CT-FFR: How a new technology could transform cardiovascular diagnostic imaging

Abstract

Background

CT-based fractional flow reserve (CT-FFR) is a promising noninvasive method for the functional assessment of coronary stenosis. It expands the diagnostic capabilities of coronary CT angiography (cCTA) by providing hemodynamic information and potentially reducing unnecessary invasive coronary angiography examinations

Methods

This review summarizes current technological developments, study results, and clinical applications of CT-FFR. It also discusses the advantages and disadvantages of various software solutions, including artificial intelligence (AI)-based on-site analyses, and their potential integration into the clinical routine.

Results

Studies show that CT-FFR improves diagnostic accuracy compared to cCTA and can optimize patient management. Advances in artificial intelligence and new imaging techniques such as photon-counting CT could further refine CT-FFR and expand its applicability. Despite promising results, further research is needed regarding long-term validation, standardized workflows, and economic feasibility.

Conclusion

CT-FFR is a promising complementary tool for assessing the hemodynamic relevance of coronary stenoses. CT-FFR is particularly helpful in complex, long-segment, or consecutive stenosis, because a purely anatomical visual examination is not always sufficient. The combination of technical innovations and AI-assisted image analysis could have the potential to transform noninvasive coronary diagnostics.

Key Points

• CT-FFR increases specificity and diagnostic accuracy compared to cCTA alone.

• Technological advances could further refine CT-FFR and expand its applicability.

• The increasing adoption and improved applicability of CT-FFR in routine clinical practice is promising.

Citation Format

• Kloth C, Brendel JM, Kübler J et al. CT-FFR: How a new technology could transform cardiovascular diagnostic imaging. Rofo 2025; DOI 10.1055/a-2697-5413

Keywords

heart - CT-angiography - cardiac

Abbreviations

AUC: area under the curve

CAD-RADS: coronary artery disease reporting and data system

cCTA: coronary CT angiography

CT: computed tomography

CT-FFR: CT-based fractional flow reserve

CT-P: CT perfusion

EID: energy-integrating detector

ESC: European Society of Cardiology

FFR: fractional flow reserve

ICA: invasive coronary angiography

CHD: coronary heart disease

AI: artificial intelligence

MACE: major cardiac event

MRI: magnetic resonance imaging

NPV: negative predictive value

NSTEMI: non-ST-elevation myocardial infarction

PCD: photon-counting detector

PCD-CT: photon-counting detector computed tomography

PPV: positive predictive value

SPECT: single-photon emission computed tomography

TAVI: transcatheter aortic valve implantation

Introduction

The guidelines of the European Society of Cardiology (ESC) regarding the diagnosis and treatment of chronic symptomatic coronary syndromes were updated in 2019 [1] [2]. As part of this update, coronary CT angiography (cCTA) was recommended as the preferred first-line imaging method for patients with low to intermediate pretest probability of coronary heart disease (CHD) [2]. Due to its high sensitivity regarding the detection of coronary stenosis and its excellent negative predictive value, cCTA is suitable particularly for the reliable exclusion of CHD [3]. The recent inclusion of cCTA in the services catalog of the statutory health insurance companies by the Federal Joint Committee further highlights its growing importance in clinical practice [4] [5].

The degree of stenosis is determined based on the percentage of lumen narrowing [6]. However, this exclusively morphological evaluation of coronary stenosis often results in overestimation of the hemodynamic relevance. More than half of significant stenoses detected on cCTA do not result in ischemia [3] [7]. Due to the limited specificity of cCTA regarding functional evaluation, many patients with plaques causing anatomical stenosis visible on CT are subjected to invasive coronary angiography without detection of a hemodynamically relevant stenosis [8]. Various noninvasive methods have been developed for functional evaluation as a supplement to cCTA. In addition to myocardial CT perfusion (CT-P), which measures contrast enhancement in the myocardium under stress or pharmacologically induced hyperemia, CT-based fractional flow reserve (CT-FFR) has become established as a promising method [9] [10]. The hemodynamic relevance of coronary lesions is evaluated based on numerical modeling of the blood flow. The calculation uses the principles of flow mechanics, often supported by artificial intelligence (AI) methods, to simulate the drop in pressure caused by stenosis, analogously to invasive FFR. In contrast to invasive FFR, CT-FFR does not require catheterization or medication-induced hyperemia by means of adenosine [7]. CT-FFR improves the specificity and positive predictive value compared to purely morphological cCTA [11]. In contrast to the invasive measurements at specific locations performed during invasive FFR, CT-FFR allows a continuous functional analysis along the entire coronary tree.

CT-FFR can potentially also be used for diagnostic imaging prior to transcatheter aortic valve implantation (TAVI). Studies indicate that the combination of cCTA and CT-FFR can significantly improve diagnostic accuracy in this high-risk group [12] [13] [14] [15] [16] [17] [18] [19] [20]. In spite of its promise, CT-FFR is not yet recommended in the ESC guidelines for routine diagnostic imaging [1] [2] [9]. Due to the lack of prospective comparative studies with long-term data, its use is currently considered supplementary and clear algorithms for action have not been defined [9] [21] [22]. The present review summarizes the technical principles, major developments, and current clinical advancements of CT-FFR and discusses its potential for cardiovascular imaging in the future.

Technical background of CT-FFR

CT-FFR calculation is based on a detailed analysis of cCTA data and uses physical flow models of coronary blood flow. In contrast to invasive fractional flow reserve (FFR), CT-FFR does not determine the functional impact of a coronary stenosis directly by means of intracoronary pressure measurement but rather it simulates this mathematically. Pharmacological hyperemia is modeled mathematically with patient-specific parameters, e.g., myocardial mass, being included in the simulation.

The technical implementation of CT-FFR calculation includes the following steps [23] [24]:

1. Generation of a precise three-dimensional vascular model of the epicardial coronary arteries based on the cCTA image data.

2. Simulation of coronary blood flow assuming standardized microcirculation parameters to visualize hemodynamic changes.

3. Use of numerical flow dynamics models to calculate the pressure gradient along the course of the vessel.

The evaluation is typically performed 10–20 mm distal to the stenosis in order to minimize the effect of the pressure recovery phenomenon in segments with post-stenotic dilation [3]. Studies by Nozaki et al. and Kuch et al. show that a measurement 1–2 cm distal to the lesion correlates better with revascularization decisions than measurements performed directly in the region of maximum stenosis [25] [26].

In addition to the purely morphological degree of stenosis, additional hemodynamic parameters, like peripheral vascular resistance and collateral flow, influence the pressure gradient across a stenosis [3]. CT-FFR takes these variables into consideration by comparing the maximum possible coronary blood flow in the affected segment to that of a healthy reference vessel. As a result of this combination of anatomical and functional information, the hemodynamic relevance of stenoses can be evaluated in a more differentiated manner [3]. CT-FFR values less than 0.75–0.80 are typically interpreted as an indication of a hemodynamically relevant stenosis [27] [28]. Values greater than 0.80 are considered unremarkable. The range between 0.76 and 0.80 is a diagnostic gray area in which additional clinical information or additional testing is recommended [3]. [Fig. 1] shows an example of CT-FFR analysis.

