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Objectives: In this international, multicenter study, using third-generation dual-source computed tomography (CT), we investigated the diagnostic performance of dynamic stress CT myocardial perfusion imaging (CT-MPI) in addition to coronary CT angiography (CTA) compared to invasive coronary angiography (ICA) and invasive fractional flow reserve (FFR)., Background: CT-MPI combined with coronary CTA integrates coronary artery anatomy with inducible myocardial ischemia, showing promising results for the diagnosis of hemodynamically significant coronary artery disease in single-center studies., Methods: At 9 centers in Europe, Japan, and the United States, 132 patients scheduled for ICA were enrolled; 114 patients successfully completed coronary CTA, adenosine-stress dynamic CT-MPI, and ICA. Invasive FFR was performed in vessels with 25% to 90% stenosis. Data were analyzed by independent core laboratories. For the primary analysis, for each coronary artery the presence of hemodynamically significant obstruction was interpreted by coronary CTA with CT-MPI compared to coronary CTA alone, using an FFR of ≤0.80 and angiographic severity as reference. Territorial absolute myocardial blood flow (MBF) and relative MBF were compared using C-statistics., Results: ICA and FFR identified hemodynamically significant stenoses in 74 of 289 coronary vessels (26%). Coronary CTA with ≥50% stenosis demonstrated a per-vessel sensitivity, specificity, and accuracy for the detection of hemodynamically significant stenosis of 96% (95% CI: 91%-100%), 72% (95% CI: 66%-78%), and 78% (95% CI: 73%-83%), respectively. Coronary CTA with CT-MPI showed a lower sensitivity (84%; 95% CI: 75%-92%) but higher specificity (89%; 95% CI: 85%-93%) and accuracy (88%; 95% CI: 84%-92%). The areas under the receiver-operating characteristic curve of absolute MBF and relative MBF were 0.79 (95% CI: 0.71-0.86) and 0.82 (95% CI: 0.74-0.88), respectively. The median dose-length product of CT-MPI and coronary CTA were 313 mGy·cm and 138 mGy·cm, respectively., Conclusions: Dynamic CT-MPI offers incremental diagnostic value over coronary CTA alone for the identification of hemodynamically significant coronary artery disease. Generalized results from this multicenter study encourage broader consideration of dynamic CT-MPI in clinical practice. (Dynamic Stress Perfusion CT for Detection of Inducible Myocardial Ischemia [SPECIFIC]; NCT02810795)., Competing Interests: Funding Support and Author Disclosures This study was supported by unrestricted grants from Siemens Healthineers and Bayer Healthcare. Dr Nguyen’s research is supported by the National Institutes of Health (R01HL134830-01). Koen Nieman’s research is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (R01HL141712; R01HL146754). Dr Geisler has received research grants from Medtronic and Edwards Lifesciences. Dr Kitagawa has received an endowed chair position supported by Siemens Healthineers. Dr Vliegenthart has received an institutional research grant from Siemens Healthineers. Dr Hausleiter has received receiving speaker honoraria and research support from Abbott Vascular and Edwards Lifesciences; and has served as a consultant for Edwards Lifesciences. Dr Pugliese has received research support from Siemens Healthineers. Dr Budde has received institutional research support to the Erasmus MC from Siemens Healthineers. Dr Nikolauo has received research grants from Siemens Healthineers, GE Healthcare, and Bayer Healthcare; and has served as a consultant for Siemens Healthineers; and Bayer Healthcare. Dr Sakuma has received departmental research grants from FUJIFILM Toyama Chemical Co, Ltd, and Guerbet Japan KK. Dr Klotz is a retired employee of and serves as a consultant for Siemens Healthineers. Dr Bamberg has received research grants from Siemens Healthineers and Bayer Healthcare; and has served as a consultant for Siemens Healthineers, Bayer Healthcare, and Bracco. Dr Nieman has received unrestricted institutional research support from Siemens Healthineers and HeartFlow Inc; has served as a consultant for Siemens Medical Systems USA; and holds equity in Lumen Therapeutics. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Compared with invasive fractional flow reserve (FFR), coronary CT angiography (cCTA) is limited in detecting hemodynamically relevant lesions. cCTA-based FFR (CT-FFR) is an approach to overcome this insufficiency by use of computational fluid dynamics. Applying recent innovations in computer science, a machine learning (ML) method for CT-FFR derivation was introduced and showed improved diagnostic performance compared to cCTA alone. We sought to investigate the influence of stenosis location in the coronary artery system on the performance of ML-CT-FFR in a large, multicenter cohort., Methods: Three hundred and thirty patients (75.2% male, median age 63 years) with 502 coronary artery stenoses were included in this substudy of the MACHINE (Machine Learning Based CT Angiography Derived FFR: A Multi-Center Registry) registry. Correlation of ML-CT-FFR with the invasive reference standard FFR was assessed and pooled diagnostic performance of ML-CT-FFR and cCTA was determined separately for the following stenosis locations: RCA, LAD, LCX, proximal, middle, and distal vessel segments., Results: ML-CT-FFR correlated well with invasive FFR across the different stenosis locations. Per-lesion analysis revealed improved diagnostic accuracy of ML-CT-FFR compared with conventional cCTA for stenoses in the RCA (71.8% [95% confidence interval, 63.0%-79.5%] vs. 54.8% [45.7%-63.8%]), LAD (79.3 [73.9-84.0] vs. 59.6 [53.5-65.6]), LCX (84.1 [76.0-90.3] vs. 63.7 [54.1-72.6]), proximal (81.5 [74.6-87.1] vs. 63.8 [55.9-71.2]), middle (81.2 [75.7-85.9] vs. 59.4 [53.0-65.6]) and distal stenosis location (67.4 [57.0-76.6] vs. 51.6 [41.1-62.0])., Conclusion: In a multicenter cohort with high disease prevalence, ML-CT-FFR offered improved diagnostic performance over cCTA for detecting hemodynamically relevant stenoses regardless of their location., Competing Interests: Declaration of competing interest Dr. Renker has received speaker fees from Abbott. Dr. Baumann has received consulting fees from Phillips Volcano. Dr. Tesche has received research support and honoraria for speaking from Siemens. Dr. Kim received proctor/speaker fees from Abbott, Boston Scientific, Edwards Lifesciences, Medtronic, Meril. Dr. Nieman reports unrestricted institutional support from Siemens Healthineers, Bayer, GE, and Heartflow Inc., and consultancy honoraria from Siemens Medical Solutions USA. Dr. Persson reports on institutional support from Siemens Healthineers. Dr. Schoepf has received grants and/or personal fees from Bayer, Bracco, Elucid BioImaging, GE, Guerbet, HeartFlow Inc., Keya Medical, and Siemens. All other authors declare that they have no financial disclosures., (Copyright © 2021 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)

