Coronary CT angiography-derived fractional flow reserve correlated with invasive fractional flow reserve measurements – initial experience with a novel physician-driven algorithm

A simplified coronary model for diagnosis of ischemia-causing coronary stenosis

BACKGROUND AND OBJECTIVE

The functional assessment of the severity of coronary stenosis from coronary computed tomography angiography (CCTA)-derived fractional flow reserve (FFR) has recently attracted interest. However, existing algorithms run at high computational cost. Therefore, this study proposes a fast calculation method of FFR for the diagnosis of ischemia-causing coronary stenosis.

METHODS

We combined CCTA and machine learning to develop a simplified single-vessel coronary model for rapid calculation of FFR. First, a zero-dimensional model of single-vessel coronary was established based on CCTA, and microcirculation resistance was determined through the relationship between coronary pressure and flow. In addition, a coronary stenosis model based on machine learning was introduced to determine stenosis resistance. Computational FFR (cFFR) was then obtained by combining the zero-dimensional model and the stenosis model with inlet boundary conditions for resting (cFFRr) and hyperemic (cFFRh) aortic pressure, respectively. We retrospectively analyzed 75 patients who underwent clinically invasive FFR (iFFR), and verified the model accuracy by comparison of cFFR with iFFR.

RESULTS

The average computing time of cFFR was less than 2 s. The correlations between cFFRr and cFFRh with iFFR were r = 0.89 (p < 0.001) and r = 0.90 (p < 0.001), respectively. Diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio for cFFRr and cFFRh were 90.7%, 95.0%, 89.1%, 76.0%, 98.0%, 8.7, 0.1 and 92.0%, 95.0%, 90.9%, 79.2%, 98.0%, 10.5, 0.1, respectively.

CONCLUSIONS

The proposed model enables rapid prediction of cFFR and exhibits high diagnostic performance in selected patient cohorts. The model thus provides an accurate and time-efficient computational tool to detect ischemia-causing stenosis and assist with clinical decision-making.

A novel method for noninvasive quantification of fractional flow reserve based on the custom function

Boundary condition settings are key risk factors for the accuracy of noninvasive quantification of fractional flow reserve (FFR) based on computed tomography angiography (i.e., FFRCT). However, transient numerical simulation-based FFRCT often ignores the three-dimensional (3D) model of coronary artery and clinical statistics of hyperemia state set by boundary conditions, resulting in insufficient computational accuracy and high computational cost. Therefore, it is necessary to develop the custom function that combines the 3D model of the coronary artery and clinical statistics of hyperemia state for boundary condition setting, to accurately and quickly quantify FFRCT under steady-state numerical simulations. The 3D model of the coronary artery was reconstructed by patient computed tomography angiography (CTA), and coronary resting flow was determined from the volume and diameter of the 3D model. Then, we developed the custom function that took into account the interaction of stenotic resistance, microcirculation resistance, inlet aortic pressure, and clinical statistics of resting to hyperemia state due to the effect of adenosine on boundary condition settings, to accurately and rapidly identify coronary blood flow for quantification of FFRCT calculation (FFRU). We tested the diagnostic accuracy of FFRU calculation by comparing it with the existing methods (CTA, coronary angiography (QCA), and diameter-flow method for calculating FFR (FFRD)) based on invasive FFR of 86 vessels in 73 patients. The average computational time for FFRU calculation was greatly reduced from 1-4 h for transient numerical simulations to 5 min per simulation, which was 2-fold less than the FFRD method. According to the results of the Bland-Altman analysis, the consistency between FFRU and invasive FFR of 86 vessels was better than that of FFRD. The area under the receiver operating characteristic curve (AUC) for CTA, QCA, FFRD and FFRU at the lesion level were 0.62 (95% CI: 0.51-0.74), 0.67 (95% CI: 0.56-0.79), 0.85 (95% CI: 0.76-0.94), and 0.93 (95% CI: 0.87-0.98), respectively. At the patient level, the AUC was 0.61 (95% CI: 0.48-0.74) for CTA, 0.65 (95% CI: 0.53-0.77) for QCA, 0.83 (95% CI: 0.74-0.92) for FFRD, and 0.92 (95% CI: 0.89-0.96) for FFRU. The proposed novel method might accurately and rapidly identify coronary blood flow, significantly improve the accuracy of FFRCT calculation, and support its wide application as a diagnostic indicator in clinical practice.

The relationship between geometry and hemodynamics of the stenotic carotid artery based on computational fluid dynamics

OBJECTIVE

The purpose of this work was to investigate the relationship between the geometric factors and the hemodynamics of the stenotic carotid artery.

METHODS

We retrospectively reviewed data of patients with carotid stenosis (40%-95%). The Navier-Stokes equations were solved using ANSYS CFX 18.0. Correlation analysis was based on Spearman's test. Geometric variables (p < 0.1 in the univariate analysis) were entered into the logistical regression. A receiver-operating characteristics analysis was used to detect hemodynamically significant lesions.

RESULTS

81 patients (96 arteries) were evaluated. The logistic regression analysis revealed that the translesional pressure ratio was significantly correlated with the stenosis degree (OR = 1.147, p < 0.001) and the angle between internal carotid artery and external carotid artery (angle γ) (OR = 0.933, p = 0.01). The translesional wall shear stress ratio was significantly correlated with stenosis degree (OR = 1.094, p < 0.001), lesion length (OR = 0.873, p = 0.01), lumen area of internal carotid artery (OR = 0.867, p = 0.002), and lumen area of common carotid artery (OR = 1.058, p = 0.01). For predicting low translesional pressure ratio, the AUC was 0.71 (p < 0.001) for angle γ, and was 0.87 (p < 0.001) for stenosis degree. For predicting high translesional wall shear stress ratio, the AUC was 0.62 (p = 0.04) for lumen area of internal carotid artery, and was 0.77 (p < 0.001) for stenosis degree.