Fig. 1 Flowchart showing the automatic analysis of cCTAs. Curved multiplanar reconstruction images are generated automatically locally via the CT cardiac workflow (e.g., Syngo.via, Siemens Healthineers). While all image data and reconstructions are included in the visual analysis, the axial slices (Siemens Healthineers) or the multiplanar reconstructions (CorEx, Spimed-AI) are processed for FFR calculation depending on the provider. Depending on the software, the result is output as an FFR value and/or CAD-RADS classification.

A main advantage of CT-FFR compared to invasive FFR is the ability to perform a comprehensive continuous calculation of pressure and flow values along the entire coronary tree. This allows a comprehensive functional analysis, particularly in the case of serial or extensive lesions in branched vessel segments, whose hemodynamic significance is difficult to assess visually. The additional modeling provides critical information for making a well-founded treatment decision. [Table 1] provides an overview of central studies on CT-FFR conducted in recent years.

Table 1 Overview of main clinical studies on CT-FFR.

Study	Year	Number of patients	Method	Main result
DISCOVER-FLOW	2011	103	CT-FFR (HeartFlow)	Increased diagnostic accuracy, primarily as a result of improved specificity compared to cCTA alone [29].
Comparison study (Siemens)	2014	53	Siemens cFFR, version 1.4	Sensitivity of 85% (lesion-based) and 94% (patient-based), specificity of 85% and 84%, positive predictive value of 71%, and negative predictive value of 93–97% [30].
NXT	2014	254	HeartFlow	Significantly increased specificity for CT-FFR; AUC 0.90 compared to 0.81 for cCTA (p = 0.0008); high accuracy also in the case of intermediate stenosis [31].
NXT substudy	2016	51	HeartFlow	Significantly higher AUC for CT-FFR (0.93) vs. cCTA (0.68; p = 0.008) [32].
PLATFORM	2016	584	HeartFlow	CT-FFR-based strategy reduced costs while yielding comparable clinical results and quality of life compared to conventional care [33].
ADVANCE	2018	5,083	HeartFlow	CT-FFR changed the treatment decision compared to cCTA alone in 67% of cases [34].
Aarhus study	2018	677	HeartFlow	CT-FFR allows differentiated risk assessment in intermediate stenosis (FFR >0,80 vs. <0,80) [35].
SYNTAX III Revolution	2019	233	HeartFlow	CT-FFR resulted in a change in revascularization strategy in 7% of patients and a change in target vessels in 12% [36].
DEEPVESSEL FFR	2019	63	DEEPVESSEL FFR	Higher diagnostic accuracy regarding the detection of ischemia (AUC of 0.928) compared to cCTA alone [37].
Vancouver study	2019	207	HeartFlow	CT-FFR is an independent predictor of medium-term outcome; pathological CT-FFR value without significant additional benefit in the case of <50% stenosis [38].
FORECAST (RCT)	2021	1,400	HeartFlow	CT-FFR-assisted cCTA reduced the number of invasive angiography examinations without significant differences in cost or clinical outcome [39].
SYNTAX III subgroup	2022	183	HeartFlow	High level of agreement with invasive FFR; lesions in the RCA were predictive for diagnostic discrepancies [40].
TARGET (RCT)	2023	1,216	DEEPVESSEL FFR	Significantly lower rate of invasive angiography examinations in the CT-FFR group; increased revascularization rate without an increase in major adverse events [41].

CT-FFR software solutions: Providers, technology, and clinical integration

In addition to established providers like HeartFlow and Siemens Healthineers, new software solutions for CT-FFR calculation have increasingly been offered in recent years. These primarily use AI-based algorithms and differ with respect to technology, availability (on-site vs. cloud), and options for integration in clinical workflows [37] [42] [43] [44]:

• HeartFlow Inc. (USA): The company offers an FDA-approved solution in which CT-FFR calculation is provided as a cloud-based service. Manual post-processing by specialized technicians is offered as an option. The cost per analysis is currently approximately 1,000 USD.

• Spimed AI (France): The software solution “CorEx” uses AI-assisted algorithms for CT-FFR calculation and focuses on personalized cardiovascular diagnostic imaging. It is available as both an on-site solution and a cloud platform ([Fig. 2]).

• Siemens Healthineers (Germany): The cFFR algorithm is an on-site solution for non-invasive assessment of fractional flow reserve derived from coronary CTA data. A machine learning model, trained on flow simulations of 12000 synthetically generated coronary anatomies, ensures coverage of a wide range of lesion geometries. To facilitate intuitive evaluation, the algorithm provides both quantitative FFR values and a color-coded 3D visualization of the coronary arteries ([Fig. 3] and [Fig. 4]).

• CathWorks (Israel): Developing an on-site solution based on a one-dimensional modeling approach. This is currently in the research phase and is not yet commercially available.

• Keya Medical (China): “DEEPVESSEL FFR” is Keya Medicalʼs AI-based solution specifically optimized for Asian patient populations. The software is designed for quick automated analysis.

• Yukun Technology (China): Developing fully automated deep-learning-based CT-FFR solutions. The software “skCT-FFR”, marketed in Europe as “Careverse CoronaryDoc” and “Careverse FFR”, allows interactive user correction of the results ([Fig. 5]) [45] [46].

The selection between on-site and off-site processing has a direct effect on data availability, IT security, processing time, and cost. An overview of the advantages and disadvantages of the two approaches is provided in [Table 2].

Fig. 2 Two patients with suspicion of coronary heart disease (CHD) were examined by means of coronary CT angiography (cCTA). AI-assisted software (CorEx, Spimed-AI) performs automated analysis of coronary arteries. Patient 1 (A): Longitudinal view of the affected coronary vessel segment and magnified cross-sectional views show luminal narrowing. The software identifies obstructive CHD with luminal narrowing of 70% and a CT-FFR value of <0.8 indicating a hemodynamically relevant stenosis. Patient 2 (B): The software identifies only minimal coronary stenosis (luminal narrowing of 1–24%) without hemodynamic relevance (FFR>0.8). FFR = fractional flow reserve, AI = artificial intelligence, LAD = left anterior descending artery.

Fig. 3 Multiple instances of high-grade stenosis in the proximal and intermediate LAD detected in 53-year-old patient. The noninvasive fractional flow reserve (c) (FFRCT, Siemens Healthineers, cFFR, version 3.5) confirms this (FFR 0.63).

Fig. 4 68-year-old female patient, who underwent a cCTA examination due to suspicion of CHD. High-grade stenosis in the proximal LAD on the cCTA (a) image. The stenosis is also visible on the multiplanar reconstruction (b) with partially calcified and partially non-calcified plaque segments, also verified by CT-FFR (c, Siemens Healthineers, cFFR, version 3.5).

Fig. 5 CT-FFR evaluation of complex serial stenoses of the left anterior descending artery (LAD) using an ultra-high resolution cCTA scan acquired on a photon-counting CT scanner. The analysis was performed using the software Careverse FFR (Yukun Technology, China). The LAD has multiple consecutive, partially calcified stenoses with a gradual drop in FFR. The minimum CT-FFR value is 0.82, which only indicates a limit value for a hemodynamically relevant decrease in perfusion. The lower diagram clearly shows the continuous pressure drop over the course of the LAD. The 3D reconstruction in the middle allows segmental allocation of the flow reserve to the individual coronary branches. The multiplanar reconstructions on the right show the stenoses.

