Coronary CT Angiography derived Fractional Flow Reserve 1

This copy is for personal use only. To order printed copies, contact reprints@rsna.org Christian Tesche, MD 2 Carlo N. De Cecco, MD, PhD Moritz H. Albrecht, MD 3 Taylor M. Duguay, BS Richard R. Bayer II, MD Sheldon E. Litwin, MD Daniel H. Steinberg, MD U. Joseph Schoepf, MD Online SA-CME See www.rsna.org/education/search/ry Learning Objectives: After reading the article and taking the test, the reader will be able to: n Describe differences between fractional flow reserve (FFR) derived from coronary CT angiography ( ) determined by using computational fluid dynamics versus machine learning based principles n Explain evidence, validation, and clinical implementation of n Explain limitations and potential future applications of Accreditation and Designation Statement The RSNA is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. The RSNA designates this journal-based SA-CME activity for a maximum of 1.0 AMA PRA Category 1 Credit. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Disclosure Statement The ACCME requires that the RSNA, as an accredited provider of CME, obtain signed disclosure statements from the authors, editors, and reviewers for this activity. For this journal-based CME activity, author disclosures are listed at the end of this article. Coronary CT Angiography derived Fractional Flow Reserve 1 Invasive coronary angiography (ICA) with measurement of fractional flow reserve (FFR) by means of a pressure wire technique is the established reference standard for the functional assessment of coronary artery disease (CAD) (1,2). Coronary computed tomographic (CT) angiography has emerged as a noninvasive method for direct assessment of CAD and plaque characterization with high diagnostic accuracy compared with ICA (3,4). However, the solely anatomic assessment provided with both coronary CT angiography and ICA has poor discriminatory power for ischemia-inducing lesions. FFR derived from standard coronary CT angiography ( ) data sets by using any of several advanced computational analytic approaches enables combined anatomic and hemodynamic assessment of a coronary lesion by a single noninvasive test. Current technical approaches to the calculation of include algorithms based on full- and reduced-order computational fluid dynamic modeling, as well as artificial intelligence deep machine learning (5,6). A growing body of evidence has validated the diagnostic accuracy of techniques compared with invasive FFR. Improved therapeutic guidance has been demonstrated, showing the potential of to streamline and rationalize the care of patients suspected of having CAD and improve outcomes while reducing overall health care costs (7,8). The purpose of this review is to describe the scientific principles, clinical validation, and implementation of various approaches, their precursors, and related imaging tests. q RSNA, 2017 Reviews and Commentary n State of the Art 1 From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science (C.T., C.N.D.C., M.H.A., T.M.D., R.R.B., S.E.L., U.J.S.), and Division of Cardiology, Department of Medicine (R.R.B., S.E.L., D.H.S., U.J.S.), Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr, Charleston, SC 29425-2260. Received November 18, 2016; revision requested January 4, 2017; final revision received February 13; accepted February 27; final version accepted February 28; final review June 19. Address correspondence to U.J.S. (e-mail: schoepf@musc.edu). Current addresses: 2 Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany. 3 Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany. C.T. supported by a grant from the Fulbright Visiting Scholar Program of the U.S. Department of State, Bureau of Educational and Cultural Affairs (ECA). q RSNA, 2017 Radiology: Volume 285: Number 1 October 2017 n radiology.rsna.org 17

Invasive coronary angiography (ICA) has long been considered the reference standard technique for the diagnosis of coronary artery disease (CAD). Decisions regarding revascularization procedures have traditionally been based on the visual assessment of coronary stenosis severity. However, this strategy is being challenged based on increasing recognition of the limitations of visual interpretation of conventional angiographic images. One of the major concerns is considerable interobserver variability in interpretation of coronary angiograms (9). Even the use of quantitative techniques has not substantially improved the predictive value of ICA for identifying the presence of lesion-specific ischemia (2). Even more importantly, we now know that many other factors may collectively influence flow dynamics in a vessel in addition to the degree of narrowing at Essentials nn Current approaches to noninvasively derive fractional flow reserve (FFR) values from coronary CT angiography include fulland reduced-order computational fluid dynamic modeling, as well as artificial intelligence based deep machine learning algorithms. nn FFR derived from coronary CT angiography ( ) shows pooled sensitivity and specificity of 93% and 82%, respectively, to identify lesion-specific ischemia. nn helps avoid unnecessary invasive coronary angiography in up to 61% of patients suspected of having coronary artery disease (CAD). nn holds potential to guide therapeutic decision making with changed treatment strategy in up to 36% of patients suspected of having CAD. nn Initial data suggest that the integration of in CAD management pathways is associated with reduced cost and improved outcomes. any single point. Accordingly, current evidence and guidelines stipulate that the decision to proceed to revascularization should be governed by the hemodynamic significance of a lesion, rather than angiographic severity. Currently, measurement of fractional flow reserve (FFR) is the preferred technique for invasive assessment of flow limitation. An FFR value of less than 0.8 is generally considered to be the best discriminator of flow obstruction and the most relevant predictor of improved clinical outcome after coronary revascularization (10,11). The Fractional Flow Reserve versus Angiography for Guiding Percutaneous Coronary Intervention, or FAME, and the Clinical Outcomes Utilizing Revascularization and Aggressive Drug Evaluation, or COURAGE, trials have demonstrated that patients with flow-obstructive CAD benefit from coronary revascularization (12,13), whereas those without ischemia receive little benefit, or even harm, with revascularization. Further, the Deferral Versus Performance of Percutaneous Coronary Intervention of Non Ischemia-Producing Stenoses, or DEFER, trial has shown that patients with non flow-limiting lesions do not require intervention and show favorable outcomes with use of optimal medical therapy alone, with annual rates of myocardial infarction and mortality of less than 1% (14). Accordingly, evaluation of the hemodynamic significance of coronary stenosis during ICA by using pressure wire derived FFR, an approach that allows combined anatomic and physiologic assessment, has become a cornerstone to determine lesion-specific ischemia and appropriate decision making (15,16). In the absence of proven ischemia via noninvasive assessment, FFR assessment of lesions with anatomic or quantitative grading of stenosis between 50% and 90% severity has a class IA recommendation according to current societal guidelines (17). Invasive FFR Determination Invasive FFR measurements are determined by comparing the pressure distal to a stenosis to the aortic pressure. If a stenosis is flow-limiting, there will be a pressure decrease across the lesion (18,19). FFR is determined during ICA by using a floppy-tipped guide wire with a pressure-sensing transducer, which is then placed across the stenotic coronary lesion to record pressure values. Vasodilation of the epicardial and microvascular bed is achieved by either intravenous or intracoronary administration of adenosine (20). Measurements of pressure proximal and distal to the stenosis of interest are performed during maximal hyperemia. These have been shown to be highly reproducible and unaffected by changes in systemic hemodynamics or microvasculature (21). Additionally, FFR measurements account for the contribution of collateral blood supply because the distal pressure incorporates both antegrade and collateral flow (22). Furthermore, FFR evaluates the epicardial vessel independent of the microvasculature, as it is generally assumed that microvascular dysfunction (if present) is fixed and chronic. In the setting of chronic myocardial infarction, a stenosis in the subtending artery may have little pressure gradient and indicate no lesion-specific ischemia during FFR because it supplies nonviable myocardium, which has low metabolic requirements. https://doi.org/10.1148/radiol.2017162641 Content codes: Radiology 2017; 285:17 33 Abbreviations: AUC = area under the receiver operating characteristics curve CAD = coronary artery disease DeFACTO = Determination of Fractional Flow Reserve by Anatomic CT Angiography DISCOVER-FLOW = Diagnosis of Ischemia-causing Stenoses Obtained Via Noninvasive Fractional flow Reserve FFR = fractional flow reserve = FFR derived from coronary CT angiography ICA = invasive coronary angiography NXT = Analysis of Coronary Blood Flow Using CT Angiography: Next Steps Workstation-based flow computations of coronary blood flow were not carried out in the United States and are not currently approved by the Food and Drug Administration. Cinematic Rendering is not yet approved for clinical use. Conflicts of interest are listed at the end of this article. 18 radiology.rsna.org n Radiology: Volume 285: Number 1 October 2017

FFR measurements generally remain accurate in this situation because the stenosis is not the main factor responsible for reduced myocardial blood flow (21). Thus, stenosis deemed significant at angiography may result in a nonischemic FFR value as the pressure gradient during maximal hyperemia is low due to the low flow to the nonviable myocardium. Conversely, a stenosis that appears less severe angiographically may cause ischemia if the lesion is long, is in tandem with other stenosis, or is proximal to an area of myocardium that has a large perfusion requirement. The term fractional flow reserve is somewhat misleading as the technique directly measures pressure rather than flow. The ability to make inferences about blood flow arises from the fact that when both epicardial and myocardial vascular resistances are minimized by administering vasodilators and assuming microvascular resistance is similar in the presence and absence of an epicardial stenosis, coronary flow becomes proportional to coronary pressure. Thus, for example, an FFR value of 0.75 theoretically corresponds to a maximal myocardial blood flow of only 75% of its normal value. It is well accepted that lesions with FFR values of less than 0.75 are myocardial ischemia inducing, whereas FFR values of greater than 0.80 rule out lesion-specific ischemia, with a negative predictive value of greater than 95% (23,24). Thus, a gray zone exists for FFR values between 0.75 and 0.80. In the DEFER trial, an FFR threshold of 0.75 or less was used to indicate lesionspecific ischemia, whereas in the FAME I and FAME II multicenter trials, lesionspecific ischemia was defined as FFR values of 0.80 or less (2,25). This cutoff value of 0.80 or less is the number recommended in current guidelines (26). FFR-guided decision making, either for optimal medical therapy or the appropriate revascularization procedure, has demonstrated improved event-free survival of patients and a substantial reduction in health care costs compared with angiographic guidance alone (12,25,27). However, despite the large body of evidence supporting the use of invasive FFR and firm endorsement by current guidelines, the clinical implementation of FFR-informed treatment decision making is relatively limited, with FFR guidance integrated in only 10% 20% of revascularization procedures (28,29). Reasons that may explain the underutilization of FFR include its invasive nature, the need for additional expensive instruments, the duration of the procedure, and regional variations in reimbursement. Further, still deeply engrained is the oculostenotic reflex, which is triggered by the notion that patients with visually apparent stenoses may benefit from intervention, even though this theory has been refuted by several investigations (14). Noninvasive Modalities for the Functional Evaluation of CAD Several imaging techniques have been introduced for the noninvasive evaluation of CAD (30). However, currently there is no clinically accepted single noninvasive test that accurately identifies significant stenosis and the corresponding hemodynamic effects of each lesion. Nuclear