Feasibility and diagnostic performance of fractional flow reserve measurement derived from coronary computed tomography angiography in real clinical practice

Machine learning and computational fluid dynamics derived FFRCT demonstrate comparable diagnostic performance in patients with coronary artery disease; A Systematic Review and Meta-Analysis

BACKGROUND

As a new noninvasive diagnostic technique, computed tomography-derived fraction flow reserve (FFRCT) has been used to identify hemodynamically significant coronary artery stenosis. FFRCT can be calculated using computational fluid dynamics (CFD) or machine learning (ML) approaches. It was hypothesized that ML-based FFRCT (FFRCTML) has comparable diagnostic performance with CFD-based FFRCT (FFRCTCFD). We used invasive FFR as the reference test to evaluate the diagnostic performance of FFRCTML vs. FFRCTCFD.

METHODS

We searched PubMed, Cochrane Library, EMBASE, WOS, and Scopus for articles published until March 2024. We analyzed the synthesized sensitivity, specificity, and diagnostic odds ratio (DOR) of FFRCTML vs FFRCTCFD at both the patient and vessel levels. We generated summary receiver operating characteristic curves (SROC) and then calculated the area under the curve (AUC).

RESULTS

This meta-analysis included 23 studies reporting FFRCTCFD diagnostic performance and 18 studies reporting FFRCTML diagnostic performance. In the FFRCTCFD group, 2501 patients and 3764 vessels or lesions were analyzed. In the FFRCTML group, 1323 patients and 4194 vessels or lesions were analyzed. Our results showed that at the per-patient level, FFRCTCFD and FFRCTML had comparable pooled specificity (Z ​= ​-0.59, P ​= ​0.55) and AUC (P ​= ​0.5). At the per-vessel level, FFRCTCFD and FFRCTML also showed comparable specificity (Z ​= ​0.94, P ​= ​0.34), DOR (Z ​= ​0.7, P ​= ​0.48), and AUC (P ​= ​0.74). However, the sensitivity of FFRCTML was significantly lower compared to FFRCTCFD at both patient (Z ​= ​-3.85, P ​= ​0.0001) and vessel (Z ​= ​-2.05, P ​= ​0.04) levels.

CONCLUSION

The FFRCTML technique was comparable to standard CFD approaches in terms of AUC and specificity. However, it did not achieve the same level of sensitivity as FFRCTCFD.

Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment

Cardiovascular CT is being widely used in the diagnosis of cardiovascular disease due to the rapid technological advancements in CT scanning techniques. These advancements include the development of multi-slice CT, from early generation to the latest models, which has the capability of acquiring images with high spatial and temporal resolution. The recent emergence of photon-counting CT has further enhanced CT performance in clinical applications, providing improved spatial and contrast resolution. CT-derived fractional flow reserve is superior to standard CT-based anatomical assessment for the detection of lesion-specific myocardial ischemia. CT-derived 3D-printed patient-specific models are also superior to standard CT, offering advantages in terms of educational value, surgical planning, and the simulation of cardiovascular disease treatment, as well as enhancing doctor-patient communication. Three-dimensional visualization tools including virtual reality, augmented reality, and mixed reality are further advancing the clinical value of cardiovascular CT in cardiovascular disease. With the widespread use of artificial intelligence, machine learning, and deep learning in cardiovascular disease, the diagnostic performance of cardiovascular CT has significantly improved, with promising results being presented in terms of both disease diagnosis and prediction. This review article provides an overview of the applications of cardiovascular CT, covering its performance from the perspective of its diagnostic value based on traditional lumen assessment to the identification of vulnerable lesions for the prediction of disease outcomes with the use of these advanced technologies. The limitations and future prospects of these technologies are also discussed.

Influence of Left Ventricular Diastolic Dysfunction on the Diagnostic Performance of Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve

OBJECTIVES

Our study aimed to demonstrate the influence of left ventricular (LV) diastolic dysfunction on the diagnostic performance of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR).

METHODS

One hundred vessels from 90 patients were retrospectively analyzed. All patients underwent echocardiography, coronary computed tomography angiography (CCTA), CT-FFR, invasive coronary angiography (ICA), and fractional flow reserve (FFR). The study population was divided into normal and dysfunction groups according to the LV diastolic function, and the diagnostic performance in both groups was assessed.