Table 2 Comparison of on-site and off-site processing for CT-FFR analysis.

Criterion	On-site processing	Of-site processing
Advantages	One-time investment costs, possibly case-based license fees Unlimited use for clinical and scientific questions Direct inclusion in the clinical routine without time delay	No additional time expenditure for clinical personnel High degree of analysis standardization Minimal dependence on individual analysis experience
Disadvantages	Time- and personnel-intensive evaluation process Requires targeted training of personnel Quality of results depends on analysis experience Requires high-performance technical infrastructure	Cost per analysis is currently high

Invasive coronary angiography and invasive FFR measurement

Invasive fractional flow reserve (FFR) is based on the calculation of the pressure ratio between the post-stenotic (distal) and the aortic (proximal) mean pressure [3]. A special guide wire with an integrated pressure sensor is placed distal to the stenosis. After administration of a vasodilator – usually adenosine, the pressure measurement is performed during maximum hyperemia. The FFR value is the result of the quotient of the distal intracoronary pressure (Pd) and the proximal pressure (Pp), i.e., FFR = Pd/Pp. A value of 1.0 corresponds to undisrupted perfusion while values <0.80 indicate a hemodynamically significant stenosis [9].

The strength of invasive FFR is the direct measurement of the drop in pressure across a defined lesion under standardized conditions. However, the significance of the examined segment is limited and does not allow a comprehensive functional evaluation of the entire coronary tree. Moreover, the use of invasive FFR in the clinical routine is limited in spite of the substantial body of evidence because it is associated with greater logistical effort, higher costs, and procedural risks [47].

Innovations and application approaches

Technical innovations

The introduction of photon-counting computed tomography (PCD-CT) represents an important advancement in cardiac imaging. It allows higher spatial resolution, a reduction in image noise, and a potential dose reduction as a result of the detection and energy-resolving counting of individual photons directly at the detector [48] [49] [50]. Consequently, coronary plaques can be characterized more precisely. Phantom studies have shown that PCD-CT can visualize non-calcified and lipid-rich plaques more precisely than conventional CT systems [48] [51].

In addition, it was able to be shown that PCD-CT is superior to conventional CT with respect to the ability to evaluate stents and in-stent stenoses [52] [53]. The reduction of blooming artifacts allows a more differentiated plaque and stent evaluation, which can potentially improve the accuracy of CT-FFR calculation [54] [55].

In a monocentric comparison study, Zsarnoczay et al. compared CT-FFR results between photon-counting detectors (PCD) and classic energy-integrating detectors (EID) in 22 patients [56]. There was excellent agreement between the two types of detectors both on the vessel and the patient level.

Brendel et al. reported a high sensitivity (97.2% on the patient level, 96.6% on the vessel level) in the case of AI-assisted detection of coronary stenoses based on PCD-CT data using ultra-high-resolution reconstructions [57]. However, large multicenter studies validating these results are not yet available.

AI-assisted analysis systems

An innovative approach to the improvement of CT-FFR is the complete integration of AI-based software solutions allowing fully automated on-site analysis. Two current studies examined the effectiveness of skCT-FFR software from Shukun Technology (Shanghai, China) [45] [46]. This allows direct analysis on-site without the image data having to be transferred to an external data center.

In a multicenter randomized study including 5000 patients, Guo et al. showed that the use of skCT-FFR made it possible to reduce the number of unnecessary invasive coronary angiography (ICA) examinations by 19.4% without increasing the rate of major adverse cardiovascular events [45].

A further study including 463 patients (600 vessels) showed a diagnostic accuracy of 82% on the patient level and a high level of agreement with invasive FFR. The time needed for calculation was able to be significantly shortened and the success rate of CT-FFR analyses was increased to over 99% [46].

These results highlight the potential of AI-assisted CT-FFR technology, particularly with regard to the improvement of efficiency, workflows, and diagnostic decision-making in cardiovascular imaging. The ability to automatically prioritize abnormal findings in the worklist is an additional advantage here. However, additional external validation studies in Western populations and long-term outcome analyses are needed to ensure the transferability of the results.

However, the combination of photon-counting CT and AI-based CT-FFR analysis could initiate a paradigm shift in noninvasive coronary diagnostic imaging.

Clinical applications and subgroup analyses

CT-FFR is increasingly being examined in specific risk collectives and clinical subgroups. A series of studies focused on patients with diabetes [58] [59] [60] [61]. Due to microangiopathic changes, this group is particularly susceptible to complex coronary changes. CT-FFR was shown to be an independent predictor of major adverse cardiac events in this population in multiple studies [60]. Moreover, a connection between epicardial fat tissue volume and cardiovascular risk was identified [59].

A subanalysis of the ADVANCE cohort showed a significantly reduced ratio of coronary volume to myocardial mass (a parameter derived from CT-FFR) in smokers [61]. Whether this is suitable as a surrogate marker for vascular health was not examined in this study.

Other studies have examined the practical integration of CT-FFR in the clinical routine [62], the comparability of different software solutions [63], the evaluation of individual plaque morphologies [64], and the prognostic importance of functional parameters [65] [66] [67]. Health economics analyses have also been conducted. However, to date, data is largely only available from the USA [68] [69].

Initial studies also show that making decisions preoperatively with the help of CT-FFR prior to bypass operations is associated with a lower complication rate and higher bypass patency rates [70] [71].

In a prospective multicenter study, Mortensen et al. were able to show that CT-FFR values can improve over time during intensive lipid-lowering treatment which highlights its potential for follow-up examinations during treatment [72].

CT-FFR is also suitable for evaluating the total atherosclerotic plaque burden, as shown by Chen et al. [73]. Schuessler et al. described the use of CT-FFR for risk stratification in patients prior to liver transplantation [74].

Kübler et al. examined the diagnostic performance of AI-assisted coronary CT angiography (cCTA) in asymptomatic marathon runners [75]. This can result in overdiagnosis, particularly in populations with a low prevalence of CHD. CT-FFR could be helpful here to refute false-positive morphological findings with functional findings. The results have a high sensitivity and negative predictive value (NPV) but a lower positive predictive value (PPV) indicating a potential overestimation of stenoses. The study shows that CT-FFR can be useful even in populations with a low cardiovascular risk but is associated with specific challenges. The study emphasizes that AI models have a high sensitivity but a comparably low specificity resulting more frequently in false-positive findings [75]. This overdiagnosis can result in unnecessary invasive examinations particularly in populations with a low prevalence of CHD (e.g., athletes).

The role of CT-FFR for patients with planned transcatheter aortic valve implantation (TAVI) has been examined in numerous studies [12] [13] [14] [15] [16] [17] [18] [19] [20] [76] [77] [78].

The goal is more precise identification of hemodynamically relevant coronary stenosis compared to cCTA alone and prevention of unnecessary invasive coronary angiography examinations in this particularly vulnerable patient group ([Fig. 6]).

Fig. 6 Possible benefit of CT-FFR prior to TAVI: On cCTA of an 80-year-old patient prior to TAVI procedure, severe stenosis in the intermediate region of the LAD and the LCX was suspected (a), with a value of 0.91 being calculated for both proximal stenoses in the CT-FFR (b, Siemens Healthineers, cFFR, version 3.5). The CT-FFR result was confirmed by invasive coronary angiography with stenoses of only 30% or 40% being seen on invasive angiography.