myocardial perfusion imaging using single photon emission computed tomography (SPECT) is an established procedure that has been used for decades for evaluating myocardial perfusion and subsequent cardiac risk stratification (31). SPECT has been reported to have sensitivity and specificity of up to 88% and 76%, respectively, for detecting obstructive CAD compared with ICA as the reference standard (32,33). Some have argued that the true sensitivity of SPECT is lower than this as many of the frequently cited studies that address the accuracy of SPECT included patients with known CAD, a factor that is recognized to inflate the apparent sensitivity of a test. Another limitation is that SPECT offers only qualitative or semiquantitative determination of myocardial blood flow and is affected by low spatial resolution, which may result in small or subendocardial areas of hypoperfusion going undetected. In addition, attenuation artifact is a well-recognized factor that limits the specificity of SPECT for the detection of myocardial perfusion defects (34,35). However, technetium 99m sestamibi quantitative dynamic SPECT/computed tomography (CT) imaging and newer generation SPECT cameras using solid-state photon detectors show potential to expand quantification of absolute blood flow and flow reserve also in the single-photon domain. These technical refinements may improve evaluation of myocardial ischemia with use of SPECT (36,37). Contrast material enhanced firstpass perfusion cardiac magnetic resonance (MR) imaging performed during pharmacological stress enables semiquantitative assessment of myocardial perfusion (38,39). Stress perfusion MR imaging has been shown to detect CAD with sensitivity and specificity of 89% and 80%, respectively (40,41). Although the diagnostic superiority of stress MR imaging over nuclear testing has been demonstrated, it has not been implemented into clinical practice as the standard method for myocardial perfusion measurement, as it is still perceived as time consuming, expensive, and of limited availability. Over the past decade, coronary CT angiography has evolved into the premier tool for noninvasive interrogation of the coronary arteries (42). The exceedingly high negative predictive value of this test allows rapid, safe exclusion of obstructive CAD (43). Accordingly, coronary CT angiography is increasingly becoming an integral component in guideline-driven CAD management in a broad variety of clinical scenarios (44,45). Coronary CT angiography, as another a priori anatomic imaging test, shares with ICA the limitations for accurately gauging the hemodynamic relevance of a lesion. Careful assessment of stenosis severity is a prerequisite to minimize misinterpretation in coronary CT angiography. Luminal diameter stenosis is most commonly used for the quantification of coronary artery stenosis, followed by area stenosis, minimum lumen diameter, or minimum lumen area (46). The evaluation of lumen area stenosis appears preferable to diameter stenosis when compared with ICA (47,48), owing to irregular arterial lumen Radiology: Volume 285: Number 1 October 2017 n radiology.rsna.org 19

shapes for which diameter assessment may be difficult. Furthermore, minimum lumen area does not require selection of an appropriate reference segment, which eliminates an important error source. However, most clinical studies used a diameter assessment, since ICA as the standard of reference typically employs diameter measurements for its evaluation. Visual stenosis grading is the most commonly performed coronary lumen assessment in clinical practice for coronary CT angiography. Minimum lumen diameter and minimum lumen area of coronary lesions are assessed by using the average dimensions of nonaffected vessel segments immediately proximal and distal to the lesion as a reference for diameter and area stenosis determination. The degree of coronary artery stenosis can be evaluated by using either a qualitative grading system, that is, mild, moderate, severe or in a semiquantitative approach by using the CAD reporting and data system, or CAD-RADS: score 1, none (0%) or minimal (1% 24%); score 2, mild (25% 49% stenosis), score 3, moderate (50% 69% stenosis); score 4, severe (70% 99% stenosis); and score 5, total occlusion (100%), with obstructive flow-limiting CAD being defined as stenosis 50% or greater (49). For quantitative stenosis assessment, most cardiac CT workstations provide software tools for manual or semiautomatic measurements of lumen dimensions by using cross-sectional or longitudinal lumen images. Additionally, common workstations also provide semiautomatic software-based lumen contour detection for stenosis quantification. It is worth noting that semiautomatic lumen assessment showed better positive predictive values for diagnosing CAD when compared with visual stenosis grading with ICA as the reference standard (50). However, this approach requires manual observer interaction and is thus time consuming, which limits its use in clinical practice. Almost all clinical data investigating the diagnostic accuracy of coronary CT angiography in comparison with ICA used visual stenosis estimates and quantitative coronary angiography. In general, visual stenosis grading at coronary CT angiography overestimates lesion severity compared with quantitative coronary angiography (51). Thus, coronary CT angiography shows poor predictive value in identifying hemodynamically significant coronary stenosis. Accordingly, methods to overcome this limitation and to improve the noninvasive detection of lesion-specific ischemia by means of CT are being intensively explored. For instance, myocardial CT perfusion imaging techniques have been introduced, which are based on the evaluation of myocardial contrast material uptake during first arterial pass acquisition. The distribution of contrast material is dependent on arterial blood supply, thus allowing myocardial perfusion defects to be detected as areas of low contrast attenuation (52). To date, both static and dynamic CT myocardial perfusion imaging (MPI) techniques are available (53). Static CT MPI can be derived from routine coronary CT angiography studies acquired for the morphologic assessment of CAD (54). In contrast, dynamic CT MPI cannot be used for the evaluation of coronary arteries and must be performed in addition to coronary CT angiography, typically with use of pharmacologic