A framework for personalization of coronary flow computations during rest and hyperemia

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.

Establishing the longitudinal hemodynamic mapping framework for wearable-driven coronary digital twins

Understanding the evolving nature of coronary hemodynamics is crucial for early disease detection and monitoring progression. We require digital twins that mimic a patient's circulatory system by integrating continuous physiological data and computing hemodynamic patterns over months. Current models match clinical flow measurements but are limited to single heartbeats. To this end, we introduced the longitudinal hemodynamic mapping framework (LHMF), designed to tackle critical challenges: (1) computational intractability of explicit methods; (2) boundary conditions reflecting varying activity states; and (3) accessible computing resources for clinical translation. We show negligible error (0.0002-0.004%) between LHMF and explicit data of 750 heartbeats. We deployed LHMF across traditional and cloud-based platforms, demonstrating high-throughput simulations on heterogeneous systems. Additionally, we established LHMFC, where hemodynamically similar heartbeats are clustered to avoid redundant simulations, accurately reconstructing longitudinal hemodynamic maps (LHMs). This study captured 3D hemodynamics over 4.5 million heartbeats, paving the way for cardiovascular digital twins.

The Effect of Severe Coronary Calcification on Diagnostic Performance of Computed Tomography-Derived Fractional Flow Reserve Analyses in People with Coronary Artery Disease

BACKGROUND

Negative CCTA can effectively exclude significant CAD, eliminating the need for further noninvasive or invasive testing. However, in the presence of severe CAD, the accuracy declines, thus necessitating additional testing. The aim of our study was to evaluate the diagnostic performance of noninvasive cFFR derived from CCTA, compared to ICA in detecting hemodynamically significant stenoses in participants with high CAC scores (>400).

METHODS

This study included 37 participants suspected of having CAD who underwent CCTA and ICA. CAC was calculated and cFFR analyses were performed using an on-site machine learning-based algorithm. Diagnostic accuracy parameters of CCTA and cFFR were calculated on a per-vessel level.

RESULTS

The median total CAC score was 870, with an IQR of 642-1370. Regarding CCTA, sensitivity and specificity for RCA were 60% and 67% with an AUC of 0.639; a LAD of 87% and 50% with an AUC of 0.688; an LCX of 33% and 90% with an AUC of 0.617, respectively. Regarding cFFR, sensitivity and specificity for RCA were 60% and 61% with an AUC of 0.606; a LAD of 75% and 54% with an AUC of 0.647; an LCX of 50% and 77% with an AUC of 0.647. No significant differences between AUCs of coronary CTA and cFFR for each vessel were found.

CONCLUSIONS

Our results showed poor diagnostic accuracy of CCTA and cFFR in determining significant ischemia-related lesions in participants with high CAC scores when compared to ICA. Based on our results and study limitations we cannot exclude cFFR as a method for determining significant stenoses in people with high CAC. A key issue is accurate and detailed lumen segmentation based on good-quality CCTA images.

Diagnostic Performance of Coronary Angiography Derived Computational Fractional Flow Reserve

BACKGROUND

Computational fluid dynamics can compute fractional flow reserve (FFR) accurately. However, existing models are limited by either the intravascular hemodynamic phenomarkers that can be captured or the fidelity of geometries that can be modeled.

METHODS AND RESULTS

This study aimed to validate a new coronary angiography-based FFR framework, FFRHARVEY, and examine intravascular hemodynamics to identify new biomarkers that could augment FFR in discerning unrevascularized patients requiring intervention. A 2-center cohort was used to examine diagnostic performance of FFRHARVEY compared with reference wire-based FFR (FFRINVASIVE). Additional biomarkers, longitudinal vorticity, velocity, and wall shear stress, were evaluated for their ability to augment FFR and indicate major adverse cardiac events. A total of 160 patients with 166 lesions were investigated. FFRHARVEY was compared with FFRINVASIVE by investigators blinded to the invasive FFR results with a per-stenosis area under the curve of 0.91, positive predictive value of 90.2%, negative predictive value of 89.6%, sensitivity of 79.3%, and specificity of 95.4%. The percentage ofdiscrepancy for continuous values of FFR was 6.63%. We identified a hemodynamic phenomarker, longitudinal vorticity, as a metric indicative of major adverse cardiac events in unrevascularized gray-zone cases.

