Software-based on-site estimation of fractional flow reserve using standard coronary CT angiography data

Intra-individual comparison of coronary CT angiography-based FFR between energy-integrating and photon-counting detector CT systems

BACKGROUND

Coronary computed tomography angiography (CCTA)-based fractional flow reserve (CT-FFR) allows for noninvasive determination of the functional severity of anatomic lesions in patients with coronary artery disease. The aim of this study was to intra-individually compare CT-FFR between photon-counting detector (PCD) and conventional energy-integrating detector (EID) CT systems.

METHODS

In this single-center prospective study, subjects who underwent clinically indicated CCTA on an EID-CT system were recruited for a research CCTA on PCD-CT within 30 days. Image reconstruction settings were matched as closely as possible between EID-CT (Bv36 kernel, iterative reconstruction strength level 3, slice thickness 0.5 mm) and PCD-CT (Bv36 kernel, quantum iterative reconstruction level 3, virtual monoenergetic level 55 keV, slice thickness 0.6 mm). CT-FFR was measured semi-automatically using a prototype on-site machine learning algorithm by two readers. CT-FFR analysis was performed per-patient and per-vessel, and a CT-FFR ≤ 0.75 was considered hemodynamically significant.

RESULTS

A total of 22 patients (63.3 ± 9.2 years; 7 women) were included. Median time between EID-CT and PCD-CT was 5.5 days. Comparison of CT-FFR values showed no significant difference and strong agreement between EID-CT and PCD-CT in the per-vessel analysis (0.88 [0.74-0.94] vs. 0.87 [0.76-0.93], P = 0.096, mean bias 0.02, limits of agreement [LoA] -0.14/0.19, r = 0.83, ICC = 0.92), and in the per-patient analysis (0.81 [0.60-0.86] vs. 0.76 [0.64-0.86], P = 0.768, mean bias 0.02, LoA -0.15/0.19, r = 0.90, ICC = 0.93). All included patients were classified into the same category (CT-FFR > 0.75 vs ≤0.75) with both CT systems.

CONCLUSIONS

CT-FFR evaluation is feasible with PCD-CT and it shows a strong agreement with EID-CT-based evaluation when images are similarly reconstructed.

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.

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.

Deep learning prediction of quantitative coronary angiography values using myocardial perfusion images with a CZT camera

PURPOSE

Evaluate the prediction of quantitative coronary angiography (QCA) values from MPI, by means of deep learning.

METHODS

546 patients (67% men) undergoing stress 99mTc-tetrofosmin MPI in a CZT camera in the upright and supine position were included (1092 MPIs). Patients were divided into two groups: ICA group included 271 patients who performed an ICA within 6 months of MPI and a control group with 275 patients with low pre-test probability for CAD and a normal MPI. QCA analyses were performed using radiologic software and verified by an expert reader. Left ventricular myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. A deep learning model was trained using a double cross-validation scheme such that all data could be used as test data as well.

RESULTS

Area under the receiver-operating characteristic curve for the prediction of QCA, with > 50% narrowing of the artery, by deep learning for the external test cohort: per patient 85% [95% confidence interval (CI) 84%-87%] and per vessel; LAD 74% (CI 72%-76%), RCA 85% (CI 83%-86%), LCx 81% (CI 78%-84%), and average 80% (CI 77%-83%).

CONCLUSION

Deep learning can predict the presence of different QCA percentages of coronary artery stenosis from MPIs.

CT Fractional Flow Reserve: A Practical Guide to Application, Interpretation, and Problem Solving