stress agents, which increase the complexity of this procedure (55 57). Although dynamic CT MPI involves a higher radiation dose in comparison to static CT MPI, this technique allows performing both quantitative and semiquantitative evaluation of the myocardium through the analysis of the derived time-attenuation curves, while static CT MPI only enables qualitative myocardial evaluation (58). Overall, both static and dynamic CT MPI have the potential to provide a comprehensive anatomic and functional evaluation of the heart within a single modality. Both technical approaches have a wide range of reported diagnostic accuracies, with sensitivities of 50% 96% for static CT MPI and 76% 100% for dynamic CT MPI, with corresponding specificities of 68% 98% and 74% 100%, respectively (59). Although the combined approach of coronary CT angiography and CT MPI enables morphologic and functional assessment of CAD with high accuracy and in a truly quantitative fashion (60), the use of CT MPI as an ancillary test to coronary CT angiography is so far restricted to experienced centers. Recently, several additional techniques for functional CT assessment of CAD effects on myocardial blood flow have been introduced. These are all based on image postprocessing analysis of standard coronary CT angiography studies: Transluminal attenuation gradient (TAG) is defined as the contrast opacification gradient along the length of a coronary artery at coronary CT angiography and can be directly calculated from in-lumen contrast medium (61). It has been argued that TAG evaluation may be limited by the temporal uniformity that is disrupted by multiple heartbeat acquisitions and its susceptibility to conditions that influence blood flow, such as vessel branching and CAD (62). Therefore, correction models have been developed to overcome possible imprecision in TAG evaluation (61). Corrected coronary opacification is based on the dephasing of contrast material delivery by relating coronary attenuation to corresponding descending aorta opacification in the same transverse plane (63,64). Corrected coronary opacification is then defined as the quotient of this value derived from proximal and distal reference areas of a coronary stenosis. TAG and corrected coronary opacification show a wide spread in reported diagnostic accuracy for detecting hemodynamically significant CAD, with ranges of sensitivity and specificity of 37% 95% and 76% 97%, respectively (3,61,63,65 69). The wide range in diagnostic accuracy is thought to be related to factors that influence image quality, which is a prerequisite for appropriate TAG evaluation. These factors include heavy calcifications, severe CAD, body mass index, and the cardiac phase selected for image acquisition or reconstruction. Beyond this shortcoming, manual TAG and corrected coronary opacification evaluation is rather time consuming, which to date limits its applicability in a real-world clinical setting. More recently, the determination of lesion-specific ischemia from coronary 20 radiology.rsna.org n Radiology: Volume 285: Number 1 October 2017

CT angiography data by using fractional myocardial mass, a concept of vesselspecific subtended myocardial mass that could reduce anatomic-physiologic mismatch, has been introduced. Fractional myocardial mass is computed by using an approach based on allometric scaling between length of the coronary artery tree and left ventricular myocardial mass (70). Kim et al investigated the diagnostic performance of fractional myocardial mass for the evaluation of lesion-specific ischemia in 724 vessels in 463 patients (71). On a per-vessel level, they demonstrated superior discriminatory power for the ratio of fractional myocardial mass per minimal lumen diameter (FMM/MLD) when compared with diameter stenosis on ICA to detect myocardial ischemia (C-statistics 0.84 vs 0.74, P,.001). The optimal cut-off value of FMM/MLD was 29 g/mm with a sensitivity and specificity of 75% and 77%, respectively. These authors preliminary approach, adding myocardial mass to anatomic stenosis, may further reduce the gap between anatomic and physiologic stenosis severity. Noninvasive derivation of coronary FFR from diagnostic coronary CT angiography ( ) studies is the most recent approach making a strong bid to match the unmet need for a single imaging test that provides both structural and functional information (72 74). The ability to reliably obtain such combined information in a noninvasive fashion has potential to replace the traditional combination of tests required for appropriate, guideline-driven patient management and assume the role of the long-sought gatekeeper to invasive catheterization and revascularization (45). The concepts, current evidence, clinical integration, and future directions of will be discussed in the following sections. Noninvasive is a noninvasive image postprocessing technique that enables the determination of physiologic significance of coronary artery stenosis by using data acquired from standard, routine diagnostic coronary CT angiography studies. Such an approach is attractive, as there is no need for additional image acquisition or administration of pharmacologic stress agents (75,76). The scientific principles of some of the underlying techniques were previously described in detail (5,75,77,78). is made possible by advances in computer performance and image-based modeling. Determination of requires different essential steps. In the case of approaches based in computational fluid dynamics, for instance, these comprise (a) creation of a patientspecific anatomic model of the coronary tree, (b) specification of inflow and outflow boundary conditions of the patientspecific hemodynamics, and (c) application of computational fluid dynamics methods to solve equations incorporating coronary flow, pressure, and velocity during rest and hyperemia. The addition of physiologic parameters and fluid dynamic principles to anatomic models allows for computation of coronary artery blood flow and pressure during maximal hyperemia. Although the anatomic model and hemodynamic boundary conditions are unique for each individual, the applied physical equations of blood flow, velocity, and pressure are universal. The creation of the patient-specific anatomic coronary artery model requires accurate semiautomated segmentation and contouring of the lumen dimensions for the main epicardial