CONCLUSIONS

FFRHARVEY had high performance (area under the curve: 0.91, positive predictive value: 90.2%, negative predictive value: 89.6%) compared with FFRINVASIVE. The proposed framework provides a robust and accurate way to compute a complete set of intravascular phenomarkers, in which longitudinal vorticity was specifically shown to differentiate vessels predisposed to major adverse cardiac events.

Cloud Computing to Enable Wearable-Driven Longitudinal Hemodynamic Maps

Tracking hemodynamic responses to treatment and stimuli over long periods remains a grand challenge. Moving from established single-heartbeat technology to longitudinal profiles would require continuous data describing how the patient's state evolves, new methods to extend the temporal domain over which flow is sampled, and high-throughput computing resources. While personalized digital twins can accurately measure 3D hemodynamics over several heartbeats, state-of-the-art methods would require hundreds of years of wallclock time on leadership scale systems to simulate one day of activity. To address these challenges, we propose a cloud-based, parallel-in-time framework leveraging continuous data from wearable devices to capture the first 3D patient-specific, longitudinal hemodynamic maps. We demonstrate the validity of our method by establishing ground truth data for 750 beats and comparing the results. Our cloud-based framework is based on an initial fixed set of simulations to enable the wearable-informed creation of personalized longitudinal hemodynamic maps.

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.

Impact of baseline coronary flow and its distribution on fractional flow reserve prediction

Model-based prediction of fractional flow reserve (FFR) in the context of stable coronary artery disease (CAD) diagnosis requires a number of modelling assumptions. One of these assumptions is the definition of a baseline coronary flow, ie, total coronary flow at rest prior to the administration of drugs needed to perform invasive measurements. Here we explore the impact of several methods available in the literature to estimate and distribute baseline coronary flow on FFR predictions obtained with a reduced-order model. We consider 63 patients with suspected stable CAD, for a total of 105 invasive FFR measurements. First, we improve a reduced-order model with respect to previous results and validate its performance versus results obtained with a 3D model. Next, we assess the impact of a wide range of methods to impose and distribute baseline coronary flow on FFR prediction, which proved to have a significant impact on diagnostic performance. However, none of the proposed methods resulted in a significant improvement of prediction error standard deviation. Finally, we show that intrinsic uncertainties related to stenosis geometry and the effect of hyperemic inducing drugs have to be addressed in order to improve FFR prediction accuracy.

CT-based fractional flow reserve: development and expanded application

Computations of fractional flow reserve, based on CT coronary angiography and computational fluid dynamics (CT-based FFR) to assess the severity of coronary artery stenosis, was introduced around a decade ago and is now one of the most successful applications of computational fluid dynamic modelling in clinical practice. Although the mathematical modelling framework behind this approach and the clinical operational model vary, its clinical efficacy has been demonstrated well in general. In this review, technical elements behind CT-based FFR computation are summarised with some key assumptions and challenges. Examples of these challenges include the complexity of the model (such as blood viscosity and vessel wall compliance modelling), whose impact has been debated in the research. Efforts made to address the practical challenge of processing time are also reviewed. Then, further application areas—myocardial bridge, renal stenosis and lower limb stenosis—are discussed along with specific challenges expected in these areas.

