Original ResearchOpen Access

Determinants of Rejection Rate for Coronary CT Angiography Fractional Flow Reserve Analysis

Published Online:https://doi.org/10.1148/radiol.2019182673

Abstract

Background

Coronary artery fractional flow reserve (FFR) derived from CT angiography (FFTCT) enables functional assessment of coronary stenosis. Prior clinical trials showed 13%–33% of coronary CT angiography studies had insufficient quality for quantitative analysis with FFRCT.

Purpose

To determine the rejection rate of FFRCT analysis and to determine factors associated with technically unsuccessful calculation of FFRCT.

Materials and Methods

Prospectively acquired coronary CT angiography scans submitted as part of the Assessing Diagnostic Value of Noninvasive FFRCT in Coronary Care (ADVANCE) registry (https://ClinicalTrials.gov: NCT02499679) and coronary CT angiography series submitted for clinical analysis were included. The primary outcome was the FFRCT rejection rate (defined as an inability to perform quantitative analysis with FFRCT). Factors that were associated with FFRCT rejection rate were assessed with multiple linear regression.

Results

In the ADVANCE registry, FFRCT rejection rate due to inadequate image quality was 2.9% (80 of 2778 patients; 95% confidence interval [CI]: 2.1%, 3.2%). In the 10 621 consecutive patients who underwent clinical analysis, the FFRCT rejection rate was 8.4% (n = 892; 95% CI: 6.2%, 7.2%; P < .001 vs the ADVANCE cohort). The main reason for the inability to perform FFRCT analysis was the presence of motion artifacts (63 of 80 [78%] and 729 of 892 [64%] in the ADVANCE and clinical cohorts, respectively). At multivariable analysis, section thickness in the ADVANCE (odds ratio [OR], 1.04; 95% CI: 1.001, 1.09; P = .045) and clinical (OR, 1.03; 95% CI: 1.02, 1.04; P < .001) cohorts and heart rate in the ADVANCE (OR, 1.05; 95% CI: 1.02, 1.08; P < .001) and clinical (OR, 1.06; 95% CI: 1.05, 1.07; P < .001) cohorts were independent predictors of rejection.

Conclusion

The rates for technically unsuccessful CT-derived fractional flow reserve in the ADVANCE registry and in a large clinical cohort were 2.9% and 8.4%, respectively. Thinner CT section thickness and lower patient heart rate may increase rates of completion of CT fractional flow reserve analysis.

Published under a CC BY 4.0 license.

Online supplemental material is available for this article.

See also the editorial by Sakuma in this issue.

Summary

Both the Assessing Diagnostic Value of Noninvasive FFRCT in Coronary Care, or ADVANCE, cohort and the clinical cohort of more than 10 000 patients showed relatively low rates for technically unsuccessful calculation of CT fractional flow reserve (2.9% and 8.4%, respectively).

Key Points

  • ■ Analysis of contemporary databases shows CT fractional flow reserve (FFR) from coronary CT angiography studies had relatively low rates for technically unsuccessful studies (range, 2.9%–8.4%).

  • ■ Contemporary CT angiography made greater use of dual-source technology and wide-coverage single-source scanners than did prior clinical trials.

  • ■ In the Assessing Diagnostic Value of Noninvasive FFRCT in Coronary Care, or ADVANCE, and clinical cohorts, greater CT section thickness (odds ratio, 1.04 and 1.03, respectively) and higher patient heart rate (odds ratio, 1.05 and 1.06, respectively) were associated with an inability to perform CT-derived FFR analysis.

Introduction

Coronary artery disease is a major cause of mortality and morbidity, and its management consumes a large portion of health care budgets (1). Several noninvasive tests are commonly used as gatekeepers to invasive coronary angiography. Despite the frequent use of noninvasive functional stress testing, the prevalence of obstructive coronary artery disease at elective invasive coronary angiography is low (2,3).