CONCLUSIONS

Apart from stenosis degree, other geometric characteristics of lesions may also have an influence on hemodynamics of the stenotic carotid artery.

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.

Diastolic versus systolic coronary computed tomography angiography derived fractional flow reserve for the identification of lesion-specific ischemia

OBJECTIVES

To investigate the measurement discrepancy of coronary computed tomography angiography (CTA)-derived fractional flow reserve (FFR) between diastolic (CT-FFR-D) and systolic (CT-FFR-S) phases using FFR as the reference standard.

METHODS

Participants, suspected of coronary artery disease and indicated for invasive coronary angiography (ICA) and FFR and coronary CTA and CT-FFR, were enrolled in this study (Clinicaltrials.gov:NCT03692936) from September 2018 to October 2019. For every participant, coronary CTA of both systolic and diastolic phases was postprocessed to calculate CT-FFR-S and CT-FFR-D, respectively. Diagnostic sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve were compared.

RESULTS

A total of 181 lesions from 151 participants (mean age 54.5 ± 7.8 years, 113 males) were analyzed. Of these, 129 lesions from 110 participants were successfully measured both in diastolic and systolic phases. Sensitivity, specificity, and accuracy of CT-FFR-D and CT-FFR-S on per-patient level were 88.9%, 91.3%, 90.1% and 66.7%, 87.7%, 76.7%, on per-vessel level were 89.5%, 91.5%, 90.6% and 66.7%, 87.0%, 77.9%, respectively. The ROC curve of CT-FFR-D was significantly higher than that of CT-FFR-S on both per-patient and per-vessel levels (0.938 vs. 0.771, 0.935 vs. 0.772, both p < 0.0001). In severe hemodynamic lesions (FFR ≤ 0.7), the absolute difference between CT-FFR-S and FFR was significantly higher than that between CT-FFR-D and FFR [0.1636, inter-quartile range (IQR): 0.0662-0.2586 vs. 0.0953, IQR: 0.0496-0.1702, p = 0.035].

CONCLUSION

CT-FFR derived in diastole was superior to that derived in systole in detecting coronary ischemic lesions. For lesions with FFR < 0.7, CT-FFR measured in the diastolic phase was noted to be more closely approximated to FFR.

Image-based computational fluid dynamics for estimating pressure drop and fractional flow reserve across iliac artery stenosis: A comparison with in-vivo measurements

Computational Fluid Dynamics (CFD) and time-resolved phase-contrast magnetic resonance imaging (PC-MRI) are potential non-invasive methods for the assessment of the severity of arterial stenoses. Fractional flow reserve (FFR) is the current "gold standard" for determining stenosis severity in the coronary arteries but is an invasive method requiring insertion of a pressure wire. CFD derived FFR (vFFR) is an alternative to traditional catheter derived FFR now available commercially for coronary artery assessment, however, it can potentially be applied to a wider range of vulnerable vessels such as the iliac arteries. In this study CFD simulations are used to assess the ability of vFFR in predicting the stenosis severity in a patient with a stenosis of 77% area reduction (>50% diameter reduction) in the right iliac artery. Variations of vFFR, overall pressure drop and flow split between the vessels were observed by using different boundary conditions. Correlations between boundary condition parameters and resulting flow variables are presented. The study concludes that vFFR has good potential to characterise iliac artery stenotic disease.

Influence of reconstruction kernels on the accuracy of CT-derived fractional flow reserve

OBJECTIVES

We evaluated the influence of image reconstruction kernels on the diagnostic accuracy of CT-derived fractional flow reserve (FFR_CT) compared to invasive FFR in patients with coronary artery disease.

METHODS

Sixty-nine patients, in whom coronary CT angiography was performed and who were further referred for invasive coronary angiography with FFR measurement via pressure wire, were retrospectively included. CT data sets were acquired using a third-generation dual-source CT system and rendered with medium smooth (Bv40) and sharp (Bv49) reconstruction kernels. FFR_CT was calculated on-site using prototype software. Coronary stenoses with invasive FFR ≤ 0.80 were classified as significant. Agreement between FFR_CT and invasive FFR was determined for both reconstruction kernels.

RESULTS

One hundred analyzed vessels in 69 patients were included. Twenty-five vessels were significantly stenosed according to invasive FFR. Using a sharp reconstruction kernel for FFR_CT resulted in a significantly higher correlation with invasive FFR (r = 0.74, p < 0.01 vs. r = 0.58, p < 0.01; p = 0.04) and a higher AUC in ROC curve analysis to correctly identify/exclude significant stenosis (AUC = 0.92 vs. AUC = 0.82 for sharp vs. medium smooth kernel, respectively, p = 0.02). A FFR_CT value of ≤ 0.8 using a sharp reconstruction kernel showed a sensitivity of 88% and a specificity of 92% for detecting ischemia-causing lesions, resulting in a diagnostic accuracy of 91%. The medium smooth reconstruction kernel performed worse (sensitivity 60%, specificity 89%, accuracy 82%).

CONCLUSION

Compared to invasively measured FFR, FFR_CT using a sharp image reconstruction kernel shows higher diagnostic accuracy for detecting lesions causing ischemia, potentially altering decision-making in a clinical setting.

KEY POINTS

• Image reconstruction parameters influence the diagnostic accuracy of simulated fractional flow reserve derived from coronary computed tomography angiography. • Using a sharp kernel image reconstruction algorithm delivers higher diagnostic accuracy compared to medium smooth kernel image reconstruction (gold standard invasive fractional flow reserve).

Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve

BACKGROUND

Fractional flow reserve (FFR) is a widely used gold standard to evaluate ischemia-causing lesions. A new method of non-invasive approach, termed as AccuFFRct, for calculating FFR based on coronary computed tomography angiography (CCTA) and computational fluid dynamics (CFD) has been proposed. However, its diagnostic accuracy has not been validated.

OBJECTIVES

This study sought to present a novel approach for non-invasive computation of FFR and evaluate its diagnostic performance in patients with coronary stenosis.