CT fractional flow reserve (FFR_CT) is a physiologic simulation technique that models coronary flow from routine coronary CT angiography (CTA). To evaluate lesion-specific ischemia, FFR_CT is measured 2 cm distal to a stenotic lesion. FFR_CT greater than 0.8 is normal, 0.76-0.8 is borderline, and 0.75 or less is abnormal. FFR_CT should always be interpreted in correlation with clinical and anatomic coronary CTA findings. FFR_CT increases the specificity of coronary CTA in the evaluation of coronary artery disease, decreases the prevalence of nonobstructive disease in invasive coronary angiography (ICA), and helps with revascularization decisions and planning. Patients with intermediate-risk coronary anatomy at CTA and abnormal FFR_CT can undergo ICA and revascularization, whereas those with normal FFR_CT can be safely deferred from ICA. In borderline FFR_CT values, management is decided in the context of the clinical scenario, but many cases could be safely managed with medical treatment. There are some limitations and pitfalls of FFR_CT. Abnormal FFR_CT values can be seen in mild stenosis, and normal FFR_CTvalues can be seen in severe stenosis. Gradually decreasing or abnormal low FFR_CT values at the distal vessel without a proximal focal lesion could be due to diffuse atherosclerosis. Coronary stents, bypass grafts, coronary anomalies, coronary dissection, transcatheter aortic valve replacement, unstable angina, and acute or recent myocardial infarction are situations in which FFR_CT has not been validated and should not be used at this time. The authors provide a practical guide to the applications and interpretation of FFR_CT, focusing on common pitfalls and challenges. Online supplemental material is available for this article. ^©RSNA, 2022.

Diagnostic accuracy of on-site coronary computed tomography-derived fractional flow reserve in the diagnosis of stable coronary artery disease

PURPOSE

Invasive fractional flow reserve (FFR), the reference standard for identifying significant coronary artery disease (CAD), can be estimated non-invasively by computed tomography-derived fractional flow reserve (CT-FFR). Commercially available off-site CT-FFR showed improved diagnostic accuracy compared to coronary computed tomography angiography (CCTA) alone. However, the diagnostic performance of this lumped-parameter on-site method is unknown. The aim of this cross-sectional study was to determine the diagnostic accuracy of on-site CT-FFR in patients with suspected CAD.

METHODS

A total of 61 patients underwent CCTA and invasive coronary angiography with FFR measured in 88 vessels. Significant CAD was defined as FFR and CT-FFR below 0.80. CCTA with stenosis above 50% was regarded as significant CAD. The diagnostic performance of both CT-FFR and CCTA was assessed using invasive FFR as the reference standard.

RESULTS

Of the 88 vessels included in the analysis, 34 had an FFR of ≤ 0.80. On a per-vessel basis, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 91.2%, 81.4%, 93.6%, 75.6% and 85.2% for CT-FFR and were 94.1%, 68.5%, 94.9%, 65.3% and 78.4% for CCTA. The area under the receiver operating characteristic curve was 0.91 and 0.85 for CT-FFR and CCTA, respectively, on a per-vessel basis.

CONCLUSION

On-site non-invasive FFR derived from CCTA improves diagnostic accuracy compared to CCTA without additional testing and has the potential to be integrated in the current clinical work-up for diagnosing stable CAD.

Incremental Diagnostic Value of CT Fractional Flow Reserve Using Subtraction Method in Patients with Severe Calcification: A Pilot Study

Although on-site workstation-based CT fractional flow reserve (CT-FFR) is an emerging method for assessing vessel-specific ischemia in coronary artery disease, severe calcification is a significant factor affecting CT-FFR’s diagnostic performance. The subtraction method significantly improves the diagnostic value with respect to anatomic stenosis for patients with severe calcification in coronary CT angiography (CCTA). We evaluated the diagnostic capability of CT-FFR using the subtraction method (subtraction CT-FFR) in patients with severe calcification. This study included 32 patients with 45 lesions with severe calcification (Agatston score >400) who underwent both CCTA and subtraction CCTA using 320-row area detector CT and also received invasive FFR within 90 days. The diagnostic capabilities of CT-FFR and subtraction CT-FFR were compared. The sensitivities, specificities, positive predictive values (PPVs), and negative predictive values (NPVs) of CT-FFR vs. subtraction CT-FFR for detecting hemodynamically significant stenosis, defined as FFR ≤ 0.8, were 84.6% vs. 92.3%, 59.4% vs. 75.0%, 45.8% vs. 60.0%, and 90.5% vs. 96.0%, respectively. The area under the curve for subtraction CT-FFR was significantly higher than for CT-FFR (0.84 vs. 0.70) (p = 0.04). The inter-observer and intra-observer variabilities of subtraction CT-FFR were 0.76 and 0.75, respectively. In patients with severe calcification, subtraction CT-FFR had an incremental diagnostic value over CT-FFR, increasing the specificity and PPV while maintaining the sensitivity and NPV with high reproducibility.