arteries, the corresponding side branches, and the left ventricular mass, resulting in a three-dimensional mesh representing a geometric model of the coronary artery tree. The patient-specific boundary conditions are represented as lumped models for the heart and coronary microcirculation. A lumped heart model (time-varying elastance model) is coupled at the inlet of the aorta to provide the inlet boundary condition. The outlet boundary conditions are provided by coronary microvascular models, which account for the influence of the myocardial contraction on the flow waveform (79). The model and methods used for personalizing it to patient conditions are described in greater detail in the study by Itu et al (80). The boundary conditions are estimated from rest state conditions including systolic and diastolic blood pressure, heart rate, and ventricular mass. The conditions are then modified to incorporate the effect of maximal hyperemia by modeling the decrease in microvascular resistance caused by the administration of adenosine (75,81). The computation of values are based on the Navier- Stokes equations, the physical laws that govern fluid dynamics. Blood is treated as an incompressible Newtonian fluid with a constant viscosity within the coronary arteries. The Navier-Stokes equations can be used to solve the flow and pressure across the coronary vasculature by applying computational fluid dynamics methods. These nonlinear partial differential equations are mathematically complex, and derived from three-dimensional models is computationally demanding. Hence, full-order model computations are currently implemented off-site on supercomputers in core laboratories (82,83). A step-bystep model of the determination by using computational fluid dynamics principles is shown in Figure 1. Reduced-order and steady-state models have been introduced to overcome the limitations of three-dimensional models, that is, computational power on supercomputers, off-site calculation, computation time, by averaging the Navier-Stokes equations over vessel-cross sections, with promising results regarding overall accuracy (5,84). However, these approaches, which use generalized coronary microvasculature resistance parameters, have limited value in small segments, near side branches or bifurcations, and in nonuniform luminal compromise (ie, eccentric lesions), which often arise from coronary plaques. A patientspecific reduced-order computational fluid dynamics model has been recently introduced, enabling determination by using individual boundary conditions with a hybrid approach, coupling reduced-order and full-order models for fast flow computation. The computational fluid dynamics approach employs numerical methods to compute time-varying flow and pressures by solving the reduced-order Navier-Stokes equations, with blood being modeled as an incompressible fluid with constant Radiology: Volume 285: Number 1 October 2017 n radiology.rsna.org 21

Figure 1 Figure 1: Determination of by using computational fluid dynamics principles. A, Acquisition of standard coronary CT angiography data. B, Creation of a threedimensional model of the coronary artery anatomy. C, Determination of a physiologic model of the coronary microcirculation derived from patient-specific boundary conditions. D, Application of computational fluid dynamics for the computation of coronary blood flow. E, Color-coded three-dimensional mesh representing values for each point throughout the coronary tree. CCTA = coronary CT angiography, 3D = three-dimensional. (Image courtesy of HeartFlow, Redwood City, Calif.) viscosity. For the healthy nonstenotic coronary arteries, a reduced-order model is used in combination with a lumped parameter model for the coronary microvasculature. To enable accurate pressure computation in the stenotic regions for a given anatomic model, locally defined pressure drop models are embedded into the reducedorder blood flow model, leading to a modified hybrid reduced-order formulation. This is done to account for the complex shape of the stenosis and its impact on the pressure decrease across the respective vessel segment. This approach allows for on-site, physiciandriven flow computation on standard postprocessing workstations in less than 1 hour (75,77). However, while this approach saves computation time, it does not overcome the limitations of small segments, bifurcations, et cetera. More recently, an additional approach has emerged based on deep machine learning methods by using an artificial intelligence algorithm to compute the functional severity of a lesion (6). The deep learning algorithm uses a multilayer neural network architecture that was trained offline to learn the complex relationship between the anatomy of the coronary tree and its corresponding hemodynamics. Model training utilized a large database of synthetically generated coronary anatomies and their corresponding hemodynamic conditions from a computational fluid dynamics simulation. On the basis of geometric features of the patient s anatomy on coronary CT angiograms, such as vessel radius, degree of tapering, and branch length, the algorithm uses the learned relationship to calculate the machine learning based values. In this case, the learned relationship is based on input data, for example, the anatomy of a vascular tree. The quantity of interest, for example, FFR, is represented by a model built from a database of samples with known characteristics and outcome derived from the computational fluid dynamic approach. In initial experiences, this machine learning based algorithm performed with a sensitivity of 81.6%, a specificity of 83.9%, and an accuracy of 83.2%. Compared with a physics-based computational fluid dynamics model, the 22 radiology.rsna.org n Radiology: Volume 285: Number 1 October 2017