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)., (Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

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.

Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) is a noninvasive application to evaluate the hemodynamic impact of coronary artery disease by simulating invasively measured FFR based on CT data. CT-FFR is based on the assumption of a normal coronary microvascular response. We assessed the diagnostic performance of a machine-learning based application for on-site computation of CT-FFR in patients with and without diabetes mellitus with suspected coronary artery disease. The study population included 75 diabetic and 276 nondiabetic patients who were enrolled in the MACHINE consortium. The overall diagnostic performance of coronary CT angiography alone and in combination with CT-FFR were analyzed with direct invasive FFR comparison in 110 coronary vessels of the diabetic group and in 415 coronary vessels of the nondiabetic group. Per-vessel discrimination of lesion-specific ischemia by CT-FFR was assessed by the area under the receiver operating characteristic curves. The overall diagnostic accuracy of CT-FFR in diabetic patients was 83% and in nondiabetic patients 75% (p = 0.088), showing improvement over the diagnostic accuracy of coronary CT angiography, which was 58% and 65% (p = 0.223), respectively. In addition, the diagnostic accuracy of CT-FFR was similar between diabetic and nondiabetic patients per stratified CT-FFR group (CT-FFR < 0.6, 0.6 to 0.69, 0.7 to 0.79, 0.8 to 0.89, ≥0.9). The area under the curves for diabetic and nondiabetic patients were also comparable, 0.88 and 0.82 (p = 0.113), respectively. In conclusion, on-site machine-learning CT-FFR analysis improved the diagnostic performance of coronary CT angiography and accurately discriminated lesion-specific ischemia in both diabetic and nondiabetic patients suspected of coronary artery disease., (Copyright © 2018. Published by Elsevier Inc.)

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., (© 2018 American Heart Association, Inc.)