Patients with severe aortic valve stenosis are typically older and have multiple comorbidities. Therefore, there is particular interest in reducing the potential burden of contrast administration and invasive interventions [79].

In this constellation, CT-FFR can contribute to the functional evaluation without any additional burden or procedural risks for the patient.

In a subanalysis of their cohort, Steyer et al. showed that major adverse cardiac events after TAVI could be identified predictively by CT-FF [80]. Aquino et al. also reported that the predictive value for the occurrence of major adverse cardiac events was significantly improved by the integration of CT-FFR in existing predictive models (p = 0.002), while no significant effect on the overall mortality was identified (p = 0.67) [81].

CT-FFR in acute and stable angina pectoris: Gatekeeper potential

CT-FFR contributes significantly to the reduction of unnecessary invasive coronary angiography examinations and can thus not only improve the quality of care but also reduce costs. A meta-analysis by Zhuang et al. on acute coronary symptoms showed a pooled sensitivity and specificity of 89% and 71%, respectively, on the patient level and 85% and 82%, respectively, on the vessel level. While the sensitivity of CT-FFR was comparable to that of cCTA, the specificity was superior [82].

Martin et al. demonstrated that CT-FFR was a better predictor for the need for coronary revascularization and major adverse cardiac events than triple rule-out CT alone in an acute situation (odds ratio: 3.4 vs. 2.2) [83].

A current study by Meier et al. showed that CT-FFR has better diagnostic accuracy than cCTA even in high-risk NSTEMI patients and helps to rule out hemodynamically insignificant stenosis [84]. As a result, CT-FFR was able to reduce the number of unnecessary invasive interventions.

Fischer at al. showed that patients with a CT-FFR > 0.8 had a low risk of major adverse cardiac events [85]. Madsen et al. confirmed this protective effect in patients with chronic angina pectoris [86], which indicates that unnecessary invasive angiography examinations can also be avoided in this group.

This also applies to patients with chronic angina pectoris, and a CT-FFR-based strategy can result in a significant reduction in the number of invasive interventions in this group [87] [88].

A large, randomized study by Yang J et al. including 1216 patients showed that the percentage of patients with stable CHD who underwent an invasive coronary angiography examination was significantly lower in the CT-FFR group [41]. Interestingly, the revascularization rate in this cohort was higher but without an increase in the rate of major adverse cardiac events [41].

These events are supported by a meta-analysis by Di Pietro et al. who documented a lower angiography rate but higher revascularization rate using CT-FFR in patients with stable CHD. No differences in mortality or MACE were seen after one year[89].

CT-FFR also allows additional functional stratification within the CAD-RADS classification. While ischemia is unlikely in CAD-RADS 0–2, CAD-RADS >3 requires a thorough workup [90]. As a noninvasive method, CT-FFR can contribute to ischemia stratification and assume a key role in deciding between invasive and conservative treatments.

Comparison of functional imaging methods

CT-FFR is increasingly considered a functional supplement to cCTA and thus competes with methods like SPECT, CT perfusion, and stress MRI. In the case of stable CHD, multiple studies have shown comparable diagnostic accuracy between CT-FFR and SPECT, with the sensitivity of CT-FFR being higher in some cases [91] [92] [93] [94] [95].

Nørgaard et al. showed that the rate of invasive angiography examinations could be reduced in intermediate stenosis by replacing myocardial perfusion imaging with CT-FFR in otherwise stable coronary heart disease [96].

For risk assessment, Miyajima K et al. showed that the diagnostic accuracy of CT-FFR (AUC 0.81) was significantly superior to SPECT (AUC 0.64, p = 0.0239) in lesions with stenosis ≥ 50% on cCTA [97].

CT-FFR and CT perfusion are considered complementary methods. Combined use can improve specificity without negatively impacting sensitivity[98] [99]. The results from Soschynski et al. similarly show that no significant difference in diagnostic accuracy was able to be detected between cCTA in combination with CT-FFR and cCTA in combination with CT perfusion with respect to the detection of hemodynamically relevant coronary stenosis [10]. Based on their results, the authors recommend a sequential diagnostic approach in which an initial cCTA examination with CT-FFR is supplemented by CT perfusion to further increase the specificity without negatively impacting the sensitivity [10].

Comparisons with cardiac MRI show that CT-FFR has similar diagnostic performance on the vessel level. However, MRI can be superior on the patient level due to higher specificity. A smaller systematic analysis including 110 patients from Denmark with stable angina pectoris yielded comparable results regarding the diagnostic accuracy of cCTA, CT-FFR, and cardiac MRI. The sensitivity with regard to predicting revascularization was highest for CT-FFR, while the specificity was highest for MRI [100] [101] [102].

Cost-benefit ratio

Multiple studies examined the economic impact of CT-FFR compared to conventional strategies. Numerous studies of varying size on the clinical application of CT-FFR are available, but the number of current cost-benefit analyses is significantly smaller. In a study including 96 patients from DISCOVER-FLOW, Hlatky et al. show a cost savings of 30% for the selective use of CT-FFR to identify patients who need a coronary intervention [103].

Similar savings (32%) with a simultaneous reduction in the event rate (–19%) were reported by Kimura et al. in a Japanese cohort [104].

A retrospective analysis by Graby et al. in Great Britain yielded better cost efficiency when using CT-FFR starting at a degree of stenosis of >50% [105].

In Germany, the study by Colleran et al. including 116 patients showed that the cost per patient was able to be significantly reduced by decreasing the number of necessary invasive angiography examinations [88]. A total reduction of 40 angiography examinations was able to be achieved in the study since relevant CHD was able to be ruled out in advance by the combination of cCTA and CT-FFR, without the occurrence of major adverse cardiac events in a follow-up period of one year.

In contrast, Mittal et al. documented higher costs per patient compared to stress imaging with SPECT or MRI based on data from almost 2,300 patients [106].

On the whole, the studies show savings potential for CT-FFR, particularly in the case of targeted use. However, a robust health economics analysis requires additional prospective studies – particularly under consideration of European healthcare systems.

Limitations

The use of CT-FFR has multiple technical and systemic limitations including the limited ability to evaluate complete coronary occlusions since the underlying algorithms are dependent on patent vascular structures. Anatomical variants, e.g., aberrant or missing coronary arteries, can affect modeling and interpretation.

The accuracy of CT-FFR depends greatly on the quality of the underlying image data. Unfavorable contrast distribution, movement caused by arrhythmia, or insufficient breath-hold capability can cause artifacts and limit the analysis [8] [107].

After revascularization, e.g., in the case of stent implantation or bypass operations, the diagnostic significance of CT-FFR is limited. Metal artifacts can have a significant impact on segmentation and flow modeling. To date, only a few studies have shown reliable evaluation of in-stent stenoses [108] [109].

Smaller vessels and side branches are often not included in the flow calculation, thereby reducing the accuracy in certain clinical scenarios [110]. Advances in software development could help here in the future [111].

The evaluation of CT-FFR requires manual post-processing experience, particularly in the case of semiautomatic solutions. The interactive correction of segmentation or vessel contours can result in interindividual differences and requires specialized knowledge.