RESULTS

There was a good correlation between CT-FFR and FFR (R = 0.768 p < 0.001) on a per-vessel basis. The sensitivity, specificity, and accuracy were 82.3%, 81.8%, and 82%, respectively. The sensitivity, specificity, and accuracy were 84.6%, 88.5%, and 87.2% in the normal group and 81%, 77.5%, and 78.7% in the dysfunction group, respectively. CT-FFR showed no statistically significant difference in the AUC in the normal group vs. the dysfunction group (AUC: 0.920 [95% CI 0.787-0.983] vs. 0.871 [95% CI 0.761-0.943], Z = 0.772 p = 0.440). However, there was still a good correlation between CT-FFR and FFR in the normal group (R = 0.767, p < 0.001) and dysfunction group (R = 0.767 p < 0.001).

CONCLUSIONS

LV diastolic dysfunction had no effect on the diagnostic accuracy of CT-FFR. CT-FFR has good diagnostic performance in both LV diastolic dysfunction and the normal group and can be used as an effective tool for finding lesion-specific ischemia while screening for arterial disease in patients.

PERSONALIZED FLOW DIVISION METHOD BASED ON THE LEFT-RIGHT CORONARY CROSS-SECTIONAL AREA

This paper proposes a personalized method to estimate blood flow distribution based on the cross-sectional area of the left-right coronary artery openings. According to the cross-sectional area of the left-right coronary artery in 30 cases, a personalized flow distribution model was derived. A 0D/3D geometric multiscale model was used for the numerical simulation of FFR. To evaluate the accuracy of the cross-sectional area method, invasive FFR was used as the standard. The diagnostic efficiency of the proposed method was verified through the simulation results of the volume and the fixed ratio methods. The flow of the left-right coronary artery was proportional to the 3/4 power of the cross-sectional area. The 95% LOA between the cross-sectional area method, volume method, fixed ratio method and FFR were [Formula: see text]0.06 ([[Formula: see text]0.22, 0.10]), [Formula: see text]0.03 ([[Formula: see text]0.35, 0.28]), and [Formula: see text]0.05 ([[Formula: see text]0.30, 0.20]), the accuracy values were 94.44%, 77.78%, and 77.78%, respectively. Flow distribution based on the cross-sectional area represents the supply and demand relationship of the myocardium. The flow of the left-right coronary arteries is proportional to the 3/4 exponent of the cross-sectional area, which affects the accuracy of FFRCT by affecting the exit boundary conditions of the 0D/3D model.

Effect of Coronary Calcification Severity on Measurements and Diagnostic Performance of CT-FFR With Computational Fluid Dynamics: Results From CT-FFR CHINA Trial

Aims: To explore the effect of coronary calcification severity on the measurements and diagnostic performance of computed tomography-derived fractional flow reserve (FFR; CT-FFR).

Methods: This study included 305 patients (348 target vessels) with evaluable coronary calcification (CAC) scores from CT-FFR CHINA clinical trial. The enrolled patients all received coronary CT angiography (CCTA), CT-FFR, and invasive FFR examinations within 7 days. On both per-patient and per-vessel levels, the measured values, accuracy, and diagnostic performance of CT-FFR in identifying hemodynamically significant lesions were analyzed in all CAC score groups (CAC = 0, > 0 to <100, ≥ 100 to <400, and ≥ 400), with FFR as reference standard.

Results: In total, the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under receiver operating characteristics curve (AUC) of CT-FFR were 85.8, 88.7, 86.9, 87.8, 87.1%, 0.90 on a per-patient level and 88.3, 89.3, 89.5, 88.2, 88.9%, 0.88 on a per-vessel level, respectively. Absolute difference of CT-FFR and FFR values tended to elevate with increased CAC scores (CAC = 0: 0.09 ± 0.10; CAC > 0 to <100: 0.06 ± 0.06; CAC ≥ 100 to <400: 0.09 ± 0.10; CAC ≥ 400: 0.11 ± 0.13; p = 0.246). However, no statistically significant difference was found in patient-based and vessel-based diagnostic performance of CT-FFR among all CAC score groups.

Conclusion: This prospective multicenter trial supported CT-FFR as a viable tool in assessing coronary calcified lesions. Although large deviation of CT-FFR has a tendency to correlate with severe calcification, coronary calcification has no significant influence on CT-FFR diagnostic performance using the widely-recognized cut-off value of 0.8.