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.

Diagnostic performance of computed tomography-based fraction flow reserve in identifying myocardial ischemia caused by coronary artery stenosis: A meta-analysis

BACKGROUND

As a new noninvasive diagnostic technique, computed tomography (CT)-based fraction flow reserve (FFR) has been used to identify hemodynamically significant coronary artery stenosis. This meta-analysis used invasive FFR as the standard to evaluate the diagnostic performance of FFRCT.

METHODS

We searched the PubMed, Cochrane library, and EMBASE for articles published between January 2009 and January 2021. The synthesized sensitivity and specificity of invasive FFR and FFRCT were analyzed at both the patient and vessel levels. We generated a summary receiver operating characteristic curve (SROC) and then calculated the area under the curve (AUC).

RESULTS

We included a total of 23 studies, including 2,178 patients and 3,029 vessels or lesions. Analysis at each patient level demonstrated a synthesized sensitivity of 88%, specificity of 79%, LR+ of 4.16, LR-of 0.15, and AUC of 0.89 for FFRCT. Analysis at the level of each vessel or lesion showed a synthesized sensitivity of 85%, specificity of 81%, LR+ of 4.44, LR-of 0.19, and AUC of 0.87 for FFRCT.

CONCLUSION

Our research reveals that FFRCT has high diagnostic performance in patients with coronary artery stenosis, regardless of whether it is at the patient level or the vessel level.

Clinical application of computed tomography angiography and fractional flow reserve computed tomography in patients with coronary artery disease: A meta-analysis based on pre- and post-test probability

PURPOSE

To assess the diagnostic role of coronary computed tomography angiography (CCTA) and fractional flow reserve computed tomography (FFRCT) in confirming or excluding ischemic coronary artery disease (CAD) and to provide a rational use of CCTA and FFRCT in different pre-test probability (PTP) of CAD.

METHODS

We searched the electronic databases from the earliest relevant literature to July 2020 comparing FFRCT or CCTA with FFR. The bivariate random-effects models and Bayes' theorem were used to investigate the diagnostic performance of CCTA and FFRCT with the sensitivity, specificity, pre- and post-test probability.

RESULTS

Fifty-three articles with 4817 patients and 7026 vessels finally met our inclusion criteria. At the patient level, the sensitivity and specificity of CCTA were (0.94, 0.89-0.97), and (0.50, 0.43-0.58), respectively. For FFRCT, the sensitivity and specificity were (0.90, 0.87-0.93) and (0.81, 0.73-0.87). CCTA or FFRCT could increase the post-test probability to >85 % in patients with a PTP > 74.9 % or 54.5 %; CCTA or FFRCT could decrease the post-test probability to <15 % in patients with a pre-test probability <61.3 % or 59.3 %.

CONCLUSIONS

In patients with low to intermediate PTP, CCTA is suggested to exclude CAD, while the time-consuming calculation of FFRCT may be unnecessary. If CCTA detects significant or uncertain stenosis with intermediate to high PTP of CAD, further FFRCT is suggested. The advantages of FFRCT for guiding CAD treatment have sufficiently been demonstrated.

Functional cardiac CT-Going beyond Anatomical Evaluation of Coronary Artery Disease with Cine CT, CT-FFR, CT Perfusion and Machine Learning

The aim of this review is to provide an overview of different functional cardiac CT techniques which can be used to supplement assessment of the coronary arteries to establish the significance of coronary artery stenoses. We focus on cine-CT, CT-FFR, CT-myocardial perfusion and how developments in machine learning can supplement these techniques.