METHODS

A total of 54 consecutive patients with 78 vessels from a single center who underwent CCTA and invasive FFR measurement were retrospectively analyzed. The CT-derived FFR values were computed using a novel CFD-based model (AccuFFRct, ArteryFlow Technology Co., Ltd., Hangzhou, China). Diagnostic performance of AccuFFRct and CCTA in detecting hemodynamically significant coronary artery disease (CAD) was evaluated using the invasive FFR as a reference standard.

RESULTS

Diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for AccuFFRct in detecting FFR ≤ 0.8 on per-patient basis were 90.7, 89.5, 91.4, 85.0 and 94.1%, respectively, while those of CCTA were 38.9, 100.0, 5.71, 36.5 and 100.0%, respectively. The correlation between AccuFFRct and FFR was good (r = 0.76 and r = 0.65 on per-patient and per-vessel basis, respectively, both p < 0.0001). Area under the curve (AUC) values of AccuFFRct for identifying ischemia per-patient and per-vessel basis were 0.945 and 0.925, respectively. There was much higher accuracy, specificity and AUC for AccuFFRct compared with CCTA.

CONCLUSIONS

AccuFFRct computed from CCTA images alone demonstrated high diagnostic performance for detecting lesion-specific ischemia, it showed superior diagnostic power than CCTA and eliminated the risk of invasive tests, which could be an accurate and time-efficient computational tool for diagnosing ischemia and assisting clinical decision-making.

Diagnostic Performance of a Machine Learning-Based CT-Derived FFR in Detecting Flow-Limiting Stenosis

BACKGROUND

The non-invasive quantification of the fractional flow reserve (FFRCT) using a more recent version of an artificial intelligence-based software and latest generation CT scanner (384 slices) may show high performance to detect coronary ischemia.

OBJECTIVES

To evaluate the diagnostic performance of FFRCT for the detection of significant coronary artery disease (CAD) in contrast to invasive FFR (iFFR) using previous generation CT scanners (128 and 256- detector rows).

METHODS

Retrospective study with patients referred to coronary artery CT angiography (CTA) and catheterization (iFFR) procedures. Siemens Somatom Definition Flash (256-detector rows) and AS+ (128-detector rows) CT scanners were used to acquire the images. The FFRCT and the minimal lumen area (MLA) were evaluated using a dedicated software (cFFR version 3.0.0, Siemens Healthineers, Forchheim, Germany). Obstructive CAD was defined as CTA lumen reduction ≥ 50%, and flow-limiting stenosis as iFFR ≤0.8. All reported P values are two-tailed, and when <0.05, they were considered statistically significant.

RESULTS

Ninety-three consecutive patients (152 vessels) were included. There was good agreement between FFRCT and iFFR, with minimal FFRCT overestimation (bias: -0.02; limits of agreement:0.14-0.09). Different CT scanners did not modify the association between FFRCT and FFRi (p for interaction=0.73). The performance of FFRCT was significantly superior compared to the visual classification of coronary stenosis (AUC 0.93vs.0.61, p<0.001) and to MLA (AUC 0.93vs.0.75, p<0.001), reducing the number of false-positive cases. The optimal cut-off point for FFRCT using a Youden index was 0.85 (87% Sensitivity, 86% Specificity, 73% PPV, 94% NPV), with a reduction of false-positives.

CONCLUSION

Machine learning-based FFRCT using previous generation CT scanners (128 and 256-detector rows) shows good diagnostic performance for the detection of CAD, and can be used to reduce the number of invasive procedures.

Prevalence of pathological FFRCT values without coronary artery stenosis in an asymptomatic marathon runner cohort

OBJECTIVES

To evaluate computed tomography fractional flow reserve (FFR_CT) values in distal parts of the coronaries in an asymptomatic cohort of marathon runners without any coronary stenosis for potentially false-positive values.

METHODS

Ninety-eight asymptomatic male marathon runners (age 53 ± 7 years) were enrolled in a prospective monocentric study and underwent coronary computed tomography angiography (CCTA). CCTA data were analyzed for visual coronary artery stenosis. FFR_CT was evaluated in 59 participants without coronary artery stenosis in proximal, mid, and distal coronary sections using an on-site software prototype.

RESULTS

In participants without coronary artery stenosis, abnormal FFR_CT values ≤ 0.8 in distal segments were found in 22 participants (37%); in 19 participants in the LAD; in 5 participants in the LCX; and in 4 participants in the RCA. Vessel diameters in participants with FFR_CT values > 0.80 compared to ≤ 0.80 were 1.6 ± 0.3 mm versus 1.5 ± 0.3 mm for distal LAD (p = 0.025), 1.8 ± 0.3 mm versus 1.6 ± 0.5 mm for distal LCX (p = 0.183), and 2.0 ± 0.4 mm versus 1.5 ± 0.2 mm for distal RCA (p < 0.001).

CONCLUSIONS

Abnormal FFR_CT values of ≤ 0.8 frequently occurred in distal coronary segments in subjects without any anatomical coronary artery stenosis. This effect is only to some degree explainable by small distal vessel diameters. Therefore, the validity of hemodynamic relevance evaluation using FFR_CT in distal coronary artery segment stenosis is reduced.

KEY POINTS

• Abnormal FFR_CT values (≤ 0.8) occurred in over a third of the subjects in the distal LAD despite the absence of coronary artery stenosis.. • Therefore, the validity of hemodynamic relevance evaluation in distal coronary artery segment stenosis is reduced. • Decision-making based on abnormal FFR_CT values in distal vessel sections should be performed with caution and only in combination with visual assessment of the grade of stenosis..

Serial coronary CT angiography-derived fractional flow reserve and plaque progression can predict long-term outcomes of coronary artery disease

OBJECTIVE

To investigate the utility of coronary CT angiography-derived fractional flow reserve (FFR_CT) and plaque progression in patients undergoing serial coronary CT angiography for predicting major adverse cardiovascular events (MACE).

