CT-derived fractional flow reserve (FFR-CT) is a noninvasive technique that combines coronary computed tomography angiography (CCTA), with computational modeling to assess the physiological significance of coronary artery stenoses. By integrating anatomical and hemodynamic data, FFR-CT provides lesion-specific pressure estimates comparable to invasive fractional flow reserve, without the need for catheterization or pharmacologic hyperemia. This narrative review summarizes the fundamental principles, validation evidence, clinical applications, and limitations of FFR-CT in contemporary coronary artery disease (CAD) management. FFR-CT is computed using computational fluid dynamics or machine-learning algorithms that simulate hyperemic blood f low within patient-specific coronary geometries reconstructed from standard CCTA images. Major multicenter trials and registries, including DISCOVER FLOW, DeFACTO, NXT, and ADVANCE, have consistently demonstrated high diagnostic accuracy and robust prognostic value, showing strong correlation with invasive FFR and improved specificity compared with CCTA alone. Integration of FFR-CT into diagnostic workflows significantly reduces unnecessary invasive angiography and revascularization procedures while maintaining excellent clinical outcomes. Recent guideline endorsements by the European Society of Cardiology and the American College of Cardiology reflect its growing role as a validated, cost-effective tool in stable CAD evaluation. Despite limitations related to image quality, microvascular assumptions, and restricted utility in acute settings, ongoing advances in artificial intelligence, image reconstruction, and hybrid modeling promise faster, more accurate, and more accessible analysis. FFR-CT represents a paradigm shift in noninvasive coronary physiology assessment, providing a unified anatomic–functional framework that enhances precision in CAD diagnosis and management.

• CT-derived fractional flow reserve

• Coronary CT angiography

• Computational fluid dynamics

• Coronary artery disease

• Noninvasive physiology assessment

ÇEL?K MC, ?AH?N MM, KALÇIK M, YET?M M, BEKAR L, et al. (2025) Advances in CT-Derived Fractional Flow Reserve: Principles, Evi dence, and Clinical Utility. J Radiol Radiat Ther 13(2): 1116.

Coronary artery disease (CAD) remains the leading global cause of morbidity and mortality, accounting for an estimated 9 million deaths annually and representing the single largest contributor to health-related disability worldwide [1]. The pathophysiological substrate of CAD, atherosclerotic plaque formation and luminal narrowing, has traditionally been evaluated through anatomic imaging modalities such as invasive coronary angiography (ICA) and coronary CT angiography (CCTA). While these techniques provide high-resolution visualization of coronary anatomy, they fail to fully capture the hemodynamic significance of a given stenosis. The degree of luminal narrowing correlates only modestly with ischemia, and reliance on anatomical severity alone often leads to both overestimation and underestimation of physiologically significant lesions [2].

Invasive fractional flow reserve (FFR) transformed the diagnostic paradigm by quantifying lesion-specific ischemic burden through the ratio of distal coronary pressure to aortic pressure during pharmacologically induced maximal hyperemia [3]. The pivotal FAME trials demonstrated that FFR-guided percutaneous coronary intervention (PCI) improved clinical outcomes and reduced unnecessary revascularizations compared with angiography-guided strategies [4,5]. However, routine invasive FFR measurement remains limited by the need for coronary instrumentation, adenosine administration, procedural time, and cost. Consequently, noninvasive methods capable of integrating anatomic and physiologic information have been sought to optimize patient selection for invasive procedures.

CT-derived fractional flow reserve (FFR-CT) emerged as a technological solution that uses computational modeling to simulate coronary blood flow and pressure fields directly from standard CCTA datasets. This technique applies computational fluid dynamics (CFD) or machine learning algorithms to derive vessel-specific FFR values without additional image acquisition, radiation, or pharmacologic stress. Conceptually, it bridges the gap between anatomical imaging and physiologic assessment, offering the potential to noninvasively identify ischemia producing lesions before referral to the catheterization laboratory. 

Initial proof-of-concept studies established feasibility and diagnostic accuracy. The DISCOVER-FLOW study first demonstrated that FFR-CT could accurately identify functionally significant stenoses compared with invasive FFR [6]. Subsequently, the DeFACTO trial validated FFR CT against invasive standards across multiple centers, showing improved specificity relative to CCTA alone [7]. The NXT study further confirmed diagnostic robustness with optimized image quality and refined CFD modeling [8]. Large-scale registry data, including the ADVANCE registry, later supported real-world utility, showing that FFR-CT-guided management strategies reduced unnecessary invasive testing and improved downstream event rates [9].