Moreover, the demands regarding processing power and IT infrastructure are still high. The cost of software licenses and the limited number of validated applications are an impediment to broad clinical implementation. Widespread routine use is currently only economically feasible on a limited basis.

Future developments should target an improvement in image quality, a reduction of artifacts, and standardization of algorithms across different manufacturers. The robustness of existing AI models also needs to be increased in order to minimize false-positive results [72].

Additional studies directly comparing the individual software applications are not yet available. The results of individual studies using a specific software currently cannot be transferred to other software applications given the generally low level of standardization between software providers.

Conclusion and outlook

Current studies show the high diagnostic and prognostic potential of CT-FFR in various patient collectives. As a result of the integration of photon-counting CT, deep learning algorithms, and AI-assisted workflows, further development of noninvasive functional imaging can be expected.

In spite of technological advances, broad clinical use remains limited. Technical challenges, high infrastructure demands, and insufficient standardization of software solutions complicate broad implementation. A comparison of different software providers has not yet been performed.

A well-founded, clinical health economics analysis requires prospective, long-term multicenter studies, ideally with validation across manufacturers and standardized workflow.

CT-FFR cannot replace invasive diagnostic imaging but could be a useful supplement, particularly in the case of complex stenoses or multiple stenoses in which visual assessment alone is not sufficient. The possibility to automatically prioritize pathological cases could also increase reporting efficiency.

The increasing availability of CT-FFR and its greater applicability in the clinical routine are indications of the possibility of exciting and promising developments in the future. However, use in the clinical routine is not yet economically feasible and is mainly limited to research and the analysis of individual cases. Broad clinical implementation requires approved software solutions that can be implemented on-site and can be integrated directly into existing diagnostic workflows. Therefore, in the long term, CT-FFR could become an established part of patient-specific cardiovascular risk stratification.

Conflict of Interest

The authors declare that they have no conflict of interest.