Diagnostic performance of computed tomography-based fraction flow reserve in identifying myocardial ischemia caused by coronary artery stenosis: A meta-analysis

BACKGROUND

As a new noninvasive diagnostic technique, computed tomography (CT)-based fraction flow reserve (FFR) has been used to identify hemodynamically significant coronary artery stenosis. This meta-analysis used invasive FFR as the standard to evaluate the diagnostic performance of FFRCT.

METHODS

We searched the PubMed, Cochrane library, and EMBASE for articles published between January 2009 and January 2021. The synthesized sensitivity and specificity of invasive FFR and FFRCT were analyzed at both the patient and vessel levels. We generated a summary receiver operating characteristic curve (SROC) and then calculated the area under the curve (AUC).

RESULTS

We included a total of 23 studies, including 2,178 patients and 3,029 vessels or lesions. Analysis at each patient level demonstrated a synthesized sensitivity of 88%, specificity of 79%, LR+ of 4.16, LR-of 0.15, and AUC of 0.89 for FFRCT. Analysis at the level of each vessel or lesion showed a synthesized sensitivity of 85%, specificity of 81%, LR+ of 4.44, LR-of 0.19, and AUC of 0.87 for FFRCT.

CONCLUSION

Our research reveals that FFRCT has high diagnostic performance in patients with coronary artery stenosis, regardless of whether it is at the patient level or the vessel level.

Clinical application of computed tomography angiography and fractional flow reserve computed tomography in patients with coronary artery disease: A meta-analysis based on pre- and post-test probability

PURPOSE

To assess the diagnostic role of coronary computed tomography angiography (CCTA) and fractional flow reserve computed tomography (FFRCT) in confirming or excluding ischemic coronary artery disease (CAD) and to provide a rational use of CCTA and FFRCT in different pre-test probability (PTP) of CAD.

METHODS

We searched the electronic databases from the earliest relevant literature to July 2020 comparing FFRCT or CCTA with FFR. The bivariate random-effects models and Bayes' theorem were used to investigate the diagnostic performance of CCTA and FFRCT with the sensitivity, specificity, pre- and post-test probability.

RESULTS

Fifty-three articles with 4817 patients and 7026 vessels finally met our inclusion criteria. At the patient level, the sensitivity and specificity of CCTA were (0.94, 0.89-0.97), and (0.50, 0.43-0.58), respectively. For FFRCT, the sensitivity and specificity were (0.90, 0.87-0.93) and (0.81, 0.73-0.87). CCTA or FFRCT could increase the post-test probability to >85 % in patients with a PTP > 74.9 % or 54.5 %; CCTA or FFRCT could decrease the post-test probability to <15 % in patients with a pre-test probability <61.3 % or 59.3 %.

CONCLUSIONS

In patients with low to intermediate PTP, CCTA is suggested to exclude CAD, while the time-consuming calculation of FFRCT may be unnecessary. If CCTA detects significant or uncertain stenosis with intermediate to high PTP of CAD, further FFRCT is suggested. The advantages of FFRCT for guiding CAD treatment have sufficiently been demonstrated.

Cardiac CT perfusion and FFRCTA: pathophysiological features in ischemic heart disease

Cardiac computed tomography (CCT) has rapidly evolved, becoming a powerful integrated tool for the evaluation of coronary artery disease (CAD), and being superior to other noninvasive methods due to its high accuracy and ability to simultaneously assess both lumen stenosis and atherosclerotic plaque burden. Furthermore, CCT is regarded as an effective gatekeeper for coronary angiography, and carries independent important prognostic information. In the last decade, the introduction of new functional CCT applications, namely CCT perfusion (CCTP) imaging and CT-derived fractional flow reserve (FFRCTA), has opened the door for accurate assessment of the haemodynamic significance of stenoses. These new CCT technologies, thus, share the unique advantage of assessing both myocardial ischemia and patient-specific coronary artery anatomy, providing an integrated anatomical/functional analysis. In the present review, starting from the pathophysiology of myocardial ischemia, we evaluate the existing evidence for functional CCT imaging and its value in relation to alternative, well-established, non-invasive imaging modalities and invasive indices of ischemia (currently the gold-standard). The knowledge of clinical applications, benefits, and limitations of these new CCT technologies will allow efficient and optimal use in clinical practice in the near future.

