Invasive Fractional Flow Reserve Research Articles

Non-invasive fractional flow reserve estimation in coronary arteries using angiographic images

Coronary artery disease is the leading global cause of mortality and Fractional Flow Reserve (FFR) is widely regarded as the gold standard for assessing coronary artery stenosis severity. However, due to the limitations of invasive FFR measurements, there is a pressing need for a highly accurate virtual FFR calculation framework. Additionally, it’s essential to consider local haemodynamic factors such as time-averaged wall shear stress (TAWSS), which play a critical role in advancement of atherosclerosis. This study introduces an innovative FFR computation method that involves creating five patient-specific geometries from two-dimensional coronary angiography images and conducting numerical simulations using computational fluid dynamics with a three-element Windkessel model boundary condition at the outlet to predict haemodynamic distribution. Furthermore, four distinct boundary condition methodologies are applied to each geometry for comprehensive analysis. Several haemodynamic features, including velocity, pressure, TAWSS, and oscillatory shear index are investigated and compared for each case. Results show that models with average boundary conditions can predict FFR values accurately and observed errors between invasive FFR and virtual FFR are found to be less than 5%.

Non-invasive fractional flow reserve estimation in coronary arteries using angiographic images

Coronary artery disease is the leading global cause of mortality and Fractional Flow Reserve (FFR) is widely regarded as the gold standard for assessing coronary artery stenosis severity. However, due to the limitations of invasive FFR measurements, there is a pressing need for a highly accurate virtual FFR calculation framework. Additionally, it’s essential to consider local haemodynamic factors such as time-averaged wall shear stress (TAWSS), which play a critical role in advancement of atherosclerosis. This study introduces an innovative FFR computation method that involves creating five patient-specific geometries from two-dimensional coronary angiography images and conducting numerical simulations using computational fluid dynamics with a three-element Windkessel model boundary condition at the outlet to predict haemodynamic distribution. Furthermore, four distinct boundary condition methodologies are applied to each geometry for comprehensive analysis. Several haemodynamic features, including velocity, pressure, TAWSS, and oscillatory shear index are investigated and compared for each case. Results show that models with average boundary conditions can predict FFR values accurately and observed errors between invasive FFR and virtual FFR are found to be less than 5%.

Invasive fractional-flow-reserve prediction by coronary CT angiography using artificial intelligence vs. computational fluid dynamics software in intermediate-grade stenosis.

Coronary computed angiography (CCTA) with non-invasive fractional flow reserve (FFR) calculates lesion-specific ischemia when compared with invasive FFR and can be considered for patients with stable chest pain and intermediate-grade stenoses according to recent guidelines. The objective of this study was to compare a new CCTA-based artificial-intelligence deep-learning model for FFR prediction (FFRAI) to computational fluid dynamics CT-derived FFR (FFRCT) in patients with intermediate-grade coronary stenoses with FFR as reference standard. The FFRAI model was trained with curved multiplanar-reconstruction CCTA images of 500 stenotic vessels in 413 patients, using FFR measurements as the ground truth. We included 37 patients with 39 intermediate-grade stenoses on CCTA and invasive coronary angiography, and with FFRCT and FFR measurements in this retrospective proof of concept study. FFRAI was compared with FFRCT regarding the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for predicting FFR ≤ 0.80. Sensitivity, specificity, PPV, NPV, and diagnostic accuracy of FFRAI in predicting FFR ≤ 0.80 were 91% (10/11), 82% (23/28), 67% (10/15), 96% (23/24), and 85% (33/39), respectively. Corresponding values for FFRCT were 82% (9/11), 75% (21/28), 56% (9/16), 91% (21/23), and 77% (30/39), respectively. Diagnostic accuracy did not differ significantly between FFRAI and FFRCT (p = 0.12). FFRAI performed similarly to FFRCT for predicting intermediate-grade coronary stenoses with FFR ≤ 0.80. These findings suggest FFRAI as a potential non-invasive imaging tool for guiding therapeutic management in these stenoses.

Clinical Likelihood Prediction of Hemodynamically Obstructive CoronaryArtery Disease in Patients WithStable Chest Pain.

Selection for invasive angiography is recommended to be based on pretest probabilities (PTPs), and physiological measures of hemodynamical impairment by, for example, fractional flow reserve (FFR) should guide revascularization. The risk factor-weighted clinical likelihood (RF-CL) and coronary artery calcium score-weighted clinical likelihood (CACS-CL) models show superior discrimination of patients with suspected obstructive coronary artery disease (CAD), but validation against hemodynamic impairment is warranted. The aim of this study was to validate the RF-CL and CACS-CL models against hemodynamically obstructive CAD. Stable de novo chest pain patients (N=4,371) underwent coronary computed tomography angiography and subsequently invasive coronary angiography with FFR measurements. Hemodynamically obstructive CAD was defined as invasive FFR≤0.80 or high-grade stenosis by visual assessment (>90% diameter stenosis). For comparison, a guideline-endorsed basic PTP model was calculated based on age, sex, and symptom typicality. The RF-CL model additionally included the number of risk factors, and the CACS-CL model incorporated the coronary artery calcium score into the RF-CL. In total, 447 of 4,371 (10.9%) patients had hemodynamically obstructive CAD. Both the RF-CL and CACS-CL models classified more patients with a very low clinical likelihood (≤5%) of obstructive CAD compared to the basic PTP model (33.0% and 53.7% vs 12.0%; P< 0.001) with a preserved low prevalence of hemodynamically obstructive CAD (<5% for all models). Against hemodynamically obstructive CAD, calibration and discrimination of the RF-CL and CACS-CL models were superior to the basic PTP model. The RF-CL and CACS-CL models are well calibrated and superior