Coronary CT angiography is an alternative anatomically based imaging modality used to assess coronary artery disease. However, because of its limited positive predictive value, it can lead to increased downstream use of invasive coronary angiography (46). Recently, participant-specific coronary CT angiography data have been combined with computational fluid dynamic models, allowing for noninvasive determination of the fractional flow reserve derived from coronary CT angiography (FFRCT). This correlates well with invasive FFR and reduces rates of invasive coronary angiography use compared with coronary CT angiography alone (7,8). In these previous trials, FFRCT analysis could be performed in 87%–88% of coronary CT angiography data sets (7,8); however, a more recent substudy of the Prospective Multicenter Imaging Study for Evaluation of Chest Pain, or PROMISE, trial found that because of low-quality images, only 67% of coronary CT angiography data sets could be used for FFRCT analysis (9). Previous studies have examined the factors that affect the accuracy of FFRCT (for instance, both nitrate use and β-blocker use improve diagnostic accuracy) (10,11). However, factors that affect the ability to perform FFRCT analysis of coronary CT angiography data remain poorly understood. Identification of such factors would allow for targeted intervention to increase the likelihood of using this additional analysis in clinical care.

Thus, the aim of our study was to examine the rejection rate of coronary CT angiography for FFRCT analysis and the factors associated with an inability to perform FFRCT in the controlled Assessing Diagnostic Value of Noninvasive FFRCT in Coronary Care (ADVANCE) registry (12) and in an uncontrolled cohort of clinical cases.

Materials and Methods

This study was conducted in accordance with the Declaration of Helsinki. Participants in the ADVANCE registry all provided written informed consent. The clinical cohort analysis was performed with anonymized metadata. Institutional review board approval and patient consent were obtained at all sites and from all participants in the ADVANCE registry. For the commercial cases, all sites provided local institutional review board approval to publish any metadata available from the FFRCT submission. HeartFlow served as a sponsor of the ADVANCE registry (https://ClinicalTrials.gov: NCT02499679). All data in the study were held by the authors, and data analysis, reporting, and submission were performed independent of the sponsor.

Study Cohorts

ADVANCE registry.—The ADVANCE registry is a multicenter prospective controlled registry; patients were recruited for inclusion from July 15, 2015, to October 20, 2017. The ADVANCE registry was designed to evaluate clinical utility, outcomes, and resource use after FFRCT-guided treatment in clinically stable symptomatic patients with a coronary artery disease diagnosis at coronary CT angiography (12,13). The inclusion and exclusion criteria have been previously published and are available in Figures E1–E3 (online) (14). Analyses performed using FFRCT analysis software (version 2.0 or higher) were included in the current analysis; 1450 analyses performed using software versions older than version 2.0 were excluded. Version 2.0 or higher software was used because these are the versions that are currently used in clinical care; thus, they are the most generalizable to current practice.

Clinical submission cohort.—All patients referred for clinical consecutive coronary CT angiography between July 2016 and March 2018 and who had FFRCT analysis performed with version 2.0 or higher FFRCT analysis software were considered for inclusion. Before this point, 5062 cases in the clinical cohort were analyzed with software versions older than 2.0 and were not included in the current study. There was no overlap of participants in the clinical cohort with those in the ADVANCE cohort. All coronary CT angiography examinations were performed according to the local policy of the referring center. When a scan was rejected for analysis, a reason was included in the rejection notification to the referring center. If this could be modified, the case could then be resubmitted. Only the final accepted scans were included. The rationale for this was that if a scan could be amended and subsequently processed, it was simply a postprocessing issue, whereas the rejections that were not amenable to such simple steps were of interest because they represented a lost opportunity.

During the initial commercial set-up, there is an intensive process for the first few weeks, during which structured education and feedback are provided to ensure optimization of the data submitted for analysis; this is referred to as onboarding. Centers that were still onboarding at the end of the study period and all scans performed during the onboarding process (n = 739) were excluded from this study.

FFRCT analysis.—The decision to refer a patient for FFRCT analysis was made by the physician interpreting the coronary CT angiography images. All FFRCT analysis was performed at a single center (HeartFlow). Analysis was performed as previously described (15).