Objectives: The aim of this study was to investigate the individual and combined accuracy of dynamic computed tomography (CT) myocardial perfusion imaging (MPI) and computed tomography angiography (CTA) fractional flow reserve (FFR) for the identification of functionally relevant coronary artery disease (CAD)., Background: Coronary CTA has become an established diagnostic test for ruling out CAD, but it does not allow interpretation of the hemodynamic severity of stenotic lesions. Two recently introduced functional CT techniques are dynamic MPI and CTA FFR using computational fluid dynamics., Methods: From 2 institutions, 74 patients (n = 62 men, mean age 61 years) planned for invasive angiography with invasive FFR measurement in 142 vessels underwent CTA imaging and dynamic CT MPI during adenosine vasodilation. A patient-specific myocardial blood flow index was calculated, normalized to remote myocardial global left ventricular blood flow. CTA FFR was computed using an on-site, clinician-operated application. Using binary regression, a single functional CT variable was created combining both CT MPI and CTA FFR. Finally, stepwise diagnostic work-up of CTA FFR with selective use of CT MPI was simulated. The diagnostic performance of CT MPI, CTA FFR, and CT MPI integrated with CTA FFR was evaluated using C statistics with invasive FFR, with a threshold of 0.80 as a reference., Results: Sensitivity, specificity, and accuracy were 73% (95% confidence interval [CI]: 61% to 86%), 68% (95% CI: 56% to 80%), and 70% (95% CI: 62% to 79%) for CT MPI and 82% (95% CI: 72% to 92%), 60% (95% CI: 48% to 72%), and 70% (63% to 80%) for CTA FFR. For CT MPI integrated with CTA FFR, diagnostic accuracy was 79% (95% CI: 71% to 87%), with improvement of the area under the curve from 0.78 to 0.85 (p < 0.05). Accuracy of the stepwise approach was 77%., Conclusions: CT MPI and CTA FFR both identify functionally significant CAD, with comparable accuracy. Diagnostic performance can be improved by combining the techniques. A stepwise approach, reserving CT MPI for intermediate CTA FFR results, also improves diagnostic performance while omitting nearly one-half of the population from CT MPI examinations., (Copyright © 2017 American College of Cardiology Foundation. All rights reserved.)

Background: To evaluate the combined diagnostic accuracy of coronary computed tomography angiography (CCTA) and computed tomography derived fractional flow reserve (FFRct) in patients with suspected or known coronary artery disease (CAD)., Methods: PubMed, The Cochrane library, Embase and OpenGray were searched to identify studies comparing diagnostic accuracy of CCTA and FFRct. Diagnostic test measurements of FFRct were either extracted directly from the published papers or calculated from provided information. Bivariate models were conducted to synthesize the diagnostic performance of combined CCTA and FFRct at both "per-vessel" and "per-patient" levels., Results: 7 articles were included for analysis. The combined diagnostic outcomes from "both positive" strategy, i.e. a subject was considered as "positive" only when both CCTA and FFRct were "positive", demonstrated relative high specificity (per-vessel: 0.91; per-patient: 0.81), high positive likelihood ratio (LR+, per-vessel: 7.93; per-patient: 4.26), high negative likelihood ratio (LR-, per-vessel: 0.30; per patient: 0.24) and high accuracy (per-vessel: 0.91; per-patient: 0.81) while "either positive" strategy, i.e. a subject was considered as "positive" when either CCTA or FFRct was "positive", demonstrated relative high sensitivity (per-vessel: 0.97; per-patient: 0.98), low LR+ (per-vessel: 1.50; per-patient: 1.17), low LR- (per-vessel: 0.07; per-patient: 0.09) and low accuracy (per-vessel: 0.57; per-patient: 0.54)., Conclusion: "Both positive" strategy showed better diagnostic performance to rule in patients with non-significant stenosis compared to "either positive" strategy, as it efficiently reduces the proportion of testing false positive subjects., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Objectives: The aim of this study is to assess the effect of blood pressure (BP) on coronary computed tomography angiography (CTA) derived computational fractional flow reserve (CTA-FFR)., Materials and Methods: Twenty-one patients who underwent coronary CTA and invasive FFR were retrospectively identified. Ischemia was defined as invasive FFR ≤0.80. Using a work-in-progress computational fluid dynamics algorithm, CTA-FFR was computed with BP measured before CTA, and simulated BPs of 60/50, 90/60, 110/70, 130/80, 150/90, and 180/100 mmHg respectively. Correlation between CTA-FFR and invasive FFR was assessed using Pearson test. The repeated measuring test was used for multiple comparisons of CTA-FFR values by simulated BP inputs., Results: Twenty-nine vessels (14 with invasive FFR ≤0.80) were assessed. The average CTA-FFR for measured BP (134 ± 20/73 ± 12 mmHg) was 0.77 ± 0.12. Correlation between CTA-FFR by measured BP and invasive FFR was good (r = 0.735, P < 0.001). For simulated BPs of 60/50, 90/60, 110/70, 130/80, 150/90, and 180/100 mmHg, the CTA-FFR increased: 0.69 ± 0.13, 0.73 ± 0.12, 0.75 ± 0.12, 0.77 ± 0.11, 0.79 ± 0.11, and 0.81 ± 0.10 respectively (P < 0.05)., Conclusion: Measurement of the BP just before CTA is preferred for accurate CTA-FFR simulation. BP variations in the common range slightly affect CTA-FFR. However, inaccurate BP assumptions differing from the patient-specific BP could cause misinterpretation of borderline significant lesions., Key Points: • The blood pressure (BP) affects the CTA-FFR computation. • Measured BP before CT examination is preferable for accurate CTA-FFR simulation. • Inaccurate BP assumptions can cause misinterpretation of borderline significant lesions.