Diagnostic performance of CT-derived resting distal to aortic pressure ratio (resting Pd/Pa) vs. CT-derived fractional flow reserve (CT-FFR) in coronary lesion severity assessment

BACKGROUND

Computed tomography-derived fractional flow reserve (CT-FFR) has emerged as a promising non-invasive substitute for fractional flow reserve (FFR) measurement. Normally, CT-FFR providing functional significance of coronary artery disease (CAD) by using a simplified total coronary resistance index (TCRI) model. Yet the error or discrepancy caused by this simplified model remains unclear.

METHODS

A total of 20 consecutive patients with suspected CAD who underwent CTA and invasive FFR measurement were retrospectively analyzed. CT-FFR and CT-(Pd/Pa)rest values derived from the coronary CTA images. The diagnostic performance of CT-FFR and CT-(Pd/Pa)rest were evaluated on a per-vessel level using C statistics with invasive FFR<0.80 as the reference standard.

RESULTS

Of the 25 vessels eventually analyzed, the prevalence of functionally significant CAD were 64%. The Youden index of the ROC curve indicated that the best cutoff value of invasive resting Pd/Pa was 0.945 for identifying functionally significant lesions. Sensitivity, specificity, negative predictive value, positive predictive value and accuracy were 85%, 91%, 92%, 83% and 88% for CT-(Pd/Pa)rest and 85%, 58% 69%, 78% and 72% for CT-FFR. Area under the receiver-operating characteristic curve (AUC) to detect functionally significant stenoses of CT-(Pd/Pa)rest and CT-FFR were 0.87 and 0.90.

CONCLUSIONS

In this study, the results suggest CT-derived resting Pd/Pa has a potential advantage over CT-FFR in triaging patients for revascularization.

Coronary CT angiography-based estimation of myocardial perfusion territories for coronary artery FFR and wall shear stress simulation

This study aims to apply a CCTA-derived territory-based patient-specific estimation of boundary conditions for coronary artery fractional flow reserve (FFR) and wall shear stress (WSS) simulation. The non-invasive simulation can help diagnose the significance of coronary stenosis and the likelihood of myocardial ischemia. FFR is often regarded as the gold standard to evaluate the functional significance of stenosis in coronary arteries. In another aspect, proximal wall shear stress (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{{WSS}_{prox}}$$\end{document}WSSprox) can also be an indicator of plaque vulnerability. During the simulation process, the mass flow rate of the blood in coronary arteries is one of the most important boundary conditions. This study utilized the myocardium territory to estimate and allocate the mass flow rate. 20 patients are included in this study. From the knowledge of anatomical information of coronary arteries and the myocardium, the territory-based FFR and the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{{WSS}_{prox}}$$\end{document}WSSprox can both be derived from fluid dynamics simulations. Applying the threshold of distinguishing between significant and non-significant stenosis, the territory-based method can reach the accuracy, sensitivity, and specificity of 0.88, 0.90, and 0.80, respectively. For significantly stenotic cases (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{FFR}_{m}$$\end{document}FFRm\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}≤ 0.80), the vessels usually have higher wall shear stress in the proximal region of the lesion.

Branch flow distribution approach and its application in the calculation of fractional flow reserve in stenotic coronary artery

OBJECTIVE

To calculate fractional flow reserve (FFR) based on computed tomography angiography (i.e., FFRCT) by considering the branch flow distribution in the coronary arteries.

BACKGROUND

FFR is the gold standard to diagnose myocardial ischemia caused by coronary stenosis. An accurate and noninvasive method for obtaining total coronary blood flow is needed for the calculation of FFRCT.

METHODS

A mathematical model for estimating the coronary blood flow rate and two approaches for setting the patient-specific flow boundary condition were proposed. Coronary branch flow distribution methods based on a volume-flow approach and a diameter-flow approach were employed for the numerical simulation of FFRCT. The values of simulated FFRCT for 16 patients were compared with their clinically measured FFR.

RESULTS

The ratio of total coronary blood flow to cardiac output and the myocardial blood flow under the condition of hyperemia were 16.97% and 4.07 mL/min/g, respectively. The errors of FFRCT compared with clinical data under the volume-flow approach and diameter-flow approach were 10.47% and 11.76%, respectively, the diagnostic accuracies of FFRCT were 65% and 85%, and the consistencies were 95% and 90%.