Advances in Cardiac Computed Tomography Functional Imaging Technology

Cardiovascular disease (CVD) is the leading cause of death among patients in China, and cardiac computed tomography (CT) is one of the most commonly used examination methods for CVD. Coronary artery CT angiography can be used for the morphologic evaluation of the coronary artery. At present, cardiac CT functional imaging has become an important direction of development of CT. At present, common CT functional imaging technologies include transluminal attenuation gradient, stress dynamic CT myocardial perfusion imaging, and CT-fractional flow reserve. These three imaging modes are introduced and analyzed in this review.

Computed tomographic evaluation of myocardial ischemia

Myocardial ischemia is caused by a mismatch between myocardial oxygen consumption and oxygen delivery in coronary artery disease (CAD). Stratification and decision-making based on ischemia improves the prognosis in patients with CAD. Non-invasive tests used to evaluate myocardial ischemia include stress electrocardiography, echocardiography, single-photon emission computed tomography, and magnetic resonance imaging. Invasive fractional flow reserve is considered the reference standard for assessment of the hemodynamic significance of CAD. Computed tomography (CT) angiography has emerged as a first-line imaging modality for evaluation of CAD, particularly in the population at low to intermediate risk, because of its high negative predictive value; however, CT angiography does not provide information on the hemodynamic significance of stenosis, which lowers its specificity. Emerging techniques, e.g., CT perfusion and CT-fractional flow reserve, help to address this limitation of CT, by determining the hemodynamic significance of coronary artery stenosis. CT perfusion involves acquisition during the first pass of contrast medium through the myocardium following pharmacological stress. CT-fractional flow reserve uses computational fluid dynamics to model coronary flow, pressure, and resistance. In this article, we review these two functional CT techniques in the evaluation of myocardial ischemia, including their principles, technology, advantages, limitations, pitfalls, and the current evidence.

Multimodal Multiparametric Three-dimensional Image Fusion in Coronary Artery Disease: Combining the Best of Two Worlds

PURPOSE

To allow for comprehensive noninvasive diagnostics of coronary artery disease (CAD) by using three-dimensional (3D) image fusion of CT coronary angiography, CT-derived fractional flow reserve (CT FFR), whole-heart dynamic 3D cardiac MRI perfusion, and 3D cardiac MRI late gadolinium enhancement (LGE).

MATERIALS AND METHODS

Seventeen patients (54 years ± 10 [standard deviation], one female) who underwent cardiac CT and cardiac MRI were included (combined subcohort of three prospective trials). Software facilitating multimodal 3D image fusion was developed. Postprocessing of CT data included segmentation of the coronary tree and heart contours, calculation of CT FFR values, and color coding of the coronary tree according to CT FFR. Postprocessing of cardiac MRI data included segmentation of the left ventricle (LV) in cardiac MRI perfusion and cardiac MRI LGE, co-registration of cardiac MRI to CT data, and projection of cardiac MRI perfusion and LGE values onto the high spatial resolution LV from CT.

RESULTS

Image quality was rated as good to excellent (scores: 2.5-2.6; 3 = excellent). CT coronary angiography revealed significant stenoses in seven of 17 cases (41%). CT FFR was possible in 16 of 17 cases (94%) and showed pathologic flow in seven of 17 cases (41%), six of which coincided with cases revealing significant stenoses at CT coronary angiography. Cardiac MRI perfusion identified eight of 17 patients (47%) with hypoperfusion (ischemic burden of 17% ± 5). Cardiac MRI LGE showed myocardial scar in three of 17 cases (18%, scar burden of 7% ± 4). Conventional two-dimensional readout of CT coronary angiography and cardiac MRI resulted in eight of 17 cases (47%) with uncertain findings. Most of these divergent findings could be solved when adding information from CT FFR and 3D image fusion (six of eight, 75%).

CONCLUSION

Multimodal 3D cardiac image fusion is feasible and may help with comprehensive noninvasive CAD diagnostics.Supplemental material is available for this article.© RSNA, 2020.

Diagnostic Performance of On-Site Coronary CT Angiography-derived Fractional Flow Reserve Based on Patient-specific Lumped Parameter Models

Purpose

To evaluate the diagnostic performance of a prototype on-site coronary CT angiography-derived fractional flow reserve (CT FFR) algorithm, based on patient-specific lumped parameter models, for the detection of functionally significant stenosis defined by invasive FFR, and to compare the performance to anatomic evaluation of stenosis degree.