The influence of image quality on diagnostic performance of a machine learning-based fractional flow reserve derived from coronary CT angiography

OBJECTIVE

To investigate the effect of image quality of coronary CT angiography (CCTA) on the diagnostic performance of a machine learning-based CT-derived fractional flow reserve (FFR_CT).

METHODS

This nationwide retrospective study enrolled participants from 10 individual centers across China. FFR_CT analysis was performed in 570 vessels in 437 patients. Invasive FFR and FFR_CT values ≤ 0.80 were considered ischemia-specific. Four-score subjective assessment based on image quality and objective measurement of vessel enhancement was performed on a per-vessel basis. The effects of body mass index (BMI), sex, heart rate, and coronary calcium score on the diagnostic performance of FFR_CT were studied.

RESULTS

Among 570 vessels, 216 were considered ischemia-specific by invasive FFR and 198 by FFR_CT. Sensitivity and specificity of FFR_CT for detecting lesion-specific ischemia were 0.82 and 0.93, respectively. Area under the curve (AUC) of high-quality images (0.93, n = 159) was found to be superior to low-quality images (0.80, n = 92, p = 0.02). Objective image quality and heart rate were also associated with diagnostic performance of FFR_CT, whereas there was no statistical difference in diagnostic performance among different BMI, sex, and calcium score groups (all p > 0.05, Bonferroni correction).

CONCLUSIONS

This retrospective multicenter study supported the FFR_CT as a noninvasive test in evaluating lesion-specific ischemia. Subjective image quality, vessel enhancement, and heart rate affect the diagnostic performance of FFR_CT.

KEY POINTS

• FFR_CTcan be used to evaluate lesion-specific ischemia. • Poor image quality negatively affects the diagnostic performance of FFR_CT. • CCTA with ≥ score 3, intracoronary enhancement degree of 300-400 HU, and heart rate below 70 bpm at scanning could be of great benefit to more accurate FFR_CTanalysis.

Diagnostic Performance of Machine Learning Based CT-FFR in Detecting Ischemia in Myocardial Bridging and Concomitant Proximal Atherosclerotic Disease

BACKGROUND

The diagnostic performance of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) in detecting ischemia in myocardial bridging (MB) has not been investigated to date.

METHODS

This retrospective multicentre study included 104 patients with left anterior descending MBs. MB was classified as either superficial or deep, short, or long, whereas all MB vessels were further divided into <50%, 50% to 69%, and ≥70% groups, according to proximal lumen stenosis on invasive coronary angiography. Diagnostic performance and receiver operating characteristics (ROC) of CT-FFR to detect lesion-specific ischemia was assessed on a per-vessel level, using invasive FFR as reference standard. Intraclass correlation coefficient (ICC) and Bland-Altman plots were used for agreement measurement.

RESULTS

Forty-eight MB vessels (46.2%) showed ischemia by invasive FFR (≤0.80). Sensitivity, specificity, and accuracy of CT-FFR to detect functional ischemia were 0.96 (0.85 to 0.99), 0.84 (0.71 to 0.92), and 0.89 (0.81 to 0.94), respectively, in all MB vessels. There were no differences in diagnostic performance between superficial and deep MB or between short and long MB (all P > 0.05). The accuracy of CT-FFR was 0.96 (0.85 to 0.99) in ≥70% stenosis, 0.82 (0.67 to 0.91) in 50% to 69% stenosis, and 0.89 (0.51 to 0.99) in <50% stenosis (P = 0.081). Bland-Altman analysis showed a slight mean difference between CT-FFR and invasive FFR of 0.014 (95% limit of agreement, -0.117 to 0.145). The ICC was 0.775 (95% confidence interval, 0.685-0.842, P < 0.001).

CONCLUSIONS

CT-FFR demonstrated high diagnostic performance for identifying functional ischemia in vessels with MB and concomitant proximal atherosclerotic disease when compared with invasive FFR. However, the clinical use of CT-FFR in patients with MB needs further study for stronger and more robust results.

Inter- and Intraoperator Variability in Measurement of On-Site CT-derived Fractional Flow Reserve Based on Structural and Fluid Analysis: A Comprehensive Analysis

Purpose

To measure the inter- and intraobserver variability among operators of varying expertise in conducting CT-derived fractional flow reserve (CT FFR) measurements on-site by using structural and fluid analysis and to evaluate differences in reproducibility between two different training methods for end users.