METHODS

This retrospective study evaluated patients suspected or known coronary artery disease who underwent serial coronary CT angiography examinations between January 2006 and December 2017 and followed up until June 2019. The primary endpoint was MACE, defined as acute coronary syndrome, rehospitalization due to progressive angina, percutaneous coronary intervention, or cardiac death. FFR_CT and plaque parameters were analyzed on a per-vessel and per-patient basis. Univariable and multivariable COX regression analysis determined predictors of MACE. The prognostic value of FFR_CT and plaque progression were assessed in nested models.

RESULTS

Two hundred eighty-four patients (median age, 61 years (interquartile range, 54-70); 202 males) were evaluated. MACE was observed in 45 patients (15.8%, 45/284). By Cox multivariable regression modeling, vessel-specific FFR_CT ≤ 0.80 was associated with a 2.4-fold increased risk of MACE (HR (95% CI): 2.4 (1.3-4.4); p = 0.005) and plaque progression was associated with a 9-fold increased risk of MACE (HR (95% CI): 9 (3.5-23); p < 0.001) after adjusting for clinical and imaging risk factors. FFR_CT and plaque progression improved the prediction of events over coronary artery calcium (CAC) score and high-risk plaques (HRP) in the receiver operating characteristics analysis (area under the curve: 0.70 to 0.86; p = 0.002).

CONCLUSIONS

Fractional flow reserve and plaque progression assessed by serial coronary CT angiography predicted the risk of future MACE.

KEY POINTS

• Vessel-specific CT angiography-derived fractional flow reserve (FFR_CT) ≤ 0.80 and plaque progression improved the prediction of events over current risk factors. • Major adverse cardiovascular events (MACE) significantly increased with the presence of plaque progression at follow-up stratified by the FFR_CT change group.

CT Coronary Angiography Fractional Flow Reserve: New Advances in the Diagnosis and Treatment of Coronary Artery Disease

Coronary artery disease (CAD) remains the most common cardiovascular disease, accounting for 6% of all Emergency Department visits and 27% of all Emergency Department hospitalizations.¹ Invasive coronary angiography with fractional flow reserve (FFR) remains the gold standard to assess for hemodynamically stenosis in CAD patients. However, for low- and intermediate-risk patients, noninvasive modalities have started to gain favor as patients with stable CAD who received optimal medical therapy did as well as patients who underwent percutaneous coronary intervention.² This led to the incorporation of FFR_CT. cCTA provides good spatial resolution for evaluating stenosis. FFR provides additional information regarding whether the stenosis is hemodynamically significant. FFR is the ratio of maximum blood flow in a stenotic artery to the maximum blood flow through that artery without stenosis.³ Computational fluid dynamics involved in FFR_CT is based on Navier-Stokes equations, allowing the assessment of pressure and flow across coronary arteries. Limitations do exist with FFR_CT which includes false-positive results due to step artifact and left ventricular hypertrophy, as well as manual segmentation and ostial stenosis, which can cause false-negative results. However, there are improvements on the horizon including artificial intelligence-driven computation of FFR and the utilization of virtual stenting for surgical planning. The purpose of this review is to describe the clinical validation, underlying mechanism, and implementation of FFR_CT.

The effects of clinically-derived parametric data uncertainty in patient-specific coronary simulations with deformable walls

Cardiovascular simulations are increasingly used for noninvasive diagnosis of cardiovascular disease, to guide treatment decisions, and in the design of medical devices. Quantitative assessment of the variability of simulation outputs due to input uncertainty is a key step toward further integration of cardiovascular simulations in the clinical workflow. In this study, we present uncertainty quantification in computational models of the coronary circulation to investigate the effect of uncertain parameters, including coronary pressure waveform, intramyocardial pressure, morphometry exponent, and the vascular wall Young's modulus. We employ a left coronary artery model with deformable vessel walls, simulated via an Arbitrary-Lagrangian-Eulerian framework for fluid-structure interaction, with a prescribed inlet pressure and open-loop lumped parameter network outlet boundary conditions. Stochastic modeling of the uncertain inputs is determined from intra-coronary catheterization data or gathered from the literature. Uncertainty propagation is performed using several approaches including Monte Carlo, Quasi Monte Carlo sampling, stochastic collocation, and multi-wavelet stochastic expansion. Variabilities in the quantities of interest, including branch pressure, flow, wall shear stress, and wall deformation are assessed. We find that uncertainty in inlet pressures and intramyocardial pressures significantly affect all resulting QoIs, while uncertainty in elastic modulus only affects the mechanical response of the vascular wall. Variability in the morphometry exponent used to distribute the total downstream vascular resistance to the single outlets, has little effect on coronary hemodynamics or wall mechanics. Finally, we compare convergence behaviors of statistics of QoIs using several uncertainty propagation methods on three model benchmark problems and the left coronary simulations. From the simulation results, we conclude that the multi-wavelet stochastic expansion shows superior accuracy and performance against Quasi Monte Carlo and stochastic collocation methods.

Comparison of Machine Learning Computed Tomography-Based Fractional Flow Reserve and Coronary CT Angiography-Derived Plaque Characteristics with Invasive Resting Full-Cycle Ratio

BACKGROUND

The aim is to compare the machine learning-based coronary-computed tomography fractional flow reserve (CT-FFRML) and coronary-computed tomographic morphological plaque characteristics with the resting full-cycle ratio (RFRTM) as a novel invasive resting pressure-wire index for detecting hemodynamically significant coronary artery stenosis.

METHODS

In our single center study, patients with coronary artery disease (CAD) who had a clinically indicated coronary computed tomography angiography (cCTA) and subsequent invasive coronary angiography (ICA) with pressure wire-measurement were included. On-site prototype CT-FFRML software and on-site CT-plaque software were used to calculate the hemodynamic relevance of coronary stenosis.