Materials and Methods

In this retrospective feasibility study, 77 vessels in 57 patients (42 of 57 [74%]) men; mean age, 58.5 years ± 9.2 [standard deviation]) who underwent clinically indicated coronary CT angiography within 60 days prior to an invasive FFR measurement were analyzed. Invasive FFR less than or equal to 0.80 was used to indicate a functionally significant stenosis. Diagnostic performance of CT FFR was evaluated and compared with evaluation of stenosis degree. Analysis was performed on a per-vessel basis.

Results

Invasive FFR revealed functionally significant stenoses in 37 vessels (48%). CT FFR showed a significantly increased ability to indicate functionally significant stenosis (area under the receiver operating characteristic curve [AUC], 0.87) compared with degree of stenosis at coronary CT angiography (AUC, 0.70; ΔAUC 0.17; P < .01). Using a cutoff of less than or equal to 0.80 for CT FFR and greater than or equal to 50% degree of stenosis at coronary CT angiography to indicate a significant stenosis, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 33 of 37 (89.2%), 31 of 40 (77.5%), 33 of 42 (78.6%), 31 of 35 (88.6%), and 64 of 77 (83.1%), respectively, for CT FFR, and 33 of 37 (89.2%), 17 of 40 (42.5%), 33 of 56 (58.9%), 17 of 21 (81.0%), and 50 of 77 (64.9%), respectively, for degree of stenosis at coronary CT angiography.

Conclusion

Diagnostic performance of on-site CT FFR was superior to stenosis evaluation at coronary CT angiography for identification of functionally significant coronary artery stenosis in patients suspected of having or known to have coronary artery disease.© RSNA, 2019See also commentary by Schoepf et al.

Diagnostic performance of a fast non-invasive fractional flow reserve derived from coronary CT angiography: an initial validation study

AIM

To validate the computed tomography (CT)-derived fractional flow reserve (FFR_CT) that was computed by new, fast, automatic software and to compare the diagnostic accuracy between FFR_CT and stenosis diagnosed at coronary CT angiography (CCTA).

MATERIALS AND METHODS

A total of 110 patients (76 males, 59±9 years) and 125 vessels underwent CCTA. FFR_CT was computed by fast automatic software and compared with invasive FFR. The diagnostic performance between FFR_CT and CCTA-diagnosed stenosis were compared on the per-patient and per-vessel level.

RESULTS

The computational time of FFR_CT is 10±5 minutes (averaged over 125 vessels). The FFR_CT has a good correlation with invasive FFR (r=0.59, p<0.0001) with a small bias of -0.02 (-0.26-0.23). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of FFR_CT were 76.5, 89.5, 89.7, and 76.1% on a vessel level. The area under the receiver operating characteristic curve of FFR_CT was higher than CCTA-diagnosed stenosis (0.82 versus 0.72, P=0.034).

CONCLUSION

The computation of FFR_CT is possible and reliable when using the new, fast, automatic software first employed in the present clinical study. The FFR_CT has a good correlation with invasive FFR and provides better diagnostic performance than CCTA-diagnosed stenosis.

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).

Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result From the MACHINE Consortium

BACKGROUND

Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necessarily imply hemodynamic relevance when fractional flow reserve (FFR) is used as reference. CTA-based FFR (CT-FFR), using computational fluid dynamics (CFD), improves the correlation with invasive FFR results but is computationally demanding. More recently, a new machine-learning (ML) CT-FFR algorithm has been developed based on a deep learning model, which can be performed on a regular workstation. In this large multicenter cohort, the diagnostic performance ML-based CT-FFR was compared with CTA and CFD-based CT-FFR for detection of functionally obstructive coronary artery disease.