Materials and Methods

This retrospective analysis of the prospectively enrolled cohort included 22 symptomatic patients who underwent both 320-detector row coronary CT angiography and catheter-derived fractional flow reserve (FFR) within 90 days. Thirteen operators of varying expertise were assigned to one of two training arms: arm 1, on-site training by a specialist in CT FFR technology; arm 2, self-training through use of written materials. After the training, all 13 operators reviewed the CT data and measured CT FFR in 24 vessels in 22 patients. Inter- and intraoperator variability and agreements between CT FFR and catheter-derived FFR measurements were evaluated.

Results

The overall intraclass correlation coefficient (ICC) among operators was 0.71 (95% confidence interval: 0.58, 0.83) with a mean absolute difference (± standard deviation) of 0.027 ± 0.022. The operators in arm 2 showed greater interoperator differences than those in arm 1 (0.031 ± 0.024 vs 0.023 ± 0.018; P = .024). Among operators who recalculated CT FFR, the mean CT FFR value did not significantly differ between the first and second calculations (ICC, 0.66; 95% confidence interval: 0.46, 0.87), with the medical specialists producing the lowest intraoperator variability (0.053 ± 0.060). The overall correlation coefficient between CT FFR and catheter FFR was r = 0.61, with a mean absolute difference of 0.096 ± 0.089.

Conclusion

Good reproducibility of CT FFR values calculated on-site on the basis of structural and fluid analysis was observed among operators of varying expertise. Face-to-face training sessions may cause less variability.© RSNA, 2019Supplemental material is available for this article.

CT FFR for Ischemia-Specific CAD With a New Computational Fluid Dynamics Algorithm: A Chinese Multicenter Study

OBJECTIVES

The aim of this study was to validate the feasibility of a novel structural and computational fluid dynamics-based fractional flow reserve (FFR) algorithm for coronary computed tomography angiography (CTA), using alternative boundary conditions to detect lesion-specific ischemia.

BACKGROUND

A new model of computed tomographic (CT) FFR relying on boundary conditions derived from structural deformation of the coronary lumen and aorta with transluminal attenuation gradient and assumptions regarding microvascular resistance has been developed, but its accuracy has not yet been validated.

METHODS

A total of 338 consecutive patients with 422 vessels from 9 Chinese medical centers undergoing CTA and invasive FFR were retrospectively analyzed. CT FFR values were obtained on a novel on-site computational fluid dynamics-based CT FFR (uCT-FFR [version 1.5, United-Imaging Healthcare, Shanghai, China]). Performance characteristics of uCT-FFR and CTA in detecting lesion-specific ischemia in all lesions, intermediate lesions (luminal stenosis 30% to 70%), and "gray zone" lesions (FFR 0.75 to 0.80) were calculated with invasive FFR as the reference standard. The effect of coronary calcification on uCT-FFR measurements was also assessed.

RESULTS

Per vessel sensitivities, specificities, and accuracies of 0.89, 0.91, and 0.91 with uCT-FFR, 0.92, 0.34, and 0.55 with CTA, and 0.94, 0.37, and 0.58 with invasive coronary angiography, respectively, were found. There was higher specificity, accuracy, and AUC for uCT-FFR compared with CTA and qualitative invasive coronary angiography in all lesions, including intermediate lesions (p < 0.001 for all). No significant difference in diagnostic accuracy was observed in the "gray zone" range versus the other 2 lesion groups (FFR ≤0.75 and >0.80; p = 0.397) and in patients with "gray zone" versus FFR ≤0.75 (p = 0.633) and versus FFR >0.80 (p = 0.364), respectively. No significant difference in the diagnostic performance of uCT-FFR was found between patients with calcium scores ≥400 and <400 (p = 0.393).

CONCLUSIONS

This novel computational fluid dynamics-based CT FFR approach demonstrates good performance in detecting lesion-specific ischemia. Additionally, it outperforms CTA and qualitative invasive coronary angiography, most notably in intermediate lesions, and may potentially have diagnostic power in gray zone and highly calcified lesions.