Figure 2 Figure 2: Workflow of by using machine learning principles. A, General schematic workflow of a machine learning algorithm for derivation. c FFR CFD = using computational fluid dynamics, c FFR ML = using machine learning. B, Stepwise creation of stenotic coronary arteries with generation of healthy geometry information, stenotic lesions, and radius of the parent branch of the bifurcation. C, Deep learning network architecture to train the model. The network consists of four hidden layers and uses a fully connected network model. D, Automatic detection of stenosis-specific features. (Image courtesy of Siemens Healthineers, Forchheim, Germany.) average computation time was reduced more than 80-fold, allowing near realtime assessment of FFR. Average execution time went down from 196.3 seconds 6 78.5 to approximately 2.4 seconds 6 0.44 for the machine learning model on a workstation. A step-by-step model of the determination using machine learning principles is shown in Figure 2. Case examples using computational fluid dynamics and machine learning for noninvasive FFR derivation are illustrated in Figure 3. Current Evidence Diagnostic Accuracy and Validation Since its introduction in 2011 (85), the clinical validation of has been conducted in several studies with invasive FFR as the reference standard. To date, the diagnostic accuracy of has been evaluated in three prospective multicenter trials and several singlecenter studies. In general, by using the established threshold of 0.80 or less derived from invasive FFR to detect hemodynamic significance, the diagnostic performance of to detect lesionspecific ischemia was compared against coronary CT angiography stenosis severity by using lesions with 50% or greater stenosis by CT. The prospective multicenter Diagnosis of Ischemia-causing Stenoses Obtained Via Noninvasive Fractional flow Reserve (DISCOVER- FLOW) trial was the first investigation to evaluate, including 103 patients with 159 vessels from four sites (85). The DISCOVER-FLOW trial was performed by using the first-generation algorithm (version 1.0) (5). Image quality of the coronary CT angiography data was vetted and studies were excluded in case of nondiagnostic image quality by the core laboratory. While no changes in sensitivity were observed (88% for vs 91% for coronary CT angiography), specificity increased from 40% (coronary CT angiography) to 82% ( ) resulting in a 42% improvement in diagnostic accuracy (84% for vs 59% for coronary CT angiography). Receiver operating characteristics analysis revealed superior discriminatory power of compared with coronary CT angiography alone, both on a per-lesion and per-patient level, resulting in an area Radiology: Volume 285: Number 1 October 2017 n radiology.rsna.org 23

Figure 3 Figure 3: Example of coronary stenosis at coronary CT angiography and ICA with corresponding using computational fluid dynamics ( c FFR CFD ) and machine learning ( c FFR ML ) compared with invasive FFR measurement. Coronary CT angiography in a 65-year-old man presenting with chest pain. A, Automatically generated curved multiplanar reformation along the vessel centerline demonstrates greater than 50% stenosis of the left anterior descending coronary artery caused by a mixed plaque (arrow). B, Color-coded map of the coronary tree using computational fluid dynamics shows a value of 0.75. C, Color-coded map of the coronary tree using machine learning also shows a value of 0.75. D, ICA confirms obstructive stenosis (white arrow) with an identical invasive FFR measurement of 0.75 (black arrow), indicating lesion-specific ischemia. Stent placement was subsequently performed. under the receiver operating characteristics curve (AUC) of 0.90 versus 0.75 and 0.92 versus 0.70 (both P,.05), respectively. Building on DISCOVER- FLOW, the Determination of Fractional Flow Reserve by Anatomic CT Angiography (DeFACTO) trial was the second multicenter investigation that included a larger patient population consisting of 252 patients with 407 vessels from 17 centers (74). This trial was performed by using the second-generation algorithm (version 1.2), and coronary CT angiography image quality was vetted by the core laboratory. The diagnostic accuracy of was higher compared with coronary CT angiography alone (73% vs 64%), with improved specificity (54% vs 42%) and similar sensitivity (90% vs 84%). showed superior discriminatory power over coronary CT angiography alone on a per-lesion (AUC, 0.79 vs 0.53) and per-patient level (AUC, 0.81 vs 0.50) (both P,.05). However, the study failed to achieve its primary objective, which was to reach 70% or greater per-patient diagnostic accuracy, likely attributable to the lower 95% confidence interval (95% confidence interval: 67%, 78%). Based on the results of the first two multicenter trials, the Analysis of Coronary Blood Flow Using CT Angiography: Next Steps (NXT) trial was performed (73). Special attention was paid to high-quality CT acquisitions, with routine administration of nitroglycerine and b-blockers, and imaging protocols were adjusted to patient body size to minimize image noise. Furthermore, technical refinements of the algorithm (version 1.4) with improved lumen boundary identification and further matured physiologic models were utilized. Compared with the preceding trial, these interventions resulted in a substantial improvement of diagnostic accuracy on a per-patient and per-lesion basis for compared with coronary CT angiography alone (81% vs 53% and 86% vs 65%, respectively), resulting in higher specificity with comparable sensitivity. At receiver operating characteristics analysis, demonstrated superior diagnostic performance over coronary CT angiography alone on a per-patient and per-lesion level, with AUCs of 0.90 versus 0.81 (P =.0008) and 0.93 versus 0.89 (P,.0001), respectively. However, several factors may have influenced the diagnostic accuracy of the three multicenter trials. For instance, in the DeFACTO and NXT trials, 11% and 13% of studies were rejected from analysis because of insufficient image quality. Furthermore, in the DeFACTO trial, coronary CT angiography images with misalignment artifacts resulted in decreased overall accuracy compared with coronary CT angiography data not showing misalignment (56% vs 71%). Overall, the evolution of the algorithm is ongoing, in part explaining the different results of the three multicenter trials. A case example of obstructive and nonobstructive lesions at coronary CT angiography, ICA, and measurement by using is illustrated in Figure 4. In addition to the three prospective multicenter trials, several single-center studies have validated by using a workstation-based reduced-order computational fluid dynamics algorithm. A retrospective investigation by Renker et al included 53 patients with 67 vessels and demonstrated a per-vessel sensitivity and specificity of 85% and 85%, respectively (86,87). The AUC was 0.92 for and 0.72 for coronary CT angiography. Furthermore, Coenen et al assessed 189 vessels in 106 patients and showed diagnostic accuracy of 75% for and 56% for coronary CT 24 radiology.rsna.org n Radiology: Volume 285: Number 1 October 2017