• References

• 1 Knuuti J, Wijns W, Saraste A. et al. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J 2020; 41 (03) 407-477
• 2 Soschynski M, Hagar MT, Taron J. et al. Update for the Performance of CT Coronary Angiography – Evidence-Based Application and Technical Guidance According to Current Consensus Guidelines and Practical Advice from the Clinical Routine. Rofo 2022; 194 (06) 613-624
• 3 Gutberlet M, Krieghoff C, Gohmann R. Is FFRCT a game changer in coronary CT angiography?. Herz 2020; 45 (05) 431-440
• 4 Beschluss des Gemeinsamen Bundesausschusses über eine Änderung der Richtlinie Methoden vertragsärztliche Versorgung (MVV-RL): Computertomografie-Koronarangiografie bei Verdacht auf eine chronische koronare Herzkrankheit vom 18.01.2024. https://www.g-ba.de/downloads/39–261–6418/2024–01–18_MVV-RL_CCTA-KHK.pdf
• 5 Coronary computed tomography angiography (with or without functional evaluation) for the diagnosis of chronic coronary heart disease: IQWiG Reports – Commission No. D22–01 Institute for Quality and Efficiency in Health Care (IQWiG). Cologne (Germany): Institute for Quality and Efficiency in Health Care (IQWiG); 2024 Mar 8.
• 6 Chen J, Wetzel LH, Pope KL. et al. FFRCT: Current Status. AJR Am J Roentgenol 2021; 216 (03) 640-648
• 7 Min JK, Taylor CA, Achenbach S. et al. Noninvasive Fractional Flow Reserve Derived From Coronary CT Angiography: Clinical Data and Scientific Principles. JACC Cardiovasc Imaging 2015; 8 (10) 1209-1222
• 8 Asher A, Singhal A, Thornton G.. et al. FFRCT derived from computer tomography angiography: the experience in the UK. Expert Review of Cardiovascular Therapy 2018;
• 9 Soschynski M, Taron J, Schlett CL. et al. Update on coronary CT-more than just anatomical imaging?. : Current guidelines and functional CT techniques for the quantification of stenoses Radiologe 2020; 60 (12) 1131-1141
• 10 Soschynski M, Storelli R, Birkemeyer C. et al. CT Myocardial Perfusion and CT-FFR versus Invasive FFR for Hemodynamic Relevance of Coronary Artery Disease. Radiology 2024; 312 (02) e233234
• 11 Mathew RC, Gottbrecht M, Salerno M. Computed Tomography Fractional Flow Reserve to Guide Coronary Angiography and Intervention. Interv Cardiol Clin 2018; 7 (03) 345-354
• 12 Renker M, Schoepf UJ, Kim WK. Combined CT Coronary Artery Assessment and TAVI Planning. Diagnostics (Basel) 2023; 13 (07) 1327
• 13 Michail M, Ihdayhid AR, Comella A. et al. Feasibility and Validity of Computed Tomography-Derived Fractional Flow Reserve in Patients with Severe Aortic Stenosis: The CAST-FFR Study. Circ. Cardiovasc. Interv 2021; 14: e009586
• 14 Gohmann RF, Pawelka K, Seitz P. et al. Combined cCTA and TAVR Planning for Ruling Out Significant CAD: Added Value of ML-Based CT-FFR. JACC Cardiovasc. Imaging 2022; 15: 476-486
• 15 Brandt V, Schoepf UJ, Aquino GJ. et al. Impact of machine-learning-based coronary computed tomography angiography-derived fractional flow reserve on decision-making in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement. Eur. Radiol 2022; 32: 6008-6016
• 16 Aquino GJ, Abadia AF, Schoepf UJ. et al. Coronary CT Fractional Flow Reserve before Transcatheter Aortic Valve Replacement: Clinical Outcomes. Radiology 2022; 302: 50-58
• 17 Langenbach MC, Langenbach IL, Foldyna B. et al. Advanced CT measures of coronary artery disease with intermediate stenosis in patients with severe aortic valve stenosis. Eur Radiol 2024; 34 (08) 4897-4908
• 18 Wienemann H, Langenbach MC, Mauri V. et al. Feasibility and Comparison of Resting Full-Cycle Ratio and Computed Tomography Fractional Flow Reserve in Patients with Severe Aortic Valve Stenosis. J Cardiovasc Dev Dis 2022; 9 (04) 116
• 19 Brendel JM, Walterspiel J, Hagen F. et al. Coronary artery disease evaluation during transcatheter aortic valve replacement work-up using photon-counting CT and artificial intelligence. Diagn Interv Imaging 2024; 105 (7/8) 273-280
• 20 Becker LM, Peper J, van Ginkel DJ. et al. Coronary CTA and CT-FFR in trans-catheter aortic valve implantation candidates: a systematic review and meta-analysis. Eur Radiol 2024;
• 21 Celeng C, Leiner T, Maurovich-Horvat P. Anatomical and functional computed tomography for diagnosing hemodynamically significant coronary artery disease: a meta-analysis. JACC Cardiovasc Imaging 2019; 12: 1316-1325
• 22 Nørgaard BL, Terkelsen CJ, Mathiassen ON. et al. Coronary CT angiographic and flow reserve-guided management of patients with stable ischemic heart disease. J Am Coll Cardiol 2018; 72: 2123-2134
• 23 Rabbat MG, Berman DS, Kern M. et al. Interpreting results of coronary computed tomography angiography-derived fractional flow reserve in clinical practice. J Cardiovasc Comput Tomogr 2017; 11 (05) 383-388
• 24 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 (22) 2233-2241
• 25 Nozaki YO, Fujimoto S, Kawaguchi YO. et al. Prognostic value of the optimal measurement location of on-site CT-derived fractional flow reserve. J Cardiol 2022; 80 (01) 14-21
• 26 Kueh SH, Mooney J, Ohana M. et al. Fractional flow reserve derived from coronary computed tomography angiography reclassification rate using value distal to lesion compared to lowest value. J Cardiovasc Comput Tomogr 2017; 11 (06) 462-467
• 27 Gassenmaier S, Tsiflikas I, Greulich S. et al. Prevalence of pathological FFRCT values without coronary artery stenosis in an asymptomatic marathon runner cohort. Eur Radiol 2021; 31 (12) 8975-8982
• 28 De Bruyne B, Pijls NH, Kalesan B. et al. Fractional flow reserve-guided PCI versus medical therapy in stable coronary disease. N Engl J Med 2012; 367: 991-1001
• 29 Koo BK, Erglis A, Doh JH. 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. Am Coll Cardiol 2011; 58 (19) 1989-1997
• 30 Renker M, Schoepf UJ, Wang R. et al. Comparison of diagnostic value of a novel noninvasive coronary computed tomography angiography method versus standard coronary angiography for assessing fractional flow reserve. Am J Cardiol 2014; 114 (09) 1303-1308
• 31 Nørgaard BL, Leipsic J, Gaur S. 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 (12) 1145-1155
• 32 Ko BS, Wong DT, Nørgaard BL. et al. Diagnostic Performance of Transluminal Attenuation Gradient and Noninvasive Fractional Flow Reserve Derived from 320-Detector Row CT Angiography to Diagnose Hemodynamically Significant Coronary Stenosis: An NXT Substudy. Radiology 2016; 279 (01) 75-83
• 33 Douglas PS, De Bruyne B, Pontone G. et al. 1-year outcomes of FFR CT guided care in patients with suspected coronary disease: the PLATFORM Study. J Am Coll Cardiol 2016; 68: 435-445
• 34 Kitabata H, Leipsic J, Patel MR. et al. Incidence and predictors of lesion-specific ischemia by FFRCT: Learnings from the international ADVANCE registry. J. Cardiovasc Comput Tomogr 2018; 12 (02) 95-100
• 35 Nørgaard BL, Terkelsen CJ, Mathiassen ON. et al. Coronary CT Angiographic and Flow Reserve-Guided Management of Patients With Stable Ischemic Heart Disease. J Am Coll Cardiol 2018; 72 (18) 2123-2134
• 36 Andreini D, Modolo R, Katagiri Y. et al. Impact of Fractional Flow Reserve Derived From Coronary Computed Tomography Angiography on Heart Team Treatment Decision-Making in Patients With Multivessel Coronary Artery Disease: Insights From the SYNTAX III REVOLUTION Trial. SYNTAX III REVOLUTION Investigators. Circ Cardiovasc Interv 2019; 12 (12) e007607
• 37 Wang ZQ, Zhou YJ, Zhao YX. et al. Diagnostic accuracy of a deep learning approach to calculate FFR from coronary CT angiography. J Geriatr Cardiol 2019; 16 (01) 42-48
• 38 McNabney CG, Sellers SL, Wilson RJA. et al. Prognosis of CT-derived Fractional Flow Reserve in the Prediction of Clinical Outcomes. Radiol Cardiothorac Imaging 2019; 1 (02) e190021
• 39 Curzen N, Nicholas Z, Stuart B. 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 (37) 3844-3852