Optimal Cutoff Value of Fractional Flow Reserve Derived From Coronary Computed Tomography Angiography for Predicting Hemodynamically Significant Coronary Artery Disease

Background:

The optimal cutoff value of fractional flow reserve (FFR) derived from coronary computed tomography angiography (FFR CT ) remains unclear.

Methods:

The current study population consisted of 93 patients with 139 vessels, who had suspected coronary artery disease by computed tomography angiography and underwent invasive FFR. We evaluated diagnostic performance of FFR CT according to different FFR CT cutoff values and FFR CT ranges with invasive FFR ≤0.80 as the reference standard.

Results:

In per-vessel analysis, median invasive FFR was 0.85 (interquartile range, 0.75–0.90), and 57 out of 139 vessels (41%) showed hemodynamically significant stenosis (≤0.80). Median FFR CT was 0.77 (interquartile range, 0.66–0.84; mean difference [invasive FFR-FFR CT ], 0.06±0.11). Per-vessel accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 73%, 95%, 59%, 61%, and 94% for the cutoff value of FFR CT ≤0.80, 81%, 86%, 78%, 73%, and 89% for FFR CT ≤0.75, and 83%, 74%, 89%, 82%, and 83% for FFR CT ≤0.70, respectively. Per-vessel accuracy across the different ranges of FFR CT ≤0.60, 0.61 to 0.70, 0.71 to 0.80, 0.81 to 0.90, and >0.90 with the cutoff value of FFR CT ≤0.80 were 95%, 74%, 32%, 93%, and 100%, respectively. Setting a gray zone of FFR CT 0.71 to 0.80 provided high positive predictive value (82%; n=42/51) in the range of FFR CT ≤0.70 and high negative predictive value (94%; n=48/51) in FFR CT >0.80.

Conclusions:

This study suggested that referral to invasive coronary angiography should be considered individually in the range of FFR CT 0.71 to 0.80, whereas dichotomous decision could be made in FFR CT ≤0.70 and >0.80. Future prospective studies evaluating clinical outcomes are needed to establish optimal FFR CT -based diagnostic algorithm.

Hemodynamic impact of coronary stenosis using computed tomography: comparison between noninvasive fractional flow reserve and 3D fusion of coronary angiography with stress myocardial perfusion

Vasodilator-stress CT perfusion imaging in addition to CT coronary angiography (CTCA) may provide a single-test alternative to nuclear stress testing, commonly used to assess hemodynamic significance of stenosis. Another alternative is fractional flow reserve (FFR) calculated from cardiac CT images. We studied the concordance between these two approaches and their relationship to outcomes. We prospectively studied 150 patients with chest pain, who underwent CTCA and regadenoson CT. CTCA images were interpreted for presence and severity of stenosis. Fused 3D displays of subendocardial X-ray attenuation with coronary arteries were created to detect stress perfusion defects (SPD) in each coronary territory. In patients with stenosis > 25%, CT-FFR was quantified. Significant stenosis was determined by: (1) combination of stenosis > 50% with an SPD, (2) CT-FFR ≤ 0.80. Patients were followed-up for 36 ± 25 months for death, myocardial infarction or revascularization. After excluding patients with normal arteries and technical/quality issues, in final analysis of 76 patients, CTCA depicted stenosis > 70% in 13/224 arteries, 50-70% in 24, and < 50% in 187. CT-FFR ≤ 0.80 was found in 41/224 arteries, and combination of SPD with > 50% stenosis in 31/224 arteries. Inter-technique agreement was 89%. Despite high incidence of abnormal CT-FFR (30/76 patients), only 7 patients experienced adverse outcomes; 6/7 also had SPDs. Only 1/9 patients with CT-FFR ≤ 0.80 but normal perfusion had an event. Fusion of CTCA and stress perfusion can help determine the hemodynamic impact of stenosis in one test, in good agreement with CT-FFR. Adding stress CT perfusion analysis may help risk-stratify patients with abnormal CT-FFR.