DICOM data extraction.—From the Digital Imaging and Communications in Medicine (DICOM) metadata of the submitted coronary CT angiography scan, information on patient and technical factors was extracted. The patient factors obtained were heart rate (HR) and HR variability. The technical factors obtained included scanner type, number of sources, number of sections, section thickness, pixel spacing (distance between the center of the pixel in the x-y plane, which in turn is determined by the reconstructed field of view), temporal resolution, tube voltage, aortic attenuation, aortic noise, and effective radiation dose measured as the product of dose-length product times for conversion coefficient for the chest (K = 0.026 mSv/mGy·cm) (16). From the DICOM imaging data, aortic contrast material opacification was calculated as the mean attenuation (in Hounsfield units) within the aortic root volume extracted for FFRCT analysis, while image noise was measured as the standard deviation of Hounsfield units within the same volume of the aortic root. The signal-to-noise ratio was calculated as the mean attenuation (in Hounsfield units) in the aorta divided by the standard deviation of attenuation in the aorta. The pixel spacing contained within the DICOM metadata was used to back-calculate the reconstructed field of view, as this is the more intuitive and clinically appreciable input metric. This was done by multiplying the pixel spacing by 512 to coincide with the size of the CT output matrix.

End point.—Rate of coronary CT angiography rejection for FFRCT analysis was defined as the inability of technologists at HeartFlow, who were blinded to the clinical data, to perform FFRCT analysis. The reason for rejection was recorded and classified as follows: (a) Technical rejection included all automatic rejections due to the data failing to meet minimum technical specifications for FFRCT analysis due to data format. Reasons for technical rejection are as follows: section thickness or spacing of 1 mm or greater, pixel size of 0.5 mm or greater (equivalent to a reconstructed field of view ≥25.6 cm), or missing data. (b) Rejection due to inappropriate submission included all cases in which FFRCT was not validated for clinical use and therefore was not suitable for analysis. This included a previous history of revascularization (stent or coronary artery bypass graft) or patients who had missing coronary or cardiac segments due to inadequate scan range, inappropriate field of view reconstruction (clipped structure), or inappropriate section thickness. (c) Image quality rejections included cases in which image quality was inadequate to allow for generation of the anatomic coronary model necessary for FFRCT calculation. This decision was made based on two parallel assessments. The first was an image quality scoring system of the coronary arteries in which the length of the artifact, diameter of the vessel in which the artifact is present, whether a stenosis is present within the region of the artifact, and the type of artifact were combined and weighted according to the location of the artifact within the vessel, with more proximal artifacts given greater weighting. Scans with an inadequate image quality score were rejected. The second related decision process was based on the presence of any regions of uninterpretability (ROUs). Cases were not processed if one vessel system contained two ROUs longer than 5 mm, all three vessel systems contained one ROU longer than 5 mm, or one vessel system contained one ROU longer than 30 mm. If a scan failed because of the image quality or the ROU criteria, it was rejected. (d) For these patients, the reason for poor image quality was recorded as blooming artifact, motion artifact, or image noise. Examples of coronary CT angiography studies rejected for inadequate image quality are shown in Figures E1–E3 (online).

These criteria have remained stable since software was updated from version 1.6 through version 2.0 or higher. DICOM data from technical rejections are not stored and are not available for analysis.

Statistical Analysis

Statistical analysis was performed with statistical software (SPSS, version 23, SPSS, Chicago, Ill; R, version 2.15.2, R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were expressed as mean ± standard deviation or median and 25th to 75th percentile, as appropriate. Discrete variables were expressed as absolute numbers and percentages. Student independent t tests or Mann-Whitney tests were used, as appropriate, to compare continuous variables among patients in whom FFRCT analysis was performed versus those who were rejected for FFRCT analysis. Comparisons among groups of discrete variables were performed by using the χ2 or Fisher exact test if the expected cell count was less than five. Binary logistic regression was used to assess the association between baseline covariates and FFRCT rejection (results are presented as odds ratio [OR] and 95% confidence interval [CI]). Variables with P < .1 at univariable analysis were then included as covariates in multivariable analysis. All tests were two tailed, and P < .05 was considered to indicate a significant difference.