CONCLUSIONS

The mathematical model for estimating the coronary blood flow rate and the coronary branch flow distribution method can be applied to calculate the value of clinical noninvasive FFRCT.

Myocardial Perfusion Simulation for Coronary Artery Disease: A Coupled Patient-Specific Multiscale Model

Patient-specific models of blood flow are being used clinically to diagnose and plan treatment for coronary artery disease. A remaining challenge is bridging scales from flow in arteries to the micro-circulation supplying the myocardium. Previously proposed models are descriptive rather than predictive and have not been applied to human data. The goal here is to develop a multiscale patient-specific model enabling blood flow simulation from large coronary arteries to myocardial tissue. Patient vasculatures are segmented from coronary computed tomography angiography data and extended from the image-based model down to the arteriole level using a space-filling forest of synthetic trees. Blood flow is modeled by coupling a 1D model of the coronary arteries to a single-compartment Darcy myocardium model. Simulated results on five patients with non-obstructive coronary artery disease compare overall well to [\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{15}$$\end{document}15O]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {H}_{{2}}$$\end{document}H2O PET exam data for both resting and hyperemic conditions. Results on a patient with severe obstructive disease link coronary artery narrowing with impaired myocardial blood flow, demonstrating the model’s ability to predict myocardial regions with perfusion deficit. This is the first report of a computational model for simulating blood flow from the epicardial coronary arteries to the left ventricle myocardium applied to and validated on human data.

Quantification of effects of mean blood pressure and left ventricular mass on noninvasive fast fractional flow reserve

While brachial mean blood pressure (MBP) and left ventricular mass (LVM) measured from CTCA are the two CFD simulation input parameters, their effects on noninvasive fractional flow reserve (FFRB) have not been systematically investigated. We demonstrate that inaccurate MBP and LVM inputs differing from patient-specific values could result in misclassification of borderline ischemic lesions. This is important in the clinical application of noninvasive FFR in coronary artery disease diagnosis.

Diagnostic Accuracy of a Novel On-site Virtual Fractional Flow Reserve Parallel Computing System

PURPOSE

To evaluate the diagnostic accuracy of a novel on-site virtual fractional flow reserve (vFFR) derived from coronary computed tomography angiography (CTA).

MATERIALS AND METHODS

We analyzed 100 vessels from 57 patients who had undergone CTA followed by invasive FFR during coronary angiography. Coronary lumen segmentation and three-dimensional reconstruction were conducted using a completely automated algorithm, and parallel computing based vFFR prediction was performed. Lesion-specific ischemia based on FFR was defined as significant at ≤0.8, as well as ≤0.75, and obstructive CTA stenosis was defined that ≥50%. The diagnostic performance of vFFR was compared to invasive FFR at both ≤0.8 and ≤0.75.

RESULTS

The average computation time was 12 minutes per patient. The correlation coefficient (r) between vFFR and invasive FFR was 0.75 [95% confidence interval (CI) 0.65 to 0.83], and Bland-Altman analysis showed a mean bias of 0.005 (95% CI -0.011 to 0.021) with 95% limits of agreement of -0.16 to 0.17 between vFFR and FFR. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 78.0%, 87.1%, 72.5%, 58.7%, and 92.6%, respectively, using the FFR cutoff of 0.80. They were 87.0%, 95.0%, 80.0%, 54.3%, and 98.5%, respectively, with the FFR cutoff of 0.75. The area under the receiver-operating characteristics curve of vFFR versus obstructive CTA stenosis was 0.88 versus 0.61 for the FFR cutoff of 0.80, respectively; it was 0.94 versus 0.62 for the FFR cutoff of 0.75.