Background: Recently several publications described the diagnostic value of coronary CT angiography (coronary CTA) derived fractional flow reserve (CTA-FFR). For a recently introduced on-site CTA-FFR application, detailed methodology and factors potentially affecting performance have not yet been described., Objective: To provide a methodological background for an on-site CTA-FFR application and evaluate the effect of patient and acquisition characteristics., Methods: The on-site CTA-FFR application utilized a reduced-order hybrid model applying pressure drop models within stenotic regions. In 116 patients and 203 vessels the diagnostic performance of CTA-FFR was investigated using invasive FFR measurements as a reference. The effect of several potentially relevant factors on CTA-FFR was investigated., Results: 90 vessels (44%) had a hemodynamically relevant stenosis according to invasive FFR (threshold ≤0.80). The overall vessel-based sensitivity, specificity and accuracy of CTA-FFR were 88% (CI 95%:79-94%), 65% (55-73%) and 75% (69-81%). The specificity was significantly lower in the presence of misalignment artifacts (25%, CI: 6-57%). A non-significant reduction in specificity from 74% (60-85%) to 48% (26-70%) was found for higher coronary artery calcium scores. Left ventricular mass, diabetes mellitus and large vessel size increased the discrepancy between invasive FFR and CTA-FFR values., Conclusions: On-site calculation of CTA-FFR can identify hemodynamically significant CAD with an overall per-vessel accuracy of 75% in comparison to invasive FFR. The diagnostic performance of CTA-FFR is negatively affected by misalignment artifacts. CTA-FFR is potentially affected by left ventricular mass, diabetes mellitus and vessel size., (Copyright © 2016 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.)

Purpose: To validate an on-site algorithm for computation of fractional flow reserve (FFR) from coronary computed tomographic (CT) angiography data against invasively measured FFR and to test its diagnostic performance as compared with that of coronary CT angiography., Materials and Methods: The institutional review board provided a waiver for this retrospective study. From coronary CT angiography data in 106 patients, FFR was computed at a local workstation by using a computational fluid dynamics algorithm. Invasive FFR measurement was performed in 189 vessels (80 of which had an FFR ≤ 0.80); these measurements were regarded as the reference standard. The diagnostic characteristics of coronary CT angiography-derived computational FFR, coronary CT angiography, and quantitative coronary angiography were evaluated against those of invasively measured FFR by using C statistics. Sensitivity and specificity were compared by using a two-sided McNemar test., Results: For computational FFR, sensitivity was 87.5% (95% confidence interval [CI]: 78.2%, 93.8%), specificity was 65.1% (95% CI: 55.4%, 74.0%), and accuracy was 74.6% (95% CI: 68.4%, 80.8%), as compared with the finding of lumen stenosis of 50% or greater at coronary CT angiography, for which sensitivity was 81.3% (95% CI: 71.0%, 89.1%), specificity was 37.6% (95% CI: 28.5%, 47.4%), and accuracy was 56.1% (95% CI: 49.0%, 63.2%). C statistics revealed a larger area under the receiver operating characteristic curve (AUC) for computational FFR (AUC, 0.83) than for coronary CT angiography (AUC, 0.64). For vessels with intermediate (25%-69%) stenosis, the sensitivity of computational FFR was 87.3% (95% CI: 76.5%, 94.3%) and the specificity was 59.3% (95% CI: 47.8%, 70.1%)., Conclusion: With use of a reduced-order algorithm, computation of the FFR from coronary CT angiography data can be performed locally, at a regular workstation. The diagnostic accuracy of coronary CT angiography-derived computational FFR for the detection of functionally important coronary artery disease (CAD) was good and was incremental to that of coronary CT angiography within a population with a high prevalence of CAD.