METHODS AND RESULTS

At 5 centers in Europe, Asia, and the United States, 351 patients, including 525 vessels with invasive FFR comparison, were included. ML-based and CFD-based CT-FFR were performed on the CTA data, and diagnostic performance was evaluated using invasive FFR as reference. Correlation between ML-based and CFD-based CT-FFR was excellent (R=0.997). ML-based (area under curve, 0.84) and CFD-based CT-FFR (0.84) outperformed visual CTA (0.69; P<0.0001). On a per-vessel basis, diagnostic accuracy improved from 58% (95% confidence interval, 54%-63%) by CTA to 78% (75%-82%) by ML-based CT-FFR. The per-patient accuracy improved from 71% (66%-76%) by CTA to 85% (81%-89%) by adding ML-based CT-FFR as 62 of 85 (73%) false-positive CTA results could be correctly reclassified by adding ML-based CT-FFR.

CONCLUSIONS

On-site CT-FFR based on ML improves the performance of CTA by correctly reclassifying hemodynamically nonsignificant stenosis and performs equally well as CFD-based CT-FFR.

Effect of Tube Voltage on Diagnostic Performance of Fractional Flow Reserve Derived From Coronary CT Angiography With Machine Learning: Results From the MACHINE Registry

OBJECTIVE. Coronary CT angiography (CCTA)-based methods allow noninvasive estimation of fractional flow reserve (cFFR), recently through use of a machine learning (ML) algorithm (cFFR_ML). However, attenuation values vary according to the tube voltage used, and it has not been shown whether this significantly affects the diagnostic performance of cFFR and cFFR_ML. Therefore, the purpose of this study is to retrospectively evaluate the effect of tube voltage on the diagnostic performance of cFFR_ML. MATERIALS AND METHODS. A total of 525 coronary vessels in 351 patients identified in the MACHINE consortium registry were evaluated in terms of invasively measured FFR and cFFR_ML. CCTA examinations were performed with a tube voltage of 80, 100, or 120 kVp. For each tube voltage value, correlation (assessed by Spearman rank correlation coefficient), agreement (evaluated by intraclass correlation coefficient and Bland-Altman plot analysis), and diagnostic performance (based on ROC AUC value, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) of the cFFR_ML in terms of detection of significant stenosis were calculated. RESULTS. For tube voltages of 80, 100, and 120 kVp, the Spearman correlation coefficient for cFFR_ML in relation to the invasively measured FFR value was ρ = 0.684, ρ = 0.622, and ρ = 0.669, respectively (p < 0.001 for all). The corresponding intraclass correlation coefficient was 0.78, 0.76, and 0.77, respectively (p < 0.001 for all). Sensitivity was 100.0%, 73.5%, and 85.0%, and specificity was 76.2%, 79.0%, and 72.8% for tube voltages of 80, 100, and 120 kVp, respectively. The ROC AUC value was 0.90, 0.82, and 0.80 for 80, 100, and 120 kVp, respectively (p < 0.001 for all). CONCLUSION. CCTA-derived cFFR_ML is a robust method, and its performance does not vary significantly between examinations performed using tube voltages of 100 kVp and 120 kVp. However, because of rapid advancements in CT and postprocessing technology, further research is needed.

FFRCT derived from computed tomography angiography: the experience in the UK

INTRODUCTION

Non-invasive fractional flow reserve derived from CT coronary angiography (FFR_CT) represents a novel technology to investigate coronary artery disease. The application of computational flow dynamics to anatomical data provides the clinician with a further functional assessment to inform decision-making in patients with coronary artery disease. In the UK FFR_CT has received medical technology approval for use since February 2017. Areas covered: This article discusses the mathematical and physiological principles underpinning calculation of non-invasive fractional flow reserve (FFR), as well as discussing the differences between the commercially available technologies. Diagnostic accuracy, cost effectiveness and safety of non-invasive FFR from the early clinical trials is examined. Further to this the potential implications of the use of non-invasive FFR in clinical practice in the UK are discussed. Expert commentary: Non-invasive FFR represents a promising comprehensive imaging technology providing both anatomical and physiological data to accurately diagnose obstructive coronary artery disease. The technology has yet to prove to be cost effective in 'real world' cohorts before becoming integrated into everyday clinical practice and guidelines in the United Kingdom.