RESULTS

We enrolled 33 patients (70% male, mean age 68 ± 12 years). On a per-lesion basis, the area under the receiver operating characteristic curve (AUC) of CT-FFRML (0.90) was higher than the AUCs of the morphological plaque characteristics length/minimal luminal diameter4 (LL/MLD4; 0.80), minimal luminal diameter (MLD; 0.77), remodeling index (RI; 0.76), degree of luminal diameter stenosis (0.75), and minimal luminal area (MLA; 0.75).

CONCLUSION

CT-FFRML and morphological plaque characteristics show a significant correlation to detected hemodynamically significant coronary stenosis. Whole CT-FFRML had the best discriminatory power, using RFRTM as the reference standard.

Computational instantaneous wave-free ratio (IFR) for patient-specific coronary artery stenoses using 1D network models

In this work, we estimate the diagnostic threshold of the instantaneous wave-free ratio (iFR) through the use of a one-dimensional haemodynamic framework. To this end, we first compared the computed fractional flow reserve (cFFR) predicted from a 1D computational framework with invasive clinical measurements. The framework shows excellent promise and utilises minimal patient data from a cohort of 52 patients with a total of 66 stenoses. The diagnostic accuracy of the cFFR model was 75.76%, with a sensitivity of 71.43%, a specificity of 77.78%, a positive predictive value of 60%, and a negative predictive value of 85.37%. The validated model was then used to estimate the diagnostic threshold of iFR. The model determined a quadratic relationship between cFFR and the ciFR. The iFR diagnostic threshold was determined to be 0.8910 from a receiver operating characteristic curve that is in the range of 0.89 to 0.9 that is normally reported in clinical studies.

Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR: Results From MACHINE Registry

OBJECTIVES

This study was conducted to investigate the influence of coronary artery calcium (CAC) score on the diagnostic performance of machine-learning-based coronary computed tomography (CT) angiography (cCTA)-derived fractional flow reserve (CT-FFR).

BACKGROUND

CT-FFR is used reliably to detect lesion-specific ischemia. Novel CT-FFR algorithms using machine-learning artificial intelligence techniques perform fast and require less complex computational fluid dynamics. Yet, influence of CAC score on diagnostic performance of the machine-learning approach has not been investigated.

METHODS

A total of 482 vessels from 314 patients (age 62.3 ± 9.3 years, 77% male) who underwent cCTA followed by invasive FFR were investigated from the MACHINE (Machine Learning based CT Angiography derived FFR: a Multi-center Registry) registry data. CAC scores were quantified using the Agatston convention. The diagnostic performance of CT-FFR to detect lesion-specific ischemia was assessed across all Agatston score categories (CAC 0, >0 to <100, 100 to <400, and ≥400) on a per-vessel level with invasive FFR as the reference standard.

RESULTS

The diagnostic accuracy of CT-FFR versus invasive FFR was superior to cCTA alone on a per-vessel level (78% vs. 60%) and per patient level (83% vs. 73%) across all Agatston score categories. No statistically significant differences in the diagnostic accuracy, sensitivity, or specificity of CT-FFR were observed across the categories. CT-FFR showed good discriminatory power in vessels with high Agatston scores (CAC ≥400) and high performance in low-to-intermediate Agatston scores (CAC >0 to <400) with a statistically significant difference in the area under the receiver-operating characteristic curve (AUC) (AUC: 0.71 [95% confidence interval (CI): 0.57 to 0.85] vs. 0.85 [95% CI: 0.82 to 0.89], p = 0.04). CT-FFR showed superior diagnostic value over cCTA in vessels with high Agatston scores (CAC ≥ 400: AUC 0.71 vs. 0.55, p = 0.04) and low-to-intermediate Agatston scores (CAC >0 to <400: AUC 0.86 vs. 0.63, p < 0.001).

CONCLUSIONS

Machine-learning-based CT-FFR showed superior diagnostic performance over cCTA alone in CAC with a significant difference in the performance of CT-FFR as calcium burden/Agatston calcium score increased. (Machine Learning Based CT Angiography Derived FFR: a Multicenter, Registry [MACHINE] NCT02805621).

Prognostic value of CT myocardial perfusion imaging and CT-derived fractional flow reserve for major adverse cardiac events in patients with coronary artery disease

OBJECTIVES

The purpose of this study was to analyze the prognostic value of dynamic CT perfusion imaging (CTP) and CT derived fractional flow reserve (CT-FFR) for major adverse cardiac events (MACE).

METHODS

81 patients from 4 institutions underwent coronary computed tomography angiography (CCTA) with dynamic CTP imaging and CT-FFR analysis. Patients were followed-up at 6, 12, and 18 months after imaging. MACE were defined as cardiac death, nonfatal myocardial infarction, unstable angina requiring hospitalization, or revascularization. CT-FFR was computed for each major coronary artery using an artificial intelligence-based application. CTP studies were analyzed per vessel territory using an index myocardial blood flow, the ratio between territory and global MBF. The prognostic value of CCTA, CT-FFR, and CTP was investigated with a univariate and multivariate Cox proportional hazards regression model.

RESULTS

243 vessels in 81 patients were interrogated by CCTA with CT-FFR and 243 vessel territories (1296 segments) were evaluated with dynamic CTP imaging. Of the 81 patients, 25 (31%) experienced MACE during follow-up. In univariate analysis, a positive index-MBF resulted in the largest risk for MACE (HR 11.4) compared to CCTA (HR 2.6) and CT-FFR (HR 4.6). In multivariate analysis, including clinical factors, CCTA, CT-FFR, and index-MBF, only index-MBF significantly contributed to the risk of MACE (HR 10.1), unlike CCTA (HR 1.2) and CT-FFR (HR 2.2).

CONCLUSION

Our study provides initial evidence that dynamic CTP alone has the highest prognostic value for MACE compared to CCTA and CT-FFR individually or a combination of the three, independent of clinical risk factors.