• 40 Kawashima H, Kogame N, Ono M. et al. Diagnostic concordance and discordance between angiography-based quantitative flow ratio and fractional flow reserve derived from computed tomography in complex coronary artery disease. J Cardiovasc Comput Tomogr 2022; 16 (04) 336-342
• 41 Yang J, Shan D, Wang X. et al. On-Site Computed Tomography-Derived Fractional Flow Reserve to Guide Management of Patients With Stable Coronary Artery Disease: The TARGET Randomized Trial. Circulation 2023; 147 (18) 1369-1381
• 42 Taron J, Foldyna B, Eslami P. et al. Cardiac Computed Tomography – More Than Coronary Arteries? A Clinical Update. Rofo 2019; 191 (09) 817-826
• 43 Ding Y, Li Q, Chen Q. et al. Diagnostic performance of a novel automated CT-derived FFR technology in detecting hemodynamically significant coronary artery stenoses: A multicenter trial in China. Am Heart J 2023; 265: 180-190
• 44 Yun CH, Hung CL, Wen MS. et al. CT Assessment of Myocardial Perfusion and Fractional Flow Reserve in Coronary Artery Disease: A Review of Current Clinical Evidence and Recent Developments. Korean J Radiol 2021; 22 (11) 1749-1763
• 45 Guo B, Xing W, Hu C. et al. Clinical Effectiveness of Automated Coronary CT-derived Fractional Flow Reserve: A Chinese Randomized Controlled Trial. China CT-FFR Study 3 Group. Radiology 2024; 313 (01) e233354
• 46 Guo B, Jiang M, Guo X. et al. Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD. Sci Bull (Beijing) 2024; 69 (10) 1472-1485
• 47 Tesche C, De Cecco CN, Albrecht MH. et al. Coronary CT Angiography-derived Fractional Flow Reserve. Radiology 2017; 285 (01) 17-33
• 48 Hagen F, Soschynski M, Weis M. et al. Photon-counting computed tomography – clinical application in oncological, cardiovascular, and pediatric radiology. Rofo 2024; 196 (01) 25-35
• 49 Stein T, Rau A, Russe MF. et al. Photon-Counting Computed Tomography – Basic Principles, Potential Benefits, and Initial Clinical Experience. Rofo 2023; 195 (08) 691-698
• 50 Vecsey-Nagy M, Varga-Szemes A, Schoepf UJ. et al. Ultra-high resolution coronary CT angiography on photon-counting detector CT: bi-centre study on the impact of quantum iterative reconstruction on image quality and accuracy of stenosis measurements. Eur J Radiol 2024; 176: 111517
• 51 Si-Mohamed SA, Boccalini S, Lacombe H.. et al. Coronary CT Angiography with Photon-counting CT: First-In-Human Results. Radiology 2022; 303 (02) 303-313
• 52 Mannil M, Hickethier T, Spiczak von J.. et al. Photon-Counting CT: High-Resolution Imaging of Coronary Stents. Invest Radiol 2018; 53 (03) 143-149
• 53 Sigovan M, Si-Mohamed S, Bar-Ness D.. et al. Feasibility of improving vascular imaging in the presence of metallic stents using spectral photon counting CT and K-edge imaging. Sci Rep 2019; 9 (01) 19850
• 54 Wolf EV, Halfmann MC, Schoepf UJ. et al. Intra-individual comparison of coronary calcium scoring between photon counting detector- and energy integrating detector-CT: effects on risk reclassification, Front Cardiovasc Med. 2023; 9: 1053398
• 55 Wolf EV, Gnasso C, Schoepf UJ. et al. Intra-individual comparison of coronary artery stenosis measurements between energy-integrating detector CT and photoncounting detector CT. Imaging 2023; 15 (10)
• 56 Zsarnoczay E, Pinos D, Schoepf UJ. et al. Intra-individual comparison of coronary CT angiography-based FFR between energy-integrating and photon-counting detector CT systems. Int J Cardiol 2024; 399: 131684
• 57 Brendel JM, Walterspiel J, Hagen F. et al. Coronary artery disease detection using deep learning and ultrahigh-resolution photon-counting coronary CT angiography. Diagn Interv Imaging 2024; S2211–5684(24)00209–2
• 58 Tomizawa N, Fujimoto S, Takahashi D. et al. Energy loss is related to CT fractional flow reserve progression in type 2 diabetes mellitus patients. Am Heart J Plus 2023; 35: 100328
• 59 Wang Z, Huang Z, Wang X. The Correlation between Region-specific Epicardial Adipose Tissue and Myocardial Ischemia Defined by CT-FFR in Type 2 Diabetes Mellitus Patients. Curr Med Imaging 2024;
• 60 Lan Z, Ding X, Yu Y. et al. CT-derived fractional flow reserve for prediction of major adverse cardiovascular events in diabetic patients. Cardiovasc Diabetol 2023; 22 (01) 65
• 61 Holmes KR, Gulsin GS, Fairbairn TA. et al. Impact of Smoking on Coronary Volume-to-Myocardial Mass Ratio: An ADVANCE Registry Substudy. Radiol Cardiothorac Imaging 2024; 6 (02) e220197
• 62 Randhawa MK, Takigami AK, Thondapu V. et al. Selective Use of CT Fractional Flow at a Large Academic Medical Center: Insights from Clinical Implementation after 1 Year of Practice. Radiol Cardiothorac Imaging 2024; 6 (02) e230073
• 63 Li J, Yang Z, Sun Z. et al. CT coronary fractional flow reserve based on artificial intelligence using different software: a repeatability study. BMC Med Imaging 2024; 24 (01) 288
• 64 Nozue T, Takamura T, Fukui K. et al. Plaque Volume and Morphology are Associated with Fractional Flow Reserve Derived from Coronary Computed Tomography Angiography. J Atheroscler Thromb 2019; 26 (08) 697-704
• 65 Ouyang LN, Wang R, Wu Q. et al. Exploratory Development of a Prognostic Model for Coronary Artery Disease Utilizing CT-FFR Derived Functional Duke Jeopardy Score. Acad Radiol 2024; S1076–6332(24)00883–3
• 66 Yi Y, Li D, Xu C. et al. Prognostic significance of stress dynamic myocardial CT perfusion: comparison with CT-FFR and CT angiography stenosis-the multicenter VALIDITY trial. Eur Radiol 2024;
• 67 McNabney CG, Sellers SL, Wilson RJA. et al. Prognosis of CT-derived Fractional Flow Reserve in the Prediction of Clinical Outcomes. Radiol Cardiothorac Imaging 2019; 1 (02) e190021
• 68 Burch RA, Siddiqui TA, Tou LC. et al. The Cost Effectiveness of Coronary CT Angiography and the Effective Utilization of CT-Fractional Flow Reserve in the Diagnosis of Coronary Artery Disease. J Cardiovasc Dev Dis 2023; 10 (01) 25
• 69 Brendel JM, Nikolaou K, Foldyna B. Plaque characterization and individualized risk assessment. Radiologie, (Heidelb) 2024; 64 (12) 946-955
• 70 Fournier S, Toth GG, De Bruyne B. et al. Six-year follow-up of fractional flow reserve-guided versus angiography-guided coronary artery bypass graft surgery. Circ Cardiovasc Interv 2018; 11 (06) e006368
• 71 Glineur D, Grau JB, Etienne PY. et al. Impact of preoperative fractional flow reserve on arterial bypass graft anastomotic function: the IMPAG trial. Eur Heart J 2019; 40 (29) 2421-2428
• 72 Mortensen MB, Sand NP, Busk M. et al. Influence of intensive lipid-lowering on CT derived fractional flow reserve in patients with stable chest pain: Rationale and design of the FLOWPROMOTE study. Clin Cardiol 2022; 45 (10) 986-994
• 73 Chen M, Almeida SO, Sayre JW. et al. Distal-vessel fractional flow reserve by computed tomography to monitor epicardial coronary artery disease. Eur Heart J Cardiovasc Imaging 2024; 25 (02) 163-172
• 74 Schuessler M, Saner F, Al-Rashid F. et al. Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) in patients before liver transplantation using CT-FFR machine learning algorithm. Eur Radiol 2022; 32 (12) 8761-8768
• 75 Kübler J, Brendel JM, Küstner T. et al. Artificial intelligence-enhanced detection of subclinical coronary artery disease in athletes: diagnostic performance and limitations. Int J Cardiovasc Imaging 2024; 40 (12) 2503-2511
• 76 Gohmann RF, Schug A, Krieghoff C. et al. Interrater Variability of ML-Based CT-FFR in Patients without Obstructive CAD before TAVR: Influence of Image Quality, Coronary Artery Calcifications, and Location of Measurement. J Clin Med 2024; 13 (17) 5247
• 77 Peper J, Becker LM, van den Berg H. et al. Diagnostic Performance of CCTA and CT-FFR for the Detection of CAD in TAVR Work-Up. JACC Cardiovasc Interv 2022; 15 (11) 1140-1149
• 78 Khoo JK, Sellers S, Fairbairn T. et al. Feasibility and Utility of Anatomical and Physiological Evaluation of Coronary Disease With Cardiac CT in Severe Aortic Stenosis (FUTURE-AS Registry): Rationale and Design. J Soc Cardiovasc Angiogr Interv 2024; 3: 101293