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.

A new CFD based non-invasive method for functional diagnosis of coronary stenosis

Background

Accurate functional diagnosis of coronary stenosis is vital for decision making in coronary revascularization. With recent advances in computational fluid dynamics (CFD), fractional flow reserve (FFR) can be derived non-invasively from coronary computed tomography angiography images (FFR_CT) for functional measurement of stenosis. However, the accuracy of FFR_CT is limited due to the approximate modeling approach of maximal hyperemia conditions. To overcome this problem, a new CFD based non-invasive method is proposed.

Methods

Instead of modeling maximal hyperemia condition, a series of boundary conditions are specified and those simulated results are combined to provide a pressure-flow curve for a stenosis. Then, functional diagnosis of stenosis is assessed based on parameters derived from the obtained pressure-flow curve.

Results

The proposed method is applied to both idealized and patient-specific models, and validated with invasive FFR in six patients. Results show that additional hemodynamic information about the flow resistances of a stenosis is provided, which cannot be directly obtained from anatomy information. Parameters derived from the simulated pressure-flow curve show a linear and significant correlations with invasive FFR (r > 0.95, P < 0.05).

Conclusion

The proposed method can assess flow resistances by the pressure-flow curve derived parameters without modeling of maximal hyperemia condition, which is a new promising approach for non-invasive functional assessment of coronary stenosis.

Experience With an On-Site Coronary Computed Tomography-Derived Fractional Flow Reserve Algorithm for the Assessment of Intermediate Coronary Stenoses

Fractional flow reserve (FFR) derived from coronary computed tomography angiography (CTA) is a new technique for the diagnosis of ischemic coronary artery stenoses. The aim of this prospective study was to evaluate the diagnostic performance of a novel on-site computed tomography-based fractional flow reserve algorithm (CT-FFR) compared with invasive FFR as the gold standard, and to determine whether its diagnostic performance is affected by interobserver variations in lumen segmentation. We enrolled 44 consecutive patients (64.6 ± 8.9 years, 34% female) with 60 coronary atherosclerotic lesions who underwent coronary CTA and invasive coronary angiography in 2 centers. An FFR value ≤0.8 was considered significant. Coronary CTA scans were evaluated by 2 expert readers, who manually adjusted the semiautomated coronary lumen segmentations for effective diameter stenosis (EDS) assessment and on-site CT-FFR simulation. The mean CT-FFR value was 0.77 ± 0.15, whereas the mean EDS was 43.6 ± 16.9%. The sensitivity, specificity, positive predictive value, and negative predictive value of CT-FFR versus EDS with a cutoff of 50% were the following: 91%, 72%, 63%, and 93% versus 52%, 87%, 69%, and 77%, respectively. The on-site CT-FFR demonstrated significantly better diagnostic performance compared with EDS (area under the curve 0.89 vs 0.74, respectively, p <0.001). The CT-FFR areas under the curve of the 2 readers did not show any significant difference (0.89 vs 0.88, p = 0.74). In conclusion, on-site CT-FFR simulation is feasible and has better diagnostic performance than anatomic stenosis assessment. Furthermore, the diagnostic performance of the on-site CT-FFR simulation algorithm does not depend on the readers' semiautomated lumen segmentation adjustments.

Plaque imaging with CT-a comprehensive review on coronary CT angiography based risk assessment