Results

Cohort Characteristics

This study included 2778 participants from the ADVANCE registry (1845 [66%] were men; mean age of the total cohort, 66.2 years ± 10.3; mean age of men, 65.1 years ± 10.2; mean age of women, 68.5 years ± 10.0; P < .001 for between-sex age difference) and 10 621 participants from the clinical cohort. A flow diagram of the study participants can be found in the Figure. The characteristics of the cohorts are listed in Tables 1 and 2.

Flow diagram of the study participants in both cohorts. * The number of cases is greater than the total number of rejections, as cases could be rejected for more than one reason. Technical rejection means all automatic rejections were due to the data failing to meet minimum technical specifications for fraction flow reserve derived from coronary CT angiography (FFRCT) analysis due to data format. Image quality rejection means image quality was inadequate to allow for generation of the anatomic coronary model necessary for FFRCT calculation. ADVANCE = Assessing Diagnostic Value of Noninvasive FFRCT in Coronary Care, CABG = coronary artery bypass graft, CTA = CT angiography, DICOM = Digital Imaging and Communications in Medicine.

Table 1: Baseline Characteristics of the ADVANCE Registry

Table 1:

Note.—Continuous variables with normal distribution are expressed as mean ± standard deviation. Discrete variables are expressed as absolute number, with the percentage in parentheses. ADVANCE = Assessing Diagnostic Value of Noninvasive FFRCT in Coronary Care, FFRCT = fractional flow reserve calculated from CT.

Table 2: Baseline Characteristics of the Clinical Cohort

Table 2:

Note.—Continuous variables with normal distribution are expressed as mean ± standard deviation. Discrete variables are expressed as absolute number, with the percentage in parentheses. FFRCT = fractional flow reserve calculated from CT.

ADVANCE Registry

Scans of the 2778 participants were submitted from 38 sites in Europe, North America, and Japan. Coronary CT angiography was performed using 16 scanner models produced by four different manufacturers: GE Healthcare (n = 523 [19%]), Siemens (n = 1641 [59%]), Philips (n = 192 [7%]), and Toshiba (n = 422 [15%]). The majority of scans were performed with single-source units (n = 1507 [54%]), a substantial portion of which were wide-coverage CT scanners (n = 1072 [39%]) (Table 1). A low tube potential (≤100 kVp) was used in 1381 of 2778 (50%) participants (Table 1). The mean HR and HR variability were 60 beats per minute ± 9 and 15 beats per minute ± 30, respectively, with a mean dose of 10.0 mSv ± 9.1 (Table 1).

The rejection rate was 80 of 2778 participants (2.9%; 95% CI: 2.32%, 3.57%). Of the 2778 participants, 73 (2.62%; 95% CI: 2.06%, 3.25%) were rejected for inadequate image quality, and the other seven (0.25%; 95% CI: 0.15%, 0.57%) were rejected due to inappropriate case submission (Table 3). Rejection of coronary CT angiography scans for FFRCT analysis was greater in patients with a history of smoking, diabetes, or angina (Table 1). Technical factors associated with coronary CT angiography rejection for FFRCT analysis were lower use of dual-source CT scanning, lower aorta contrast, wider reconstructed field of view, and greater section thickness (Table 1). Finally, HR was higher in the rejected cases than in the accepted cases (63 beats per minute ± 14 vs 60 beats per minute ± 9, P = .03), with no difference in HR variability between groups.

Table 3: Reason for FFRCT Rejection in the ADVANCE Registry and Clinical Cohort

Table 3:

Note.—Discrete variables are expressed as absolute number, with the percentage in parentheses. ADVANCE = Assessing Diagnostic Value of Noninvasive FFRCT in Coronary Care, FFRCT = fractional flow reserve derived from CT.

*Values are greater than the total number of rejections because scans can be rejected for more than one reason.