Noninvasive Derivation of Fractional Flow Reserve From Coronary Computed Tomographic Angiography: A Review

Coronary computed tomographic angiography (CCTA) has evolved as a rapid and highly sensitive method for the exclusion of obstructive coronary artery disease. Unfortunately, as it pertains to moderate and severe lesions, the ability to discriminate between those that are hemodynamically significant and those that are nonobstructive is lacking. Consequently, this deficiency can result in a significant number of unnecessary referrals for invasive angiography that yields nonobstructive results. Fractional flow reserve (FFR), which assesses the hemodynamic significance of a specific lesion, when performed during invasive angiography, results in improved patient outcomes compared with visual stenosis assessment alone. Through the application of computational analytic methods to CT-derived anatomic coronary models, noninvasive calculation of FFR has become possible. This allows for the improved ability to differentiate between nonobstructive coronary lesions and those that are truly hemodynamically significant. Currently, HeartFlow FFRCT is the only FDA-approved and commercially available CCTA-derived FFR (CT-FFR) platform. By reducing the number of invasive procedures performed for nonobstructive disease, CT-derived FFR has the ability to lower health care expenditures and become the true gatekeeper to invasive angiography.

Diagnostic performance of machine-learning-based computed fractional flow reserve (FFR) derived from coronary computed tomography angiography for the assessment of myocardial ischemia verified by invasive FFR

To explore the diagnostic performance of a machine-learning-based (ML-based) computed fractional flow reserve (cFFR) derived from coronary computed tomography angiography (CCTA) in identifying ischemia-causing lesions verified by invasive FFR in catheter coronary angiography (ICA). We retrospectively studied 117 intermediate coronary artery lesions [40-80% diameter stenosis (DS)] from 105 patients (mean age 62 years, 32 female) who had undergone invasive FFR. CCTA images were used to compute cFFR values on the workstation. DS and the myocardium jeopardy index (MJI) of coronary stenosis were also assessed with CCTA. The diagnostic performance of cFFR was evaluated, including its correlation with invasive FFR and its diagnostic accuracy. Then, its performance was compared to that of combined DS and MJI. Of the 117 lesions, 36 (30.8%) had invasive FFR ≤ 0.80; 22 cFFR were measured as true positives and 74 cFFR as true negatives. The average time of cFFR assessment was 18 ± 7 min. The cFFR correlated strongly to invasive FFR (Spearman's coefficient 0.665, p < 0.01). When diagnosing invasive FFR ≤ 0.80, the accuracy of cFFR was 82% with an AUC of 0.864, which was significantly higher than that of DS (accuracy 75%, AUC 0.777, p = 0.013). The AUC of cFFR was not significantly different from that of combined DS and MJI (0.846, p = 0.743). cFFR ≤ 0.80 based on CCTA showed good diagnostic performance for detecting ischemia-producing lesions verified by invasive FFR. The short calculation time required renders cFFR promising for clinical use.

Fractional Flow Reserve Derived from Coronary Computed Tomography Angiography Datasets: The Next Frontier in Noninvasive Assessment of Coronary Artery Disease

Fractional flow reserve (FFR) derived from coronary CTA datasets (FFR_CT) is a major advance in cardiovascular imaging that provides critical information to the Heart Team without exposing the patient to excessive risk. Previously, invasive FFR measurements obtained during a cardiac catheterization have been demonstrated to reduce contrast use, number of stents, and cost of care and improve outcomes. However, there are barriers to routine use of FFR in the cardiac catheterization suite. FFR_CT values are obtained using resting 3D coronary CTA images using computational fluid dynamics. Several multicenter clinical trials have demonstrated the diagnostic superiority of FFR_CT over traditional coronary CTA for the diagnosis of functionally significant coronary artery disease. This review provides a background of FFR, technical aspects of FFR_CT, clinical applications and interpretation of FFR_CT values, clinical trial data, and future directions of the technology.

Cardiac Motion Correction for Helical CT Scan With an Ordinary Pitch

Cardiac X-ray computed tomography (CT) imaging is still challenging due to the cardiac motion during CT scanning, which leads to the presence of motion artifacts in the reconstructed image. In response, many cardiac X-ray CT imaging algorithms have been proposed, based on motion estimation (ME) and motion compensation (MC), to improve the image quality by alleviating the motion artifacts in the reconstructed image. However, these ME/MC algorithms are mainly based on an axial scan or a low-pitch helical scan. In this paper, we propose a ME/MC-based cardiac imaging algorithm for the data set acquired from a helical scan with an ordinary pitch of around 1.0 so as to obtain the whole cardiac image within a single scan of short time without ECG gating. In the proposed algorithm, a sequence of partial angle reconstructed (PAR) images is generated by using consecutive parts of the sinogram, each of which has a small angular span. Subsequently, an initial 4-D motion vector field (MVF) is obtained using multiple pairs of conjugate PAR images. The 4-D MVF is then refined based on an image quality metric so as to improve the quality of the motion-compensated image. Finally, a time-resolved cardiac image is obtained by performing motion-compensated image reconstruction by using the refined 4-D MVF. Using digital XCAT phantom data sets and a human data set commonly obtained via a helical scan with a pitch of 1.0, we demonstrate that the proposed algorithm significantly improves the image quality by alleviating motion artifacts.