Beyond diagnostic accuracy, FFR-CT has expanded into a central role within “CT-first” pathways recommended by major societies such as the European Society of Cardiology (ESC) and the American College of Cardiology (ACC) for the evaluation of stable chest pain [10,11]. As an integrated physiologic tool, it enables stratification of patients with intermediate coronary stenoses, reduces healthcare costs, and enhances precision in CAD management.

This narrative review provides an updated synthesis of the physiological foundations, technical principles, validation evidence, clinical applications, and limitations of FFR-CT. It aims to critically appraise the technology’s current status, discuss its incorporation into clinical workflows, and outline future directions, including AI driven algorithms and integration with CT perfusion imaging.

Fractional flow reserve (FFR) is defined as the ratio of maximal blood flow in a stenotic coronary artery to the theoretical maximal flow in the absence of stenosis. Mathematically, FFR equals the ratio of distal coronary pressure (Pd) to proximal aortic pressure (Pa) during conditions of maximal hyperemia, expressed as FFR = Pd/Pa. This dimensionless index directly reflects the physiological impact of a coronary stenosis on myocardial perfusion, independent of heart rate, blood pressure, or microvascular resistance under hyperemic conditions [12,13].

Invasive FFR measurement, introduced in the mid 1990s, revolutionized the evaluation of intermediate coronary lesions that appeared ambiguous by angiography [14]. Hyperemia is usually induced pharmacologically with adenosine, ensuring minimal and stable microvascular resistance, which allows pressure-derived FFR to closely approximate true flow reserve. Extensive experimental validation demonstrated a linear relationship between pressure-derived FFR and flow-based indices of ischemia [15]. A threshold value of ≤0.80 identifies hemodynamically significant stenoses with high sensitivity and specificity for myocardial ischemia, forming the basis for clinical decision-making [16].

The clinical significance of FFR has been validated in multiple pivotal trials. The FAME (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation) study showed that FFR-guided PCI reduced major adverse cardiac events compared with angiography-guided revascularization, despite treating fewer lesions [4]. The FAME 2 trial extended these findings by demonstrating improved outcomes with FFR-guided PCI versus optimal medical therapy in stable CAD [5]. Collectively, these studies established FFR as the gold standard for physiological lesion assessment.

However, invasive FFR requires coronary intubation, vasodilator administration, and pressure wire manipulation, all of which limit its widespread use in stable patients and screening settings. These limitations prompted the development of CT-derived FFR (FFR CT), which aims to reproduce the physiological insights of invasive FFR using data derived from coronary CT angiography (CCTA). By integrating anatomic detail with computational modeling of flow and pressure, FFR-CT enables comprehensive, noninvasive functional assessment of coronary stenoses without the procedural risks of catheter-based measurement [6,8].

Understanding the physiological foundation of FFR is essential to interpret FFR-CT results correctly. Both techniques rest on the same principle, that pressure loss across a stenosis is proportional to flow resistance, governed by fluid dynamic laws (principally Bernoulli’s and Poiseuille’s equations). Under maximal vasodilation, when microvascular resistance is minimal and stable, this proportionality becomes direct, allowing pressure measurements (invasive or computational) to act as surrogates for flow. Hence, FFR-CT represents not a novel physiologic concept but an alternative means of applying established hemodynamic theory using imaging-derived boundary conditions. The conceptual and methodological differences between invasive and CT-derived FFR are summarized in Table 1.

Table 1: Key Differences Between Invasive FFR and CT-Derived FFR (FFR-CT).

Abbreviations: FFR: Fractional Flow Reserve; FFR-CT: CT-derived Fractional Flow Reserve; CCTA: Coronary Computed Tomography Angiography; Pd: Distal Pressure; Pa: Aortic Pressure.

microvascular resistance). Because invasive FFR is measured under hyperemia, the model simulates maximal vasodilation by reducing distal microvascular resistance to reflect adenosine-like conditions [20]. The result is a patient-specific map of pressure and flow that yields an FFR value for every point in the coronary network, with color-coded visualization superimposed on the CT anatomy [21].