Inadequate image quality is based on (a) length of the artifact, diameter of the vessel in which the artifact is present, whether a stenosis is present within the region of artifact, and the type of artifact is weighted according to the location of the artifact and (b) the presence of any regions of uninterpretability (ROUs). Cases are not processed if one vessel system contains two ROUs longer than 5 mm, if all three vessel systems contain one ROU longer than 5 mm, or if one vessel system contains one ROU longer than 30 mm.

Tables 4 and 5 show uni- and multivariable analysis of factors associated with rejection for analysis in the ADVANCE registry. In the fully adjusted model, smoking history (smokers vs nonsmokers: OR, 2.7; 95% CI: 1.2, 5.6; P = .01), temporal resolution (<100 msec vs ≥100 msec: OR, 2.7; 95% CI: 1.3, 5.6; P = .006), section thickness (per 0.01-mm increase in section thickness: OR, 1.04; 95% CI: 1.0, 1.09; P = .045), and HR (per beat per minute increase in HR: OR, 1.05; 95% CI: 1.02, 1.08; P < .001) were all independent predictors of rejection (Table 5).

Table 4: Univariable Predictors of FFRCT Rejection in the ADVANCE Registry and Clinical Cohort

Table 4:

Note.—Some data are missing because they were not available in both datasets. Data in parentheses are the 95% confidence interval. ADVANCE = Assessing Diagnostic Value of Noninvasive FFRCT in Coronary Care, FFRCT = fractional flow reserve calculated from CT.

Table 5: Multivariable Predictors of FFRCT Rejection of the ADVANCE Registry and Clinical Cohort

Table 5:

Note.—Data in parentheses are 95% confidence intervals. ADVANCE = Assessing Diagnostic Value of Noninvasive FFRCT in Coronary Care, FFRCT = fractional flow reserve calculated from CT.

*Heart rate variability and dose were excluded from multivariable analysis in the clinical cohort, as one or the other were not routinely collected with the Digital Imaging and Communications software of GE Healthcare, Philips, and Toshiba scanners, and this would have introduced a systematic bias in the results. A total of 7867 cases were included in the model due to missing variables in some cases.

Clinical Cohort

A total of 10 621 cases were submitted for FFRCT analysis between July 2016 and November 2017. A total of 205 submissions were automatically rejected due to the DICOM files not meeting the minimum technical data specifications. Therefore, the final population comprised 10 416 patients. The cases were submitted by 76 centers (median, 78 scans per center; range, one to 884 scans) covering 10 countries in North America, Europe, and Asia. These were performed by using 22 CT scanner models produced by four different manufacturers—GE Healthcare (3278 scans [32%]), Siemens (4125 scans [40%]), Philips (1456 scans [14%]), and Toshiba (1542 scans [15%])—with the majority being performed with single-source units (7025 scans [67%]). Low-tube potential (≤100 kVp) was used in 587 participants (5.6%) (Table 2). The mean HR and HR variability were 60 beats per minute ± 9 and 17 beats per minute ± 30, respectively, with a mean dose of 9.9 mSv ± 7.8 (Table 2).

The rejection rate was 892 of 10 416 cases submitted (8.60%; 95% CI: 7.89%, 8.93%). Of the 10 416 cases, 711 (6.8%; 95% CI: 6.23%, 7.18%) were rejected for inadequate image quality; the remainder were rejected for technical limitations of the submitted data or because of the presence of stents, bypass grafts, or other cardiac hardware (Table 3). The coronary CT angiography rejection rate was higher in the clinical cohort than in the ADVANCE registry (P < .001).