Comparison of Computed Tomography derived Fractional Flow Reserve to invasive Fractional Flow Reserve in Diagnosis of Functional Coronary Stenosis: A Meta-Analysis

Computed Tomography derived Fractional Flow Reserve (CTFFR) is an emerging non-invasive imaging modality to assess functional significance of coronary stenosis. We performed a meta-analysis to compare the diagnostic performance of CTFFR to invasive Fractional Flow reserve (FFR). Electronic search was performed to identify relevant articles. Pooled Estimates of sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-) and diagnostic odds ratio (DOR) with corresponding 95% confidence intervals (CI) were calculated at the patient level as well as the individual vessel level using hierarchical logistic regression, summary receiver operating characteristic (SROC) curve and area under the curve were estimated. Our search yielded 559 articles and of these 17 studies was included in the analysis. A total of 2,191 vessels in 1294 patients were analyzed. Pooled estimates of sensitivity, specificity, LR+, LR- and DOR with corresponding 95% CI at per-patient level were 83% (79-87), 72% (68-76), 3.0 (2.6-3.5), 0.23 (0.18-0.29) and 13 (9-18) respectively. Pooled estimates of sensitivity, specificity, LR+, LR- and DOR with corresponding 95% CI at per-vessel level were 85% (83-88), 76% (74-79), 3.6 (3.3-4.0), 0.19 (0.16-0.22) and 19 (15-24). The area under the SROC curve was 0.89 for both per patient level and at the per vessel level. In our meta-analysis, CTFFR demonstrated good diagnostic performance in identifying functionally significant coronary artery stenosis compared to the FFR.

Application of Patient-Specific Computational Fluid Dynamics in Coronary and Intra-Cardiac Flow Simulations: Challenges and Opportunities

The emergence of new cardiac diagnostics and therapeutics of the heart has given rise to the challenging field of virtual design and testing of technologies in a patient-specific environment. Given the recent advances in medical imaging, computational power and mathematical algorithms, patient-specific cardiac models can be produced from cardiac images faster, and more efficiently than ever before. The emergence of patient-specific computational fluid dynamics (CFD) has paved the way for the new field of computer-aided diagnostics. This article provides a review of CFD methods, challenges and opportunities in coronary and intra-cardiac flow simulations. It includes a review of market products and clinical trials. Key components of patient-specific CFD are covered briefly which include image segmentation, geometry reconstruction, mesh generation, fluid-structure interaction, and solver techniques.

Fusion of Three-Dimensional Echocardiographic Regional Myocardial Strain with Cardiac Computed Tomography for Noninvasive Evaluation of the Hemodynamic Impact of Coronary Stenosis in Patients with Chest Pain

BACKGROUND

Combined evaluation of coronary stenosis and the extent of ischemia is essential in patients with chest pain. Intermediate-grade stenosis on computed tomographic coronary angiography (CTCA) frequently triggers downstream nuclear stress testing. Alternative approaches without stress and/or radiation may have important implications. Myocardial strain measured from echocardiographic images can be used to detect subclinical dysfunction. The authors recently tested the feasibility of fusion of three-dimensional (3D) echocardiography-derived regional resting longitudinal strain with coronary arteries from CTCA to determine the hemodynamic significance of stenosis. The aim of the present study was to validate this approach against accepted reference techniques.

METHODS

Seventy-eight patients with chest pain referred for CTCA who also underwent 3D echocardiography and regadenoson stress computed tomography were prospectively studied. Left ventricular longitudinal strain data (TomTec) were used to generate fused 3D displays and detect resting strain abnormalities (RSAs) in each coronary territory. Computed tomographic coronary angiographic images were interpreted for the presence and severity of stenosis. Fused 3D displays of subendocardial x-ray attenuation were created to detect stress perfusion defects (SPDs). In patients with stenosis >25% in at least one artery, fractional flow reserve was quantified (HeartFlow). RSA as a marker of significant stenosis was validated against two different combined references: stenosis >50% on CTCA and SPDs seen in the same territory (reference standard A) and fractional flow reserve < 0.80 and SPDs in the same territory (reference standard B).

RESULTS

Of the 99 arteries with no stenosis >50% and no SPDs, considered as normal, 19 (19%) had RSAs. Conversely, with stenosis >50% and SPDs, RSAs were considerably more frequent (17 of 24 [71%]). The sensitivity, specificity, and accuracy of RSA were 0.71, 0.81, and 0.79, respectively, against reference standard A and 0.83, 0.81, and 0.82 against reference standard B.

CONCLUSIONS

Fusion of CTCA and 3D echocardiography-derived resting myocardial strain provides combined displays, which may be useful in determination of the hemodynamic or functional impact of coronary abnormalities, without additional ionizing radiation or stress testing.