Despite high physiologic fidelity, full CFD modeling is computationally expensive, requiring hours on high performance clusters in early implementations. To overcome this limitation, reduced-order models and machine-learning surrogates were developed after 2015. Reduced-order models simplify flow physics by coupling one-dimensional vessel flow equations with zero-dimensional lumped-parameter representations of microcirculation, reducing computation to minutes while maintaining diagnostic accuracy [22]. Machine-learning approaches, trained on large CFD-derived datasets, can infer pressure drops directly from geometric and morphological CT features, achieving near-real-time computation without explicit fluid simulation [23].

Comparative validation between these approaches demonstrated that both CFD and machine-learning based FFR-CT correlate strongly with invasive FFR (r ≈ 0.80–0.86) and outperform standard CCTA in specificity [8]. The MACHINE consortium showed that a purely machine-learning-based FFR-CT model yielded equivalent diagnostic performance to traditional CFD solutions, enabling faster turnaround and on-site processing [24].

A critical technical consideration is the accurate definition of boundary conditions, since errors in assumed microvascular resistance or flow-splitting between branches directly affect computed pressure drops. Recent work has focused on incorporating patient-specific physiologic data such as blood pressure and heart rate to individualize these parameters [25]. The reliability of FFR CT is also influenced by CT image quality, motion artifacts, heavy calcification, or poor distal opacification can cause segmentation errors and false ischemic readings [26].

Finally, rigorous quality control is essential. Commercial systems such as HeartFlow® integrate automated error detection and mesh optimization to ensure numerical stability and physiological plausibility. The continual shift from purely physics-based CFD toward hybrid physics informed AI models represents the dominant trajectory of current FFR-CT technology, aiming to combine speed, scalability, and physiologic accuracy.

The validation of CT-derived fractional flow reserve (FFR-CT) has progressed through a structured sequence of multicenter trials, registries, and meta-analyses since 2011. These studies collectively confirmed that FFR CT accurately identifies ischemia-producing lesions and improves clinical decision-making compared with anatomical imaging alone. The diagnostic and prognostic data supporting its use are now extensive and form the basis for guideline endorsement [27].

The earliest pivotal trials, DISCOVER-FLOW, DeFACTO, and NXT, established the technical feasibility and diagnostic accuracy of FFR-CT compared with invasive FFR as the reference standard. The DISCOVER-FLOW trial demonstrated that FFR-CT significantly improved specificity over CT angiography alone (84% vs. 59%), with an overall accuracy of 84% [6]. DeFACTO expanded validation across multiple international sites, confirming consistent diagnostic performance in diverse populations and scanning conditions [7]. The NXT trial, using next generation CT scanners and refined CFD algorithms, reported an area under the receiver operating characteristic curve (AUC) of 0.90 for detecting functionally significant stenoses, superior to CCTA’s 0.81 [8].

Subsequent prospective registries evaluated FFR-CT performance in real-world clinical practice. The ADVANCE Registry, encompassing over 5000 patients across 38 countries, demonstrated that integration of FFR-CT into clinical decision pathways led to a 60% reduction in unnecessary invasive angiography while maintaining low rates of adverse cardiac events over one year [9]. Importantly, diagnostic accuracy remained stable across varying image quality and scanner types, indicating reproducibility outside controlled trial settings [26].

Meta-analyses reinforced these findings. A 2018 pooled analysis including over 1300 vessels reported mean per-vessel sensitivity and specificity of 86% and 79%, respectively, with strong correlation to invasive FFR (r = 0.84) [28]. More recent systematic reviews incorporating machine-learning-based FFR-CT approaches confirmed comparable diagnostic performance to CFD-based models while offering faster computation and broader accessibility [24].

Beyond diagnostic accuracy, FFR-CT has been shown to possess prognostic value. The ADVANCE Outcomes substudy revealed that patients with negative FFR-CT (>0.80) had extremely low event rates at 12 months (<1%), validating its role in safely deferring invasive testing [29]. Longitudinal analyses from the FORECAST trial demonstrated that FFR-CT-guided management not only improved resource utilization but also reduced total cardiovascular costs without increasing adverse events compared with standard care [30].

These data prompted major society recommendations. The 2019 ESC Guidelines for chronic coronary syndromes and the 2021 ACC/AHA Chest Pain Guidelines recognize FFR-CT as an appropriate noninvasive test for assessing intermediate stenoses on CCTA, particularly when physiological significance is uncertain [10,11]. Current evidence thus positions FFR-CT as the only clinically validated, noninvasive alternative to invasive FFR, bridging anatomical and physiological assessment in the diagnostic pathway of coronary artery disease. Key multicenter validation studies that established diagnostic accuracy of FFR-CT compared with invasive FFR are presented in Table 2.