Participants in whom the coronary CT angiography scan was rejected for FFRCT analysis showed a lower use of dual-source and wide-coverage CT scanners, lower aorta contrast, wider reconstructed fields of view, and greater section thickness as compared with participants in whom coronary CT angiography scans were accepted (P < .001 for all) (Table 2). Participants in whom the coronary CT angiography scan was rejected had a higher HR (mean, 65 beats per minute ± 12 vs 60 beats per minute ± 9; P < .001) and a higher HR variability (mean, 24 beats per minute ± 42 vs 17 beats per minute ± 29; P = .001) compared with those in whom the coronary CT angiography scan was accepted (Table 2). When considering only wide-detector and dual-source CT scanners, dual-source CT scanners were associated with lower rates of coronary CT angiography rejection for FFRCT (wide detector, 364 of 4774 [7.6%] vs dual source, 206 of 3343 [6.2%]; P = .01). This difference remained when only wide-detector scanners with temporal resolution of less than 140 msec were considered (wide detector, 245 of 2675 [9.2%] vs dual source, 206 of 3343 [6.2%]; P < .001).

Tables 4 and 5 show uni- and multivariable analysis of the factors associated with coronary CT angiography rejection in the clinical cohort. At multivariable analysis, section thickness (per 0.01-mm increase: OR, 1.03; 95% CI: 1.02, 1.04; P < .001), HR (per beat per minute increase: OR, 1.06; 95% CI: 1.05, 1.07; P < .001), wide volume scanner coverage (≥16 cm vs <16 cm: OR, 0.47; 95% CI: 0.39, 0.58; P < .001), scanner temporal resolution (≥100 msec vs <100 msec: OR, 1.7; 95% CI: 1.4, 2.1; P < .001), higher aortic contrast opacification (per increase in Hounsfield units: OR, 0.997; 95% CI: 0.996, 0.998; P < .001), and a wider reconstructed field of view size (per 1-cm increase: OR, 1.01; 95% CI: 1.007, 1.0013; P < .001) were independently associated with coronary CT angiography scan rejection.

Discussion

The main findings of our study are that the rate of rejection of coronary CT angiography scans for fractional flow reserve derived from coronary CT angiography (FFRCT) analysis due to inadequate image quality is low, and it is lower in the Assessing Diagnostic Value of Noninvasive FFRCT in Coronary Care (ADVANCE) cohort than in the clinical cohort. We also found temporal resolution, section thickness, and heart rate (HR) are independent predictors of coronary CT angiography scan rejection for FFRCT analysis.

The observed rate of coronary CT angiography rejection for FFRCT analysis was significantly lower than that reported in the published literature, where it ranges from 13% to 33% (7,9). The reason for the low rate of rejection in our study may be related to the greater use of dual-source technology and wide-coverage single-source scanners. Previous studies with dual-source technology show that image quality is less dependent on HR as compared to 64-section single-source coronary CT (17,18) and that wide-coverage scanner technology allows for single-beat whole-heart acquisition, reducing the occurrence of motion or step artifacts (19,20). Despite these advances in CT hardware over the past 2 decades, HR continues to be closely associated with image quality at coronary CT angiography (21). Coronary CT angiography performed using these scanners accounted for only 17% of scans in the PROMISE FFRCT substudy, which had a 33% rejection rate (9). In our study, 85% of cases in the ADVANCE registry and 79% of the clinical cases submitted for analysis were performed with these types of scanners. An additional explanation for the difference observed between our study and the PROMISE FFRCT substudy is that only 67% of CT angiography data sets met Society of Cardiovascular Computed Tomography, or SCCT, guidelines, yielding a higher rejected rate.

The main reason for FFRCT rejection in our study was motion artifacts. In the Diagnostic Accuracy of Fractional Flow Reserve from Anatomic CT Angiography, or DEFACTO, trial, misalignment artifacts resulted in impaired sensitivity and overall accuracy of FFRCT, while use of β-blockers yielded higher FFRCT specificity (10). These findings support close adherence to the current guidelines for coronary CT angiography (22,23). Unfortunately, previous studies showed that adherence to these guidelines is variable and that the ideal HR for coronary CT angiography is achieved in only 55% of patients (17,24).

For both cohorts in our study, section thickness and pixel size showed a clear association with rejection of FFRCT analysis. Given that greater section thickness and pixel size have reduced image noise (25), it may be that the cardiac imagers are selecting settings to improve image quality for visual analysis. However, peak aortic enhancement is a more important contributor than image noise for the ability to perform FFRCT analysis, as evidenced by our observation that lower contrast opacification but not greater image noise was associated with rejection. Thus, it may be beneficial to reconstruct studies undergoing FFRCT analysis with a small field of view and the smallest possible section thickness.

