws of fluid dynamics. All energy losses in the coronary artery with stenosis were identified and analyzed in detail. A three-dimensional model of a coronary artery was generated using the corresponding angiographic images. Finally, the pressure due to stenosis was calculated and the FFR was determined.

RESULTS

The results obtained using the proposed analytical method were compared with available experimental data for 40 patients who experienced the invasive coronary angiography. The coefficient of determination, mean difference and standard deviation values are determined to be 0.726, -0.017 and 0.056, respectively. These values were slightly higher for FFR values above 0.80.

CONCLUSION

The FFR calculated by the proposed analytical method has a relatively good correlation with clinical data, which leads to the conclusion that it can provide a reliable assessment of the functional significance of coronary stenosis.

A multi-dimensional CFD framework for fast patient-specific fractional flow reserve prediction

Fractional flow reserve (FFR) is considered as the gold standard for diagnosing coronary myocardial ischemia. Existing 3D computational fluid dynamics (CFD) methods attempt to predict FFR noninvasively using coronary computed tomography angiography (CTA). However, the accuracy and efficiency of the 3D CFD methods in coronary arteries are considerably limited. In this work, we introduce a multi-dimensional CFD framework that improves the accuracy of FFR prediction by estimating 0D patient-specific boundary conditions, and increases the efficiency by generating 3D initial conditions. The multi-dimensional CFD models contain the 3D vascular model for coronary simulation, the 1D vascular model for iterative optimization, and the 0D vascular model for boundary conditions expression. To improve the accuracy, we utilize clinical parameters to derive 0D patient-specific boundary conditions with an optimization algorithm. To improve the efficiency, we evaluate the convergence state using the 1D vascular model and obtain the convergence parameters to generate appropriate 3D initial conditions. The 0D patient-specific boundary conditions and the 3D initial conditions are used to predict FFR (FFRC). We conducted a retrospective study involving 40 patients (61 diseased vessels) with invasive FFR and their corresponding CTA images. The results demonstrate that the FFRC and the invasive FFR have a strong linear correlation (r = 0.80, p < 0.001) and high consistency (mean difference: 0.014 ±0.071). After applying the cut-off value of FFR (0.8), the accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of FFRC were 88.5%, 93.3%, 83.9%, 84.8%, and 92.9%, respectively. Compared with the conventional zero initial conditions method, our method improves prediction efficiency by 71.3% per case. Therefore, our multi-dimensional CFD framework is capable of improving the accuracy and efficiency of FFR prediction significantly.

Numerical vs analytical comparison with experimental fractional flow reserve values of right coronary artery stenosis

BACKGROUND: The fractional flow reserve (FFR) index has been widely accepted as a standard diagnostic method for identifying functional relevance of coronary stenosis. Since the invasive techniques used for its determination are associated with a certain risk of vascular injury, as well as with an increased cost, several non-invasive procedures have been developed. OBJECTIVE: The aim of this study was to compare FFR values for the coronary artery obtained by computational fluid dynamics (CFD) and coronary computed tomography angiography (CCTA). METHODS: Computation of FFR has been performed using both numerical and the analytical method. The numerical method employs CFD to solve the governing equations which relate to mass and momentum conservation (the continuity equation and the Navier-Stokes equations) as well as CCTA to generate the three-dimensional computational domain. After imposing the appropriate boundary conditions, the values of the pressure change are calculated and the FFR index is determined. Based on Bernoulli’s law, the analytical method calculates the overall pressure drop across the stenosis in the coronary artery, enabling FFR determination. RESULTS: The clinical data for twenty patients who underwent invasive coronary angiography are used to validate the results obtained by using CFD (together with CCTA) simulation and analytical solution. The medically measured FFR compared to the analytical one differs by about 4%, while, the difference is about 2.6% when compared to the numerical FFR. For FFR values below 0.8 (which are considered to be associated with myocardial ischemia) the standard error has a value of 0.01201, while the standard deviation is 0.02081. For FFR values above 0.80, these values are slightly higher. Bland-Altman analysis showed that medical measurement and numerical FFR were in good agreement (SD = 0.0292, p< 0.0001). CONCLUSIONS: The analytically calculated FFR has a slightly lower coefficient of determination than the numerically computed FFR when compared with experimental one. However, it can still give a reliable answer to the question of whether patients need a stent, bypass surgery or only drug treatment and it requires a significantly lower computation time.

Towards a Deep-Learning Approach for Prediction of Fractional Flow Reserve from Optical Coherence Tomography

Cardiovascular disease (CVD) is the number one cause of death worldwide, and coronary artery disease (CAD) is the most prevalent CVD, accounting for 42% of these deaths. In view of the limitations of the anatomical evaluation of CAD, Fractional Flow Reserve (FFR) has been introduced as a functional diagnostic index. Herein, we evaluate the feasibility of using deep neural networks (DNN) in an ensemble approach to predict the invasively measured FFR from raw anatomical information that is extracted from optical coherence tomography (OCT). We evaluate the performance of various DNN architectures under different formulations: regression, classification—standard, and few-shot learning (FSL) on a dataset containing 102 intermediate lesions from 80 patients. The FSL approach that is based on a convolutional neural network leads to slightly better results compared to the standard classification: the per-lesion accuracy, sensitivity, and specificity were 77.5%, 72.9%, and 81.5%, respectively. However, since the 95% confidence intervals overlap, the differences are statistically not significant. The main findings of this study can be summarized as follows: (1) Deep-learning (DL)-based FFR prediction from reduced-order raw anatomical data is feasible in intermediate coronary artery lesions; (2) DL-based FFR prediction provides superior diagnostic performance compared to baseline approaches that are based on minimal lumen diameter and percentage diameter stenosis; and (3) the FFR prediction performance increases quasi-linearly with the dataset size, indicating that a larger train dataset will likely lead to superior diagnostic performance.