Table 2: Major Multicenter Trials Validating FFR-CT.

Abbreviations: FFR: Fractional Flow Reserve; FFR-CT: CT-derived Fractional Flow Reserve; CCTA: Coronary Computed Tomography Angiography; AUC: Area Under the Curve; ICA: Invasive Coronary Angiography; MACE: Major Adverse Cardiac Events.

The integration of FFR-CT into clinical practice has redefined the diagnostic approach to patients with suspected or known coronary artery disease (CAD). Unlike traditional imaging modalities that depict only anatomical stenosis, FFR-CT adds a physiologic dimension that enables functional interpretation of coronary lesions directly from CT angiography. This dual anatomic–functional evaluation supports a more rational use of invasive testing and revascularization, particularly in patients with intermediate lesions or multivessel disease [7].

The most important clinical application of FFR-CT lies in the evaluation of stable chest pain. Modern guidelines now endorse a “CT-first” diagnostic strategy, recommending CCTA as the initial test for patients with stable angina and intermediate pretest probability of CAD [10]. Within this pathway, FFR-CT serves as a second-line analysis to determine whether anatomically moderate lesions (40-70% diameter reduction) are physiologically significant. This approach enables safe deferral of invasive coronary angiography (ICA) in patients with nonischemic FFR CT values (>0.80), while selectively referring those with hemodynamically significant disease for revascularization [31].

Workflow studies have confirmed substantial downstream impact. The PLATFORM study showed that FFR-CT–guided evaluation reduced unnecessary ICA by 61% compared with usual care, without compromising safety [32]. Similarly, the ADVANCE registry and FORECAST randomized trial demonstrated improved diagnostic efficiency and reduced cost per patient when FFR-CT was incorporated into standard CCTA-based care pathways [9,33]. In these studies, approximately two thirds of patients avoided invasive testing, and rates of major adverse cardiac events remained extremely low among those deferred on the basis of nonischemic FFR-CT results.

In multivessel CAD, FFR-CT allows comprehensive functional mapping across all coronary branches within a single dataset. This whole-heart physiologic insight is particularly valuable for identifying the dominant culprit lesion in patients with multiple intermediate stenoses, avoiding overtreatment and unnecessary stenting [34]. FFR-CT–derived pressure maps can also identify diffuse atherosclerotic disease and guide the use of medical therapy rather than intervention in cases where no focal ischemia-producing lesion is detected [21].

Cost-effectiveness analyses have supported these findings. Health economic models using data from the United States, Europe, and Japan consistently demonstrate that CT-first pathways incorporating FFR-CT reduce overall costs while improving quality-adjusted life years (QALYs) compared with invasive strategies [35]. By avoiding unnecessary catheterizations, FFR-CT streamlines patient flow, shortens time to definitive diagnosis, and reduces procedural risks and hospital resource utilization.

Furthermore, clinical guideline integration is now widespread. The 2021 ACC/AHA and 2019 ESC guidelines classify FFR-CT as an appropriate or reasonable test for evaluating intermediate lesions detected by CCTA. These endorsements underscore its transition from a research technique to a clinically validated decision-making tool. As machine-learning-based methods continue to improve speed and local availability, FFR-CT is expected to become a routine adjunct to coronary CT interpretation in both outpatient and emergency settings [10,11]. The principal and emerging clinical applications of FFR-CT across diverse coronary syndromes are outlined in Table 3.

Table 3: Current and Emerging Clinical Applications of FFR-CT.

Abbreviations: FFR-CT: CT-derived Fractional Flow Reserve; ICA: Invasive Coronary Angiography; ESC: European Society of Cardiology; ACC: American College of Cardiology; CAD: Coronary Artery Disease; PCI: Percutaneous Coronary Intervention.

Although FFR-CT has achieved broad clinical validation and regulatory approval, several technical and practical limitations constrain its universal application. These challenges span image acquisition, computational modeling, physiologic assumptions, and workflow integration. Understanding them is critical to correctly interpreting FFR-CT results and avoiding inappropriate clinical decisions [23].