Figure 4 Figure 4: Example of coronary stenosis at coronary CT angiography and ICA with corresponding and invasive FFR measurement. Coronary CT angiography in a 57-year old woman presenting with non ST-segment elevation myocardial infarction (troponin 1.2 ng/ml). A, Automatically generated curved multiplanar reformation along the vessel centerline demonstrates greater than 50% stenosis of the left anterior descending coronary artery caused by calcified plaque (arrow). B, Three-dimensional color-coded mesh reveals a value of 0.70, indicating lesion-specific ischemia (arrow). C, ICA shows obstructive stenosis (white arrow) with FFR of 0.71 (black arrow), confirming lesion-specific ischemia. The left anterior descending coronary artery was subsequently revascularized with stent placement. angiography alone (88). Specificity was significantly higher with compared with coronary CT angiography (65% vs 38%), with comparable sensitivity of 88% and 81%. (AUC, 0.83) demonstrated higher discrimination of lesion-specific ischemia when compared with coronary CT angiography (AUC, 0.64). Similar results were shown by Yang et al, who included 72 patients with 138 vessels (89). The authors showed per-vessel sensitivity and specificity of 87% and 77%, respectively, compared with coronary CT angiography (94% vs 66%). Per-vessel analysis resulted in AUCs of 0.89 for and 0.85 for coronary CT angiography. De Geer et al reported similar sensitivity and specificity of 0.83 and 0.76 for in a smaller study that included 23 lesions (90). The Table shows the diagnostic performance of, with invasive FFR as the reference standard. Case examples of obstructive and nonobstructive lesions at coronary CT angiography, ICA, and measurement by using are illustrated in Figures 5 and 6. Furthermore, the diagnostic accuracy of machine learning based on-site determination for the identification of lesion-specific ischemia will be assessed in the ongoing multicenter MACHINE registry (Machine learning Based CT angiography derived FFR: a MulticeNtEr, Registry) (91). In a recent meta-analysis by Gonzalez et al that included data from the three prospective multicenter trials (DISCOVER-FLOW, DeFACTO, NXT) and one retrospective single-center study by Renker et al, the authors demonstrated a pooled sensitivity and specificity of 0.90 (95% confidence interval: 0.85, 0.93) and 0.82 (95% confidence interval: 0.68, 0.76) to detect lesionspecific ischemia (92). Furthermore, in a recent meta-analysis Baumann et al investigated the diagnostic accuracy of in a subgroup of patients with intermediate stenosis. The authors included data from the three prospective multicenter trials (DISCOVER-FLOW, DeFACTO, NXT) and two retrospective single-center studies by Renker et al and Coenen et al and showed a pooled sensitivity and specificity of 0.83 (95% confidence interval: 0.78, 0.89) and 0.74 (95% confidence interval: 0.52, 0.97), respectively (93). Potential differences in the diagnostic accuracy of the single-center studies, despite them having used the same software, may be explained in the retrospective study design and selection bias. Intermediate Stenosis On the basis of the limitations of coronary CT angiography to determine lesion-specific ischemia in intermediate stenosis, has the potential to be an important enhancement in differentiating patients who require further invasive assessment and revascularization or who can be deferred to conservative treatment. In a subpopulation of the DISCOVER-FLOW trial that included 150 lesions in 82 patients with intermediate lesion severity (30% 69% stenosis), the respective accuracy and specificity was 71% and 67% by using and 63% and 72% by using coronary CT angiography alone. demonstrated significantly improved identification of lesion-specific ischemia compared with coronary CT angiography stenosis grading alone (AUC, 0.79 vs 0.53, P,.0001) (74). In the NXT trial, 235 patients showed intermediate stenosis with values ranging from 30% to 70%, which could be correctly categorized as irrelevant versus ischemia-inducing, with an accuracy and specificity of 80% and 85% by using and 51% and 93% by using coronary CT angiography alone. correctly reclassified 68% of patients with false-positive findings based on coronary CT angiography to true-negative findings. In a prospective single-center study, Kruk et al specifically evaluated the impact of workstation-based, reduced-order model in intermediate stenosis in 96 lesions found in 90 patients. They reported a per-vessel sensitivity and specificity of 76% and 72% for and 100% and 2% for coronary CT Radiology: Volume 285: Number 1 October 2017 n radiology.rsna.org 25

Diagnostic Performance of in Comparison with Invasive FFR Parameter DISCOVER- FLOW (85) DeFACTO (74) NXT (73) Renker et al (86) Coenen et al (88) De Geer et al (90) Kruk et al (94) Yang et al (89) Year 2011 2012 2014 2014 2015 2015 2016 2016 Design Prospective multicenter Prospective multicenter Prospective multicenter Retrospective single center Retrospective single center Retrospective single center Prospective single center Prospective single center CT system 64 section or higher 64 section or higher 64 section or higher DSCT (64 or 128 sections) DSCT (64 or 128 sections) DSCT (128 sections) DSCT (128 sections) DSCT (128 sections) software, HeartFlow V1.2, HeartFlow V1.2, HeartFlow V1.4 cffr, Siemens V1.4 cffr, Siemens V1.4 cffr, Siemens V1.4 and V1.7 cffr, Siemens V1.4 cffr, Siemens V1.4 No. of patients 103 252 254 53 106 21 90 72 No. of vessels 159 407 484 67 189 23 96 138 Sensitivity (%)* 88 (77, 95) 89 (73, 86) 84 (75, 89) 85 (62, 97) 88 (78, 94) 83 (NA) 76 (NA) 87 (75, 94) Specificity (%)* 82 (73, 89) 61 (54, 67) 86 (82, 89) 95 (72, 94) 65 (55, 74) 76 (NA) 72 (NA) 77 (66, 85) PPV (%)* 74 (62, 84) 56 (49, 62) 61 (53, 69) 71 (49, 87) 65 (55, 74) 56 (NA) 67 (NA) 71 (58, 81) NPV (%)* 92 (78, 90) 84 (78, 98) 95 (93, 97) 93 (81, 98) 88 (79, 94) 93 (NA) 80 (NA) 90 (80, 96) AUC* 0.90 (NA) 0.79 (0.72, 0.87) 0.93 (0.91, 0.95) 0.92 (NA) 0.83 (NA) NA 0.83 (0.7, 0.90) 0.89 (0.84, 0.95) Note. PPV = positive predictive value, NPV = negative predictive value, DSCT = dual-source CT, NA = not available. * Data presented on a per-vessel level with 95% confidence interval