• 79 Felbel D, Buck C, Riedel N. et al. Combined Computed Coronary Tomography Angiography and Transcatheter Aortic Valve Implantation (TAVI) Planning Computed Tomography Reliably Detects Relevant Coronary Artery Disease Pre-TAVI. J Clin Med 2024; 13 (16) 4885
• 80 Steyer A, Puntmann VO, Nagel E. et al. Coronary Artery Disease Assessment via On-Site CT Fractional Flow Reserve in Patients Undergoing Transcatheter Aortic Valve Replacement. Radiol Cardiothorac Imaging 2024; 6 (02) e230096
• 81 Aquino GJ, Abadia AF, Schoepf UJ. et al. Coronary CT Fractional Flow Reserve before Transcatheter Aortic Valve Replacement: Clinical Outcomes. Radiology 2022; 302 (01) 50-58
• 82 Zhuang B, Wang S, Zhao S. et al. Computed tomography angiography-derived fractional flow reserve (CT-FFR) for the detection of myocardial ischemia with invasive fractional flow reserve as reference: systematic review and meta-analysis. Eur Radiol 2020; 30 (02) 712-725
• 83 Martin SS, Mastrodicasa D, van Assen M. et al. Value of Machine Learning-based Coronary CT Fractional Flow Reserve Applied to Triple-Rule-Out CT Angiography in Acute Chest Pain. Radiol Cardiothorac Imaging 2020; 2 (03) e190137
• 84 Meier D, Andreini D, Cosyns B. et al. Usefulness of FFR-CT to exclude haemodynamically significant lesions in high-risk NSTE-ACS. EuroIntervention 2025; 21 (01) 73-81
• 85 Fischer AM, van Assen M, Schoepf UJ. et al. Non-invasive fractional flow reserve (FFR CT ) in the evaluation of acute chest pain – Concepts and first experiences. Eur J Radiol 2021; 138: 109633
• 86 Madsen KT, Nørgaard BL, Øvrehus KA. et al. Prognostic Value of Coronary CT Angiography-derived Fractional Flow Reserve on 3-year Outcomes in Patients with Stable Angina. Radiology 2023; 308 (03) e230524
• 87 Bråten AT, Fossan FE, Muller LO. et al. Automated computed tomography-derived fractional flow reserve model for diagnosing haemodynamically significant coronary artery disease: a prospective validation study. Eur Heart J Imaging Methods Pract 2024; 2 (03) qyae102
• 88 Colleran R, Douglas PS, Hadamitzky M. et al. An FFRCT diagnostic strategy versus usual care in patients with suspected coronary artery disease planned for invasive coronary angiography at German sites: one-year results of a subgroup analysis of the PLATFORM (Prospective Longitudinal Trial of FFRCT: Outcome and Resource Impacts) study. Open Heart 2017; 4 (01) e000526
• 89 Di Pietro G, Improta R, De Filippo O. et al. Clinical impact of CCT-FFR as first-strategy in patients with symptomatic stable coronary artery disease: A systematic review and meta-analysis. Cardiovasc Comput Tomogr 2025; S1934–5925(25)00046–2
• 90 Becker LM, Peper J, van Nes SH. et al. Non-invasive physiological assessment of coronary artery obstruction on coronary computed tomography angiography. Neth Heart J 2024; 32 (11) 397-404
• 91 Sand NPR, Veien KT, Nielsen SS. et al. Prospective Comparison of FFR Derived From Coronary CT Angiography With SPECT Perfusion Imaging in Stable Coronary Artery Disease: The ReASSESS Study. JACC Cardiovasc Imaging 2018; 11 (11) 1640-1650
• 92 Nissen L, Winther S, Westra J. et al. Diagnosing coronary artery disease after a positive coronary computed tomography angiography: the Dan-NICAD open label, parallel, head to head, randomized controlled diagnostic accuracy trial of cardiovascular magnetic resonance and myocardial perfusion scintigraphy. Eur Heart J Cardiovasc Imaging 2018; 19 (04) 369-377
• 93 Peper J, Schaap J, Kelder JC. et al. Added value of computed tomography fractional flow reserve in the diagnosis of coronary artery disease. Sci Rep 2021; 11 (01) 6748
• 94 Okonogi T, Kawasaki T, Koga H. et al. Comparison of Diagnostic Performance of Fractional Flow Reserve Derived from Coronary Computed Tomographic Angiography Versus Single-Photon Emission Computed Tomographic Myocardial Perfusion Imaging. Am J Cardiol 2021; 159: 36-43
• 95 Driessen RS, Danad I, Stuijfzand WJ. et al. Comparison of Coronary Computed Tomography Angiography, Fractional Flow Reserve, and Perfusion Imaging for Ischemia Diagnosis. J Am Coll Cardiol 2019; 73 (02) 161-173
• 96 Nørgaard BL, Gormsen LC, Bøtker HE. et al. Myocardial Perfusion Imaging Versus Computed Tomography Angiography-Derived Fractional Flow Reserve Testing in Stable Patients With Intermediate-Range Coronary Lesions: Influence on Downstream Diagnostic Workflows and Invasive Angiography Findings. J Am Heart Assoc 2017; 6 (08) e005587
• 97 Miyajima K, Motoyama S, Sarai M. et al. On-site assessment of computed tomography-derived fractional flow reserve in comparison with myocardial perfusion imaging and invasive fractional flow reserve. Heart Vessels 2020; 35 (10) 1331-1340
• 98 Pontone G, Baggiano A, Andreini D. et al. Dynamic Stress Computed Tomography Perfusion With a Whole-Heart Coverage Scanner in Addition to Coronary Computed Tomography Angiography and Fractional Flow Reserve Computed Tomography Derived. JACC Cardiovasc Imaging 2019; 12 (12) 2460-2471
• 99 Tanaka H, Takahashi T, Ohashi N. et al. Diagnostic value of thallium-201 myocardial perfusion IQ-SPECT without and with computed tomography-based attenuation correction to predict clinically significant and insignificant fractional flow reserve: A single-center prospective study. Medicine (Baltimore) 2017; 96 (51) e9275
• 100 Rønnow Sand NP, Nissen L, Winther S. et al. Prediction of Coronary Revascularization in Stable Angina: Comparison of FFRCT With CMR Stress Perfusion Imaging. JACC Cardiovasc Imaging 2020; 13 (04) 994-1004
• 101 Lossnitzer D, Klenantz S, Andre F. et al. Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia. BMC Cardiovasc Disord 2022; 22 (01) 34
• 102 Guo W, Zhao S, Xu H. et al. Comparison of machine learning-based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease. Eur Radiol 2024; 34 (09) 5654-5665
• 103 Hlatky MA, Saxena A, Koo BK. et al. Projected costs and consequences of computed tomography-determined fractional flow reserve. Clin Cardiol 2013; 36 (12) 743-748
• 104 Kimura T, Shiomi H, Kuribayashi S. et al. Cost analysis of non-invasive fractional flow reserve derived from coronary computed tomographic angiography in Japan. Cardiovasc Interv Ther 2015; 30 (01) 38-44
• 105 Graby J, Metters R, Kandan SR. et al. Real-world clinical and cost analysis of CT coronary angiography and CT coronary angiography-derived fractional flow reserve (FFR(CT))-guided care in the National Health Service. Clin Radiol 2021; 76 (11) 862.e19-862.e28
• 106 Mittal TK, Hothi SS, Venugopal V. et al. The Use and Efficacy of FFR-CT: Real-World Multicenter Audit of Clinical Data With Cost Analysis. JACC Cardiovasc Imaging 2023; 16 (08) 1056-1065
• 107 Min JK, Koo BK, Erglis A. et al. Effect of image quality on diagnostic accuracy of noninvasive fractional flow reserve: results from the prospective multicenter international DISCOVER-FLOW study. Cardiovasc Comput Tomogr 2012; 6 (03) 191-199
• 108 Xu C, Yi Y, Xu M. et al. Diagnostic value of deep learning reconstruction-based subtraction CT-FFR in patients with calcified-related stenosis or stent implantation. Quant Imaging Med Surg 2025; 15 (02) 1599-1612
• 109 Tang CX, Guo BJ, Schoepf JU. et al. Feasibility and prognostic role of machine learning-based FFRCT in patients with stent implantation. Eur Radiol 2021; 31 (09) 6592-6604
• 110 Renker M, Schoepf UJ, Becher T. et al. Computed tomography in patients with chronic stable angina : Fractional flow reserve measurement. Herz 2017; 42 (01) 51-57
• 111 Lisi C, Moser LJ, Mergen V. et al. Increasing the rate of datasets amenable to CTFFR and quantitative plaque analysis: Value of software for reducing stair-step artifacts demonstrated in photon-counting detector CT. Eur J Radiol Open 2024; 12: 100574

Publication History

Received: 13 April 2025

Accepted after revision: 21 August 2025

Article published online:
15 October 2025