The first and most fundamental limitation lies in the dependency on high-quality coronary CT angiography. Image quality directly determines segmentation accuracy, coronary geometry reconstruction, and downstream computational fidelity. Heavy calcification, motion artifacts, misregistration, or poor distal opacification can significantly distort lumen boundaries and generate spurious pressure gradients, particularly in small-caliber vessels or distal segments [26]. Studies consistently show that diagnostic performance declines in patients with extensive coronary calcium (Agatston score >400), where blooming artifacts obscure true lumen dimensions [36].

Another key issue involves assumptions in boundary conditions and flow modeling. FFR-CT simulations depend on generic or population-based estimates of coronary microvascular resistance to emulate hyperemic flow conditions. However, these assumptions may not reflect patient-specific physiologic variability, such as microvascular dysfunction, left ventricular hypertrophy, or diabetes, all of which alter flow dynamics independent of epicardial stenosis [37]. Consequently, FFR-CT may overestimate or underestimate ischemia in patients with abnormal microcirculation, as it cannot directly measure microvascular resistance [38].

Artifacts related to stents and bypass grafts represent another limitation. Metallic implants and surgical clips cause streak artifacts that prevent accurate segmentation and flow simulation. For this reason, most commercial FFR-CT solutions are not validated for post-stent or post coronary artery bypass graft (CABG) assessments [39]. Moreover, vessel segments distal to chronic total occlusions or heavily calcified bifurcations may not be analyzable due to incomplete or nonuniform contrast filling [21].

Operational and logistical constraints also persist. The most widely used CFD-based FFR-CT platform (HeartFlow®) requires off-site supercomputing infrastructure, creating a time delay of several hours between image upload and result availability [40]. Although on-site machine-learning based systems now offer faster processing, they remain dependent on vendor-specific software and dedicated post-processing expertise, which limits adoption in resource-limited centers [24].

Economic and reimbursement barriers vary by region. While cost-effectiveness analyses favor FFR-CT, upfront implementation costs and lack of reimbursement in some healthcare systems reduce uptake [35]. Additionally, the proprietary nature of algorithms raises transparency concerns; most commercial FFR-CT platforms are “black box” systems, providing limited insight into computational parameters or model assumptions [41].

Finally, clinical applicability remains uncertain in several scenarios, including acute coronary syndromes, unstable hemodynamics, and serial or diffuse lesions where pressure recovery complicates interpretation. FFR CT reflects a steady-state hyperemic assumption and may not accurately capture dynamic flow changes caused by vasospasm, microembolization, or transient ischemia [42]. Thus, current evidence supports its use primarily in stable CAD, with ongoing studies evaluating its role in acute and post-revascularization settings [34]. Major technical and operational limitations together with potential mitigation strategies are summarized in Table 4.

Table 4: Technical and Operational Limitations of FFR-CT.

Abbreviations: FFR-CT: CT-derived Fractional Flow Reserve; AI: Artificial Intelligence; LVH: Left Ventricular Hypertrophy; ACS: Acute Coronary Syndrome; CT: Computed Tomography.

The evolution of FFR-CT continues toward faster computation, broader accessibility, deeper physiological integration. The earliest implementations and required hours of off-site computation using high-fidelity CFD; modern systems now generate results within minutes through hybrid models that merge simplified physics with machine learning. This shift preserves physiologic accuracy while reducing computational cost, enabling near real-time decision support in clinical settings [24].

A major emerging direction is on-site, fully automated FFR-CT, where AI-driven segmentation, boundary assignment, and pressure-field estimation occur directly on hospital workstations. Early prospective studies have shown diagnostic equivalence between on-site machine learning FFR-CT and traditional off-site CFD platforms, with processing times under ten minutes [23]. As deep learning architectures mature, geometry-aware neural networks are being trained to infer pressure and flow directly from three-dimensional coronary meshes, bypassing explicit numerical simulation altogether.

Integration with other imaging biomarkers represents another frontier. Combining FFR-CT with CT-based plaque characterization or CT perfusion imaging can yield a comprehensive assessment of both anatomic and physiologic disease burden. Plaque morphology, particularly low-attenuation or positively remodeled lesions, has been shown to correlate with low FFR-CT and future acute coronary events [43]. Hybrid approaches may thus enhance risk prediction and enable preventive therapy before the onset of flow-limiting disease.

From a clinical systems perspective, expanding reimbursement and cloud-based deployment are expected to drive adoption in community hospitals. The continuing decline in CT radiation dose and contrast load will further widen eligibility, including in younger and lower-risk populations. Meanwhile, research is exploring the role of FFR-CT in special settings such as post-PCI follow-up,coronary anomalies, and heart-transplant vasculopathy, domains where invasive testing is often undesirable [44].