in parentheses. Figure 5 Figure 5: Example of coronary stenosis at coronary CT angiography and ICA with corresponding and invasive FFR measurement. Coronary CT angiography in a 63-year old woman presenting with symptoms suggestive of acute coronary syndrome. A, Automatically generated curved multiplanar reformation along the vessel centerline demonstrates greater than 50% stenosis of the left anterior descending coronary artery caused by noncalcified atherosclerotic plaque (arrow). B, Three-dimensional color-coded mesh reveals a ( c FFR) value of 0.73, indicating lesion-specific ischemia (arrow). C, ICA shows obstructive stenosis of the left anterior descending coronary artery (white arrow) with FFR of 0.74 (black arrow), which was subsequently treated with percutaneous coronary intervention. angiography alone. While coronary CT angiography showed an AUC of 0.66, resulted in a significant improvement, with an AUC of 0.84. However, to achieve at least 90% diagnostic accuracy, the range had to be restricted to values lower than 0.74, which included 49 study lesions (51%). Thus, confidently discriminated between ischemic and nonischemic lesions in only 50% of patients (94). Calcification In a substudy of the NXT trial, Nørgaard et al examined the impact of coronary artery calcification on determination (95). Agatston scores derived from 333 vessels in 214 patients were classified across Agatston quartiles, and performance to detect lesion-specific ischemia in patients with a range of Agatston scores was assessed. The authors reported no significant differences in accuracy, sensitivity, or specificity across Agatston quartiles. The discriminatory power of was equally high in patients with heavy 26 radiology.rsna.org n Radiology: Volume 285: Number 1 October 2017

Figure 6 Figure 6: Example of coronary stenosis at coronary CT angiography and ICA with corresponding and invasive FFR measurement. Coronary CT angiography in a 52-year old man presenting with chest pain. A, Automatically generated curved multiplanar reformation along the vessel centerline demonstrates greater than 50% stenosis of the left anterior descending coronary artery caused by a calcified plaque (arrow). B, Three-dimensional color-coded mesh reveals a ( c FFR) value of 0.76, indicating lesion-specific ischemia (arrow). C, ICA shows obstructive stenosis (white arrow) with FFR of 0.75 (black arrow), indicating lesion-specific ischemia. D, Three-dimensional color-coded cinematic rendering (not yet available for clinical use) of cardiac anatomy with map superimposed on the coronary artery course illustrates functional data in the context of the heart s morphology. Stent placement was subsequently performed. calcium burden (Agatston scores of 416 3599), as well as low-mild scores (score of 0 415), with no significant differences in discrimination (AUC, 0.86 vs 0.92, P =.65). Diagnostic accuracy and specificity were significantly higher for compared with coronary CT angiography in each Agatston score quartile, with improved discrimination of lesion-specific ischemia by over coronary CT angiography alone (AUC, 0.91 vs 0.71, P =.004). Although no significant differences in diagnostic accuracy were observed, the inclusion of the 12% of studies considered nondiagnostic for assessment may have detrimentally affected the overall accuracy. Whereas the impact of intermediate stenosis or calcification on the diagnostic accuracy of has been investigated, the more problematic subset of bifurcation and complex lesions (ie, sequential lesions) have not been sufficiently addressed in previous studies other than being specifically excluded from analysis by Renker et al (86). Clinical Implications Impact on Patient Care The investigators of the PLATFORM trial (Prospective Longitudinal Trial of : Outcome and Resource Impacts) evaluated the impact of in 584 patients who were either scheduled for ICA or traditional noninvasive work-up for suspected CAD. Within these two groups, roughly half of the patients were randomized to -guided management (96). The primary end point was the percentage of patients with planned ICA in whom no significant obstructive CAD was found at ICA within 90 days. The secondary safety end point was a composite of major adverse cardiac events (MACE) including death, myocardial infarction, and unplanned revascularization. Regarding the primary end point, a significant difference between the two guided treatment plans was observed. Among those with intended ICA, no obstructive CAD was found at ICA in 24 (12%) in the CT angiography/ arm and 137 (73%) in the usual-care arm, with similar mean cumulative radiation exposure between the groups. There was a 61% cancellation rate of ICA procedures in the -guided care group. Within a 90-day follow-up period, there was no statistically significant difference in the occurrence of MACE. Most importantly, no MACE occurred in the group where ICA was cancelled based on findings and only four patients underwent ICA after the initial cancellation, three of whom did not have obstructive CAD (7). The impact of to enhance the confidence, appropriateness, and rationality of therapeutic decision making was further investigated in the RIP- CORD trial (Does Routine Pressure Wire Assessment Influence Management Strategy at Coronary Angiography for Diagnosis of Chest Pain?) (16). Based on the results of the NXT trial, Curzen et al evaluated 200 patients to determine whether in addition to coronary CT angiography has the ability to (a) lead to substantial changes in the interpretation of lesion-specific clinical significance and (b) lead to changes in patient management. Therefore, three cardiologists reviewed 200 coronary CT angiography cases and decided in consensus, assuming that each patient was suitable for any of the management options, if optimal medical therapy, percutaneous coronary intervention plus optimal medical therapy, or coronary artery bypass grafting and optimal medical therapy would be the appropriate treatment strategy or if further information was required. Studies were re-reviewed with the addition of information, and treatment selection was reassessed by the three cardiologists in consensus. Based on findings of compared with coronary CT angiography alone, treatment was changed to optimal medical therapy in 23% of patients and was accompanied Radiology: Volume 285: Number 1 October 2017 n radiology.rsna.org 27