Coronary CT Angiography-derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling

Purpose To compare two technical approaches for determination of coronary computed tomography (CT) angiography-derived fractional flow reserve (FFR)-FFR derived from coronary CT angiography based on computational fluid dynamics (hereafter, FFR_CFD) and FFR derived from coronary CT angiography based on machine learning algorithm (hereafter, FFR_ML)-against coronary CT angiography and quantitative coronary angiography (QCA). Materials and Methods A total of 85 patients (mean age, 62 years ± 11 [standard deviation]; 62% men) who had undergone coronary CT angiography followed by invasive FFR were included in this single-center retrospective study. FFR values were derived on-site from coronary CT angiography data sets by using both FFR_CFD and FFR_ML. The performance of both techniques for detecting lesion-specific ischemia was compared against visual stenosis grading at coronary CT angiography, QCA, and invasive FFR as the reference standard. Results On a per-lesion and per-patient level, FFR_ML showed a sensitivity of 79% and 90% and a specificity of 94% and 95%, respectively, for detecting lesion-specific ischemia. Meanwhile, FFR_CFD resulted in a sensitivity of 79% and 89% and a specificity of 93% and 93%, respectively, on a per-lesion and per-patient basis (P = .86 and P = .92). On a per-lesion level, the area under the receiver operating characteristics curve (AUC) of 0.89 for FFR_ML and 0.89 for FFR_CFD showed significantly higher discriminatory power for detecting lesion-specific ischemia compared with that of coronary CT angiography (AUC, 0.61) and QCA (AUC, 0.69) (all P < .0001). Also, on a per-patient level, FFR_ML (AUC, 0.91) and FFR_CFD (AUC, 0.91) performed significantly better than did coronary CT angiography (AUC, 0.65) and QCA (AUC, 0.68) (all P < .0001). Processing time for FFR_ML was significantly shorter compared with that of FFR_CFD (40.5 minutes ± 6.3 vs 43.4 minutes ± 7.1; P = .042). Conclusion The FFR_ML algorithm performs equally in detecting lesion-specific ischemia when compared with the FFR_CFD approach. Both methods outperform accuracy of coronary CT angiography and QCA in the detection of flow-limiting stenosis.

Estimating the accuracy of a reduced-order model for the calculation of fractional flow reserve (FFR)

Image-based noninvasive fractional flow reserve (FFR) is an emergent approach to determine the functional relevance of coronary stenoses. The present work aimed to determine the feasibility of using a method based on coronary computed tomography angiography (CCTA) and reduced-order models (0D-1D) for the evaluation of coronary stenoses. The reduced-order methodology (cFFR_RO ) was kept as simple as possible and did not include pressure drop or stenosis models. The geometry definition was incorporated into the physical model used to solve coronary flow and pressure. cFFRRO was assessed on a virtual cohort of 30 coronary artery stenoses in 25 vessels and compared with a standard approach based on 3D computational fluid dynamics (cFFR_3D ). In this proof-of-concept study, we sought to investigate the influence of geometry and boundary conditions on the agreement between both methods. Performance on a per-vessel level showed a good correlation between both methods (Pearson's product-moment R=0.885, P<0.01), when using cFFR_3D  as the reference standard. The 95% limits of agreement were -0.116 and 0.08, and the mean bias was -0.018 (SD =0.05). Our results suggest no appreciable difference between cFFR_RO  and cFFR_3D with respect to lesion length and/or aspect ratio. At a fixed aspect ratio, however, stenosis severity and shape appeared to be the most critical factors accounting for differences in both methods. Despite the assumptions inherent to the 1D formulation, asymmetry did not seem to affect the agreement. The choice of boundary conditions is critical in obtaining a functionally significant drop in pressure. Our initial data suggest that this approach may be part of a broader risk assessment strategy aimed at increasing the diagnostic yield of cardiac catheterisation for in-hospital evaluation of haemodynamically significant stenoses.

Fractional flow reserve in 2017: current data and everyday practice

INTRODUCTION

Fractional flow reserve (FFR) is an objective physiological index utilized in coronary angiography. It expresses the blood flow in the presence of a coronary artery stenosis as a fraction of the normal blood flow and gives information regarding the functional significance of the lesion. FFR guided percutaneous coronary intervention (PCI) has been shown to be superior to angiography guided PCI in several trials and registries. In addition, it appears that the use of FFR may also be preferable from an economic perspective. Areas covered: This article will cover the physiological principles underpinning FFR, the landmark clinical trials that have established its diagnostic utility and the current recommendations for the use of the procedure in daily practice. We will also examine potential future directions for the technology and try to predict how its use will evolve in the next five years. Expert commentary: We see FFR as an essential diagnostic tool in the modern catheterization laboratory, enabling physicians to make optimal decisions regarding percutaneous coronary intervention for an individual patient. It must be stated however that FFR is an adjunctive invasive functional tool that must be used in conjunction with sensible clinical history and exam findings pertaining to the individual patient. We expect that the results of FAME3 will further establish the role of FFR in risk stratifying patients with 3 vessel disease by utilizing a functional SYNTAX score.

Myocardial blood flow quantification for evaluation of coronary artery disease by computed tomography

During the last decade coronary computed tomography angiography (CTA) has become the preeminent non-invasive imaging modality to detect coronary artery disease (CAD) with high accuracy. However, CTA has a limited value in assessing the hemodynamic significance of a given stenosis due to a modest specificity and positive predictive value. In recent years, different CT techniques for detecting myocardial ischemia have emerged, such as CT-derived fractional flow reserve (FFR-CT), transluminal attenuation gradient (TAG), and myocardial CT perfusion (CTP) imaging. Myocardial CTP imaging can be performed with a single static scan during first pass of the contrast agent, with monoenergetic or dual-energy acquisition, or as a dynamic, time-resolved scan during stress by using coronary vasodilator agents (adenosine, dipyridamole, or regadenoson). A number of CTP techniques are available, which can assess myocardial perfusion in both a qualitative, semi-quantitative or quantitative manner. Once used primarily as research tools, these modalities are increasingly being used in routine clinical practice. All these techniques offer the substantial advantage of combining anatomical and functional evaluation of flow-limiting coronary stenosis in the same examination that would be beneficial for clinical decision-making. This review focuses on the state-of the-art and future trends of these evolving imaging modalities in the field of cardiology for the physiologic assessments of CAD.

Invasive or non-invasive imaging for detecting high-risk coronary lesions?