In the long term, FFR-CT is poised to become an integral element of multimodal cardiac imaging, linking morphology, physiology, and computational prediction. Its trajectory reflects a broader movement toward virtual physiology, where patient-specific simulation complements traditional diagnostics to guide personalized management of coronary disease.

CT-derived fractional flow reserve (FFR-CT) has transformed the clinical approach to coronary artery disease by providing a bridge between anatomical imaging and functional assessment. Conventional coronary CT angiography offers high spatial resolution and accurate delineation of stenoses, but it cannot reliably determine whether a lesion impairs myocardial perfusion. FFR CT resolves this limitation by integrating physiologic modeling into standard CT datasets, yielding a noninvasive surrogate for invasive fractional flow reserve. As a result, it enables simultaneous anatomical and hemodynamic evaluation without additional radiation, pharmacologic stress, or procedural risk.

Over the past decade, multiple multicenter validation studies and international registries have confirmed the diagnostic and prognostic reliability of FFR-CT. These investigations consistently demonstrate that FFR-CT improves the specificity of CT angiography, reduces unnecessary invasive angiography, and guides revascularization more appropriately than anatomical evaluation alone. Patients with negative FFR-CT values experience very low event rates when managed conservatively, supporting its use as a gatekeeper before invasive testing. Furthermore, economic analyses have shown that CT-first strategies incorporating FFR-CT lower total healthcare costs while maintaining or improving patient outcomes.

The integration of FFR-CT into major international guidelines marks an important milestone. Both European and American societies now recommend its use in patients with intermediate coronary stenoses detected on CT angiography, reflecting widespread recognition of its clinical and economic value. The adoption of machine learning algorithms and accelerated computational techniques has further enhanced accessibility, allowing near–real-time analysis directly on-site in many institutions.

Despite its strengths, the technology still faces practical constraints. Dependence on image quality, the need for advanced post-processing infrastructure, and limited applicability in acute or post-intervention settings remain challenges. Ongoing research aims to refine physiologic modeling, incorporate patient-specific microvascular data, and expand use into broader clinical contexts such as diffuse disease or graft assessment.

Looking ahead, FFR-CT exemplifies the broader evolution of cardiovascular imaging toward personalized,quantitative, and simulation-based medicine. By uniting structural and functional information within a single dataset, it supports a more precise and efficient diagnostic pathway. Future developments will likely make FFR-CT faster, cheaper, and more universally accessible, while combining it with plaque characterization and perfusion imaging will extend its prognostic and preventive potential. In sum, FFR-CT exemplifies the transition of cardiovascular imaging toward quantitative, simulation-based, and patient-specific diagnostics, linking coronary anatomy to physiology in a single, noninvasive examination.

All of the authors contributed planning, conduct, and reporting of the work. All authors had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

1. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990- 2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020; 396: 1204-1222.
2. Tonino PA, Fearon WF, De Bruyne B, Oldroyd KG, Leesar MA, Lee PNV, et al. Angiographic versus functional severity of coronary artery stenoses in the FAME study fractional flow reserve versus angiography in multivessel evaluation. J Am Coll Cardiol. 2010; 55: 2816-2821.
3. Dobri? M, Furtula M, Teši? M, Timcic S, Borzanovic D, Lazarevic N, et al. Current status and future perspectives of fractional flow reserve derived from invasive coronary angiography. Front Cardiovasc Med. 2023; 10: 1181803.
4. Tonino PA, De Bruyne B, Pijls NH, Siebert U, Ikeno F, Veer MV, et al. Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. N Engl J Med. 2009; 360: 213-224.
5. De Bruyne B, Pijls NH, Kalesan B, Barbato E, Tonino PAL, Piroth Z, et al. Fractional flow reserve-guided PCI versus medical therapy in stable coronary disease. N Engl J Med. 2012; 367: 991-1001.
6. Koo BK, Erglis A, Doh JH, Daniels DV, Jegere S, Kim HS, et al. Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms. Results from the prospective multicenter DISCOVER- FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Fractional Flow Reserve) study. J Am Coll Cardiol. 2011; 58: 1989-1997.
7. Min JK, Leipsic J, Pencina MJ, Berman DS, Koo BK, Mieghem CV, et al. Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA. 2012; 308: 1237-1245.
8. Nørgaard BL, Leipsic J, Gaur S, Ko BS, Ito H, Jensen JM, et al. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). J Am Coll Cardiol. 2014; 63: 1145-1155.
9. Fairbairn TA, Nieman K, Akasaka T, Berman DS, Raff G, Pontone G, et al. Real-world clinical utility and impact on clinical decision-making of coronary computed tomography angiography-derived fractionalflow reserve: lessons from the ADVANCE Registry. Eur Heart J. 2018; 39: 3701-3711.
10. Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Prescott E, et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020; 41: 407-477.
11. Writing Committee Members, Gulati M, Levy PD, Mukherjee D, Amsteram E, Bhatt DL, Birtcher KK, et al. 2021 AHA/ACC/ASE/ CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J Am Coll Cardiol. 2021; 78: 2218-2261.
12. Kern MJ, Samady H. Current concepts of integrated coronary physiology in the catheterization laboratory. J Am Coll Cardiol. 2010; 55: 173-185.
13. De Bruyne B, Pijls NH, Heyndrickx GR, Hodeige D, Kirkeeide R, Gould KL. Pressure-derived fractional flow reserve to assess serial epicardial stenoses: theoretical basis and animal validation. Circulation. 2000; 101: 1840-1847.
14. Pijls NH, De Bruyne B, Peels K, Van Der Voort PH, Bonnier HJ, Koolen JJ, et al. Measurement of fractional flow reserve to assess the functional severity of coronary-artery stenoses. N Engl J Med. 1996; 334: 1703-1708.
15. Echavarría-Pinto M, Collet C, Escaned J, Piek JJ, Serruys PW. State of the art: pressure wire and coronary functional assessment. EuroIntervention. 2017; 13: 666-679.
16. Fearon WF, Nishi T, De Bruyne B, Boothroyd DB, Barbato E, Tonino P, et al. Clinical Outcomes and Cost-Effectiveness of Fractional Flow Reserve-Guided Percutaneous Coronary Intervention in Patients With Stable Coronary Artery Disease: Three-Year Follow-Up of the FAME 2 Trial (Fractional Flow Reserve Versus Angiography for Multivessel Evaluation). Circulation. 2018; 137: 480-487.
17. Taylor CA, Fonte TA, Min JK. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. J Am Coll Cardiol. 2013; 61: 2233-2241.
18. Achenbach S, Marwan M, Ropers D, Schepis T, Pflederer T, Anders K, et al. Coronary computed tomography angiography with a consistent dose below 1 mSv using prospectively electrocardiogram-triggered high-pitch spiral acquisition. Eur Heart J. 2010; 31: 340-346.
19. Itu L, Rapaka S, Passerini T, Georgescu B, Schwemmer C, Schoebinger M, et al. A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol. 2016; 121: 42-52.
20. Morris PD, Ryan D, Morton AC, Lycett R, Lawford PV, Hose DR, et al. Virtual fractional flow reserve from coronary angiography: modeling the significance of coronary lesions: results from the VIRTU-1 (VIRTUal Fractional Flow Reserve From Coronary Angiography) study. JACC Cardiovasc Interv. 2013; 6: 149-157.
21. Coenen A, Lubbers MM, Kurata A, Kono A, Dedic A, Chelu RG, et al. Fractional flow reserve computed from noninvasive CT angiography data: diagnostic performance of an on-site clinician-operated computational fluid dynamics algorithm. Radiology. 2015; 274: 674-683.