CONCLUSION

Our novel, fully automated, on-site vFFR technology showed excellent diagnostic performance for the detection of lesion-specific ischemia.

Comparison of Computed Tomography derived Fractional Flow Reserve to invasive Fractional Flow Reserve in Diagnosis of Functional Coronary Stenosis: A Meta-Analysis

Computed Tomography derived Fractional Flow Reserve (CTFFR) is an emerging non-invasive imaging modality to assess functional significance of coronary stenosis. We performed a meta-analysis to compare the diagnostic performance of CTFFR to invasive Fractional Flow reserve (FFR). Electronic search was performed to identify relevant articles. Pooled Estimates of sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-) and diagnostic odds ratio (DOR) with corresponding 95% confidence intervals (CI) were calculated at the patient level as well as the individual vessel level using hierarchical logistic regression, summary receiver operating characteristic (SROC) curve and area under the curve were estimated. Our search yielded 559 articles and of these 17 studies was included in the analysis. A total of 2,191 vessels in 1294 patients were analyzed. Pooled estimates of sensitivity, specificity, LR+, LR- and DOR with corresponding 95% CI at per-patient level were 83% (79-87), 72% (68-76), 3.0 (2.6-3.5), 0.23 (0.18-0.29) and 13 (9-18) respectively. Pooled estimates of sensitivity, specificity, LR+, LR- and DOR with corresponding 95% CI at per-vessel level were 85% (83-88), 76% (74-79), 3.6 (3.3-4.0), 0.19 (0.16-0.22) and 19 (15-24). The area under the SROC curve was 0.89 for both per patient level and at the per vessel level. In our meta-analysis, CTFFR demonstrated good diagnostic performance in identifying functionally significant coronary artery stenosis compared to the FFR.

Noninvasive FFR derived from coronary CT angiography in the management of coronary artery disease: technology and clinical update

After a decade of clinical use of coronary computed tomographic angiography (CCTA) to evaluate the anatomic severity of coronary artery disease, new methods of deriving functional information from CCTA have been developed. These methods utilize the anatomic information provided by CCTA in conjunction with computational fluid dynamics to calculate fractional flow reserve (FFR) values from CCTA image data sets. Computed tomography-derived FFR (CT-FFR) enables the identification of lesion-specific drop noninvasively. A three-dimensional CT-FFR modeling technique, which provides FFR values throughout the coronary tree (HeartFlow FFR_CT analysis), has been validated against measured FFR and is now approved by the US Food and Drug Administration for clinical use. This technique requires off-site supercomputer analysis. More recently, a one-dimensional computational analysis technique (Siemens cFFR), which can be performed on on-site workstations, has been developed and is currently under investigation. This article reviews CT-FFR technology and clinical evidence for its use in stable patients with suspected coronary artery disease.

[Computed tomography in patients with chronic stable angina : Fractional flow reserve measurement]

Coronary computed tomography angiography (cCTA) has been established for the non-invasive diagnosis of coronary artery disease (CAD). Previous studies demonstrated the high diagnostic accuracy of cCTA, particularly for ruling out CAD. As a known limitation of cCTA a large number of visually significant coronary stenoses are found to be hemodynamically not relevant by invasive fractional flow reserve (FFR). CT-based FFR (CT-FFR) builds on recent advances in computational fluid dynamics and image simulation techniques. Along with CT myocardial perfusion imaging, CT-FFR is a promising approach towards a more accurate estimation of the hemodynamic relevance of coronary artery stenoses. CT-FFR is derived from regular CT datasets without additional image acquisitions, contrast material, or medication. Two CT-FFR techniques can be differentiated. The initial method requires external use of supercomputers and has gained approval for clinical use in the USA. Furthermore, a prototype-software has been introduced which is less computationally demanding via integration of reduced-order models for on-site calculation of CT-FFR. The present article reviews these methods in the context of available study results and meta-analyses. Furthermore, limitations and future concepts of CT-FFR are discussed.