CT based technologies have evolved considerably in recent years. Coronary CT angiography (CTA) provides robust assessment of coronary artery disease (CAD). Early coronary CTA imaging-as a gate-keeper of invasive angiography-has focused on the presence of obstructive stenosis. Coronary CTA is currently the only non-invasive imaging modality for the evaluation of non-obstructive CAD, which has been shown to contribute to adverse cardiac events. Importantly, improved spatial resolution of CT scanners and novel image reconstruction algorithms enable the quantification and characterization of atherosclerotic plaques. State-of-the-art CT imaging can therefore reliably assess the extent of CAD and differentiate between various plaque features. Recent studies have demonstrated the incremental prognostic value of adverse plaque features over luminal stenosis. Comprehensive coronary plaque assessment holds potential to significantly improve individual risk assessment incorporating adverse plaque characteristics, the extent and severity of atherosclerotic plaque burden. As a result, several coronary CTA based composite risk scores have been proposed recently to determine patients at high risk for adverse events. Coronary CTA became a promising modality for the evaluation of functional significance of coronary lesions using CT derived fractional flow reserve (FFR-CT) and/or rest/dynamic myocardial CT perfusion. This could lead to substantial reduction in unnecessary invasive catheterization procedures and provide information on ischemic burden of CAD. Discordance between the degree of stenosis and ischemia has been recognized in clinical landmark trials using invasive FFR. Both lesion stenosis and composition are possibly related to myocardial ischemia. The evaluation of lesion-specific ischemia using combined functional and morphological plaque information could ultimately improve the diagnostic performance of CTA and thus patient care. In this review we aimed to summarize current evidence on comprehensive coronary artery plaque assessment using coronary CTA.

Myocardial ischemia testing with computed tomography: emerging strategies

Although cardiac computed tomography (CT) has high negative predictive value to exclude obstructive coronary artery disease (CAD), particularly in the low to intermediate risk population, it has low specificity in the diagnosis of ischemia-inducing lesions. This inability to predict hemodynamically significant stenosis hampers the ability of CT to be an effective gatekeeper for invasive angiography and to guide appropriate revascularization. Recent advances in CT technology have resulted in the development of multiple techniques to provide hemodynamic information and detect lesion-specific ischemia, namely CT perfusion (CTP), CT-derived fractional flow reserve (CT-FFR) and coronary transluminal attenuation gradient (TAG). In this article, we provide a perspective on these emerging CT techniques in the evaluation of myocardial ischemia.

Fractional Flow Reserve Measurement by Computed Tomography: An Alternative to the Stress Test

Recent advances in computed tomographic technology have contributed towards improving coronary computed tomography angiography (CCTA) in determining the severity of coronary artery disease anatomically. Although the viability of CCTA has most often been confined to anatomical assessment, recent development has enabled evaluation of the haemodynamic significance of coronary artery disease. In light of this, CCTA-derived fractional flow reserve (FFR_CT), a novel imaging modality, now permits the physiological assessment of coronary artery disease. To date, several studies have documented the diagnostic performance of FFR_CT, and more trials are being performed that will further substantiate this technique. The present paper provides an overview and discussion of the available evidence for FFR_CT in the clinical setting as well as potential future directions of FFR_CT.

Noninvasive fractional flow reserve derived from coronary computed tomography angiography for identification of ischemic lesions: a systematic review and meta-analysis

Detection of coronary ischemic lesions by fractional flow reserve (FFR) has been established as the gold standard. In recent years, novel computer based methods have emerged and they can provide simulation of FFR using coronary artery images acquired from coronary computed tomography angiography (FFR_CT). This meta-analysis aimed to evaluate diagnostic performance of FFR_CT using FFR as the reference standard. Databases of PubMed, Cochrane Library, EMBASE, Medion and Web of Science were searched. Seven studies met the inclusion criteria, including 833 stable patients (1377 vessels or lesions) with suspected or known coronary artery disease (CAD). The patient-based analysis showed pooled estimates of sensitivity, specificity and diagnostic odds ratio (DOR) for detection of ischemic lesions were 0.89 [95%confidence interval (CI), 0.85–0.93], 0.76 (95%CI, 0.64–0.84) and 26.21 (95%CI, 13.14–52.28). At a per-vessel or per-lesion level, the pooled estimates were as follows: sensitivity 0.84 (95%CI, 0.80–0.87), specificity 0.76 (95%CI, 0.67–0.83) and DOR 16.87 (95%CI, 9.41–30.25). Area under summary receiver operating curves was 0.90 (95%CI, 0.87–0.92) and 0.86 (95%CI, 0.83–0.89) at the two analysis levels, respectively. In conclusion, FFR_CT technology achieves a moderate diagnostic performance for noninvasive identification of ischemic lesions in stable patients with suspected or known CAD in comparison to invasive FFR measurement.