Our result showing that the ADVANCE cohort had a lower rate of coronary CT angiography rejection than the clinical cohort warrants consideration. Aortic contrast opacification was a significant predictor of rejection in the clinical cohort but not in the ADVANCE registry. Tube potential is an easily amendable scanning factor that boosts intravascular contrast opacification as it moves the kilovoltage peak closer to the k-edge of iodine; however, low-kilovoltage-peak scanning was implemented in only 6% of the clinical cases, whereas it was implemented in 50% of the ADVANCE cases, suggesting that optimizing scanner parameters eliminates suboptimal aortic contrast opacification. The reconstructed field of view was associated with rejection in the clinical cohort but not in ADVANCE cohort. The reconstructed field of view was 10% smaller in the ADVANCE cohort (18.5 cm) than in the clinical cohort (20.7 cm), suggesting a reticence of the clinical referral centers to optimally crop the images for cardiac analysis, presumably because they feared cropping out the coronary arteries or cardiac chambers.

Our study had some limitations. A referral bias is likely present, as referring cardiac imagers were unlikely to submit visually nondiagnostic coronary CT angiography scans for consideration for FFRCT. Thus, our study most likely represents the percentage of visually diagnostic coronary CT angiography scans amenable to subsequent FFRCT analysis rather than the percentage of all coronary CT angiography scans that are amenable to FFRCT analysis. Moreover, there will be differences between the ADVANCE cohort and the clinical cohort, and the results of the two populations cannot be considered interchangeable, nor can the conclusions for one cohort be applied to the other.

In conclusion, in both a clinical registry and in cases undergoing clinical analysis, the rate of rejection of coronary CT angiography for fractional flow reserve derived from coronary CT angiography (FFRCT) due to inadequate image quality is significantly lower than that reported in previous studies. Optimization of scans through heart rate control, timing of contrast material administration, minimization of section thickness and reconstructed field of view, and greater use of dual-source technology or a wide-coverage scanner holds the potential to enable FFRCT analysis in most patients.