INTRODUCTION

Advances in our understanding about atherosclerotic evolution have enabled us to identify specific plaque characteristics that are associated with coronary plaque vulnerability and cardiovascular events. With constant improvements in signal and image processing an arsenal of invasive and non-invasive imaging modalities have been developed that are capable of identifying these features allowing in vivo assessment of plaque vulnerability. Areas covered: This review article presents the available and emerging imaging modalities introduced to assess plaque morphology and biology, describes the evidence from the first large scale studies that evaluated the efficacy of invasive and non-invasive imaging in detecting lesions that are likely to progress and cause cardiovascular events and discusses the potential implications of the in vivo assessment of coronary artery pathology in the clinical setting. Expert commentary: Invasive imaging, with its high resolution, and in particular hybrid intravascular imaging appears as the ideal approach to study the mechanisms regulating atherosclerotic disease progression; whereas non-invasive imaging is expected to enable complete assessment of coronary tree pathology, detection of high-risk lesions, more accurate risk stratification and thus to allow a personalized treatment of vulnerable patients.

Functional Evaluation of Coronary Disease by CT Angiography

In recent years, several technical developments in the field of cardiac computed tomography (CT) have made possible the extraction of functional information from an anatomy-based examination. Several different lines have been explored and will be reviewed in the present paper, namely: 1) myocardial perfusion imaging; 2) transluminal attenuation gradients and corrected coronary opacification indexes; 3) fractional flow reserve computed from CT; and 4) extrapolation from atherosclerotic plaque characteristics. In view of these developments, cardiac CT has the potential to become in the near future a truly 2-in-1 noninvasive evaluation for coronary artery disease.

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.

Fractional flow reserve based on computed tomography: an overview

Computed tomography coronary angiography (CTCA) is a technique proved to provide high sensitivity and negative predictive value for the identification of anatomically significant coronary artery disease (CAD) when compared with invasive X-ray coronary angiography. While the CTCA limitation of a ionizing radiation dose delivered to patients is substantially overcome by recent technical innovations, a relevant limitation remains the only anatomical assessment of coronary stenoses in the absence of evaluation of their functional haemodynamic significance. This limitation is highly important for those stenosis graded as intermediate at the anatomical assessment. Recently, non-invasive methods based on computational fluid dynamics were developed to calculate vessel-specific fractional flow reserve (FFR) using data routinely acquired by CTCA [computed tomographic fractional flow reserve (CT-FFR)]. Here we summarize methods for CT-FFR and review the evidence available in the literature up to June 26, 2016, including 16 original articles and one meta-analysis. The perspective of CT-FFR may greatly impact on CAD diagnosis, prognostic evaluation, and treatment decision-making. The aim of this review is to describe technical characteristics and clinical applications of CT-FFR, also in comparison with catheter-based invasive FFR, in order to make a cost-benefit balance in terms of clinical management and patient's health.

Coronary CT angiography derived morphological and functional quantitative plaque markers correlated with invasive fractional flow reserve for detecting hemodynamically significant stenosis

OBJECTIVE

Compare morphological and functional coronary plaque markers derived from coronary CT angiography (CCTA) for their ability to detect lesion-specific ischemia.

MATERIALS AND METHODS

Data of patients who had undergone both dual-source CCTA and invasive fractional flow reserve (FFR) measurement within 3 months were retrospectively analyzed. Various quantitative stenosis markers were derived from CCTA: Corrected coronary opacification (CCO), transluminal attenuation gradient (TAG), remodeling index (RI), computational FFR (cFFR), lesion length (LL), vessel volume (VV), total plaque volume (TPV), and calcified and non-calcified plaque volume (CPV and NCPV). Discriminatory power of these markers for flow-limiting versus non-significant coronary stenosis was assessed against invasive FFR as the reference standard.

RESULTS

The cohort included 37 patients (61 ± 12 years, 68% male). Among 37 lesions, 11 were hemodynamically significant by FFR. On a per-lesion level, sensitivity and specificity of TPV, CPV, and NCPV for hemodynamically significant stenosis detection were 88% and 74%, 67% and 53%, and 92% and 81%, respectively. For CCO, TAG, RI, and cFFR these were 64% and 86%, 35% and 56%, 82% and 54%, and 100% and 90%, respectively. At ROC analysis, only TPV (0.78, p = 0.013), NCPV (0.79, p = 0.009), cFFR (0.85, p = 0.003), and CCO (0.82, p = 0.0003) showed discriminatory power for detecting hemodynamically significant stenosis.

CONCLUSION

TPV, NCPV, CCO, and cFFR derived from CCTA can aid detecting hemodynamically significant coronary lesions with cFFR showing the greatest discriminatory ability.

Beyond Stenosis With Fractional Flow Reserve Via Computed Tomography and Advanced Plaque Analyses for the Diagnosis of Lesion-Specific Ischemia

In the treatment of stable coronary artery disease (CAD), the determination of stenosis severity by invasive coronary angiography (ICA) is a critical procedure, and for borderline lesions, the detection of ischemia through invasive fractional flow reserve (FFR) is the gold standard. With advances in computational fluid dynamics, FFR can now be calculated noninvasively using anatomic data from coronary computed tomographic angiography (CCTA). This technique is known as FFR_CT. The purpose of this review is to summarize the science of FFR_CT, describe its diagnostic accuracy, discuss its clinical and economic impact, and elucidate factors beyond stenosis severity that may mechanistically relate to lesion-specific ischemia. These factors include adverse atherosclerotic plaque characteristics such as positive remodelling, low-attenuation plaque, and spotty calcification, as well as aggregate plaque volume. These factors can be appreciated noninvasively by CCTA but not by ICA. The diagnostic accuracy of FFR_CT, compared with the gold standard of FFR, has been validated in 3 prospective multicentre blinded core laboratory-controlled trials, and as a result FFR_CT has been approved by the US Food and Drug Administration for clinical use. FFR_CT has also been shown in a clinical utility trial to better identify patients without obstructive CAD when compared with standard noninvasive assessment of stable CAD, thereby avoiding unnecessary angiograms. In addition, the use of FFR_CT has been shown to allow for a significant cost savings compared with traditional care. It is therefore important for cardiologists to appreciate the value of this important new methodology.