22. Liang F, Takagi S, Himeno R, Liu H. Multi-scale modeling of the human cardiovascular system with applications to aortic valvular and arterial stenoses. Med Biol Eng Comput. 2009; 47: 743-755.
23. Tesche C, De Cecco CN, Albrecht MH, Duguay TM, Bayer RR, Litwin SE, et al. Coronary CT Angiography-derived Fractional Flow Reserve. Radiology. 2017; 285: 17-33.
24. Coenen A, Kim YH, Kruk M, Tesche C, De Geer J, Kurata A, et al. Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result From the MACHINE Consortium. Circ Cardiovasc Imaging. 2018; 11: e007217.
25. Sankaran S, Esmaily Moghadam M, Kahn AM, Tseng EE, Guccione JM, Marsden AL. Patient-specific multiscale modeling of blood flow for coronary artery bypass graft surgery. Ann Biomed Eng. 2012; 40: 2228-2242.
26. Xu PP, Li JH, Zhou F, Jiang MD, Zhou CS, Lu MJ, et al. The influence of image quality on diagnostic performance of a machine learning-based fractional flow reserve derived from coronary CT angiography. Eur Radiol. 2020; 30: 2525-2534.
27. Patel MR, Calhoon JH, Dehmer GJ, Grantham JA, Maddox TM, Maron DJ, et al. ACC/AATS/AHA/ASE/ASNC/SCAI/SCCT/STS 2017Appropriate Use Criteria for Coronary Revascularization in Patients With Stable Ischemic Heart Disease: A Report of the American College of Cardiology Appropriate Use Criteria Task Force, American Association for Thoracic Surgery, American Heart Association, American Society of Echocardiography, American Society of Nuclear Cardiology, Society for Cardiovascular Angiography and Interventions, Society of Cardiovascular Computed Tomography, and Society of Thoracic Surgeons. J Am Coll Cardiol. 2017; 69: 2212-2241.
28. Cook CM, Petraco R, Shun-Shin MJ, Ahmad Y, Nijjer S, Al-Lamee R, et al. Diagnostic Accuracy of Computed Tomography-Derived Fractional Flow Reserve : A Systematic Review. JAMA Cardiol. 2017; 2: 803-810.
29. Patel MR, Nørgaard BL, Fairbairn TA, Nieman K, Akasaka T, Berman DS, et al. 1-Year Impact on Medical Practice and Clinical Outcomes of FFRCT: The ADVANCE Registry. JACC Cardiovasc Imaging. 2020; 13: 97-105.
30. Curzen N, Nicholas Z, Stuart B, Wilding S, Hill K, Shambrook K, et al. Fractional flow reserve derived from computed tomography coronary angiography in the assessment and management of stable chest pain: the FORECAST randomized trial. Eur Heart J. 2021; 42: 3844-3852.
31. Nørgaard BL, Fairbairn TA, Safian RD, Rabbat MG, Ko B, Jensen JM, et al. Coronary CT Angiography-derived Fractional Flow Reserve Testing in Patients with Stable Coronary Artery Disease: Recommendations on Interpretation and Reporting. Radiol Cardiothorac Imaging. 2019; 1: e190050.
32. Douglas PS, De Bruyne B, Pontone G, Patel MR, Norgaard BL, Bryne RA, et al. 1-Year Outcomes of FFRCT-Guided Care in Patients With Suspected Coronary Disease: The PLATFORM Study. J Am Coll Cardiol. 2016; 68: 435-445.
33. Madsen KT, Nørgaard BL, Øvrehus KA, Jensen JM, Parner E, Grove EL, et al. Prognostic Value of Coronary CT Angiography-derived Fractional Flow Reserve on 3-year Outcomes in Patients with Stable Angina. Radiology. 2023; 308: e230524.
34. Karády J, Mayrhofer T, Ivanov A, Foldyna B, Lu MT, Ferencik M, et al. Cost-effectiveness Analysis of Anatomic vs Functional Index Testing in Patients With Low-Risk Stable Chest Pain. JAMA Netw Open. 2020; 3: e2028312.
35. Dey D, Diaz Zamudio M, Schuhbaeck A, Otaki Y, Gransar H, Li D, et al. Relationship Between Quantitative Adverse Plaque Features From Coronary Computed Tomography Angiography and Downstream Impaired Myocardial Flow Reserve by 13N-Ammonia Positron Emission Tomography: A Pilot Study. Circ Cardiovasc Imaging. 2015; 8: e003255.
36. Jerabek S, Kovarnik T. Technical aspects and limitations of fractional flow reserve measurement. Acta Cardiol. 2019; 74: 9-16.
37. Yu Y, Kou J, Guo F, Zhang D, Pan T, Chen Y, et al. Prognostic value of CT-derived fractional flow reserve and fat attenuation index in patients with suspected coronary artery disease: a sex-disaggregated analyses. BMC Cardiovasc Disord. 2023; 23: 612.
38. Oikonomou EK, Marwan M, Desai MY. Non-invasive detection of coronary inflammation using computed tomography and predictionof residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data. Lancet. 2018; 392: 929-939.
39. Kay FU, Canan A, Abbara S. Future Directions in Coronary CT Angiography: CT-Fractional Flow Reserve, Plaque Vulnerability, and Quantitative Plaque Assessment. Korean Circ J. 2020; 50: 185-202.