Disclosures of Conflicts of Interest: G.P. Activities related to the present article: institution received a grant from HeartFlow. Activities not related to the present article: institution received grants from GE Healthcare, Bracco, and HeartFlow; institution received payment for lectures from GE Healthcare, Bracco, Bayer, and Medtronic. Other relationships: disclosed no relevant relationships. J.R.W. disclosed no relevant relationships. A.B. disclosed no relevant relationships. A.D.T. disclosed no relevant relationships. L.F. disclosed no relevant relationships. M.G. disclosed no relevant relationships. G.M. disclosed no relevant relationships. A.I.G. disclosed no relevant relationships. D.A. disclosed no relevant relationships. M.P. Activities related to the present article: institution received a grant from HeartFlow. Activities not related to the present article: institution served as a consultant for Bayer, Janssen, and AstraZeneca. Other relationships: disclosed no relevant relationships. K.N. Activities related to the present article: institution received a grant from HeartFlow. Activities not related to the present article: institution received grants from Siemens Healthineers, Bayer, and GE Healthcare; was reimbursed for travel by HeartFlow. Other relationships: disclosed no relevant relationships. T.A. Activities related to the present article: institution received grants from HeartFlow Japan; received a consulting fee and support for travel from HeartFlow Japan. Activities not related to the present article: is a consultant for Canon USA, Abbott Vascular Japan, Boston Scientific Japan, St Jude Medical Japan, and Terumo; received grants from Actelion Pharmaceuticals Japan, Acist Japan, Astellas Pharma, Bayer Japan, Boston Scientific Japan, Nippon Boehringer Ingelheim, Daiichi Sankyo, EP-CRSU, EPS, Goodman, Hearflow Japan, Infraredx, Kowa Pharmaceutical, MSD KK, Mebix, Mochida Pharmaceutical, Medtronic Japan, Novartis, Japan Lifeline, Nippon Shinyaku, Ono Pharmaceutical, Otsuka Pharmaceutical, Pfizer Japan, St. Jude Medical Japan, TOA EIYO, Takeda Pharmaceutical, Volcano Japan; served on speakers bureaus for Actelion Pharmaceuticals Japan, Acist Japan, Astellas Pharma, Amgen Astellas BioPharma, AstraZeneca, Abbott Vascular Japan, Nippon Boehringer Ingelheim, Bayer Japan, Bristol-Myers Squibb, Boston Scientific Japan, Canon Medical Systems, Chugai Pharmaceutical, Otsuka Pharmaceutical, Sumitomo Dainippon Pharma, Daiichi Sankyo, Eisai, Fujifilm, Goodman, HeartFlow Japan, Kyowa Hakko Kirin, Kowa Pharmaceutical, MSD KK, Nihon Medi-Physics, Mebix, Mochida Pharmaceutical, Medtronic Japan, Nipro, Novartis, Nippon Shinyaku, Philips Japan, St Jude Medical Japan, Sanofi, Shionogi & Company, Terumo, Toshiba Medical Systems, Takeda Pharmaceutical, and Volcano Japan; received travel assistance from TOA EIYO. Other relationships: disclosed no relevant relationships. C.R. Activities related to the present article: is an employee shareholder in HeartFlow. Activities not related to the present article: is a HeartFlow employee, holds stock in HeartFlow. Other relationships: disclosed no relevant relationships. B.L.N. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: institution received grants from Siemens and HeartFlow; received travel aid from HeartFlow. Other relationships: disclosed no relevant relationships. J.B. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: institution received grants from Edwards Lifesciences, GE Healthcare, Biotronik, Boston Scientific, and Medtronic; gave lectures for Abbott Vascular. Other relationships: disclosed no relevant relationships. G.L.R. Activities related to the present article: institution received a grant from HeartFlow. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. K.C. disclosed no relevant relationships. D.B. Activities related to the present article: institution received a grant from HeartFlow. Activities not related to the present article: receives royalties for software from Cedars-Sinai Medical Center. Other relationships: disclosed no relevant relationships. T.F. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: is on the HeartFlow speakers bureau. Other relationships: disclosed no relevant relationships. L.H.K. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: institution received grants from HeartFlow, Siemens Healthineers, and Verily; gave lectures for HeartFlow and Siemens Healthineers; developed educational presentations for Siemens and HeartFlow; received travel aid from GE Healthcare; other aid was received from Mallinckrodt. Other relationships: disclosed no relevant relationships. J.L. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: is a consultant for HeartFlow and Circle Cardiovascular Imaging; institution received grants from Edwards Lifesciences, Abbott, and Medtronic; gave lectures for GE Healthcare; has stock in HeartFlow and Circle Cardiovascular Imaging; received travel aid from HeartFlow. Other relationships: disclosed no relevant relationships.

Author Contributions

Author contributions: Guarantors of integrity of entire study, G.P., J.R.W.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, G.P., J.R.W., A.B., A.D.T., M.G., G.M., A.I.G., M.P., C.R., B.L.N., J.B., J.L.; clinical studies, G.P., J.R.W., A.B., G.M., A.I.G., D.A., M.P., K.N., T.A., C.R., B.L.N., G.L.R., K.C., T.F., L.H.K., J.L.; statistical analysis, G.P., J.R.W., L.F.; and manuscript editing, G.P., J.R.W., A.B., M.G., A.I.G., D.A., M.P., K.N., T.A., C.R., B.L.N., J.B., G.L.R., K.C., D.B., T.F., L.H.K., J.L.

* G.P. and J.R.W. contributed equally to this work.

Supported by HeartFlow.

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Article History

Received: Dec 3 2018
Revision requested: Jan 8 2019
Revision received: Apr 8 2019
Accepted: June 11 2019
Published online: July 23 2019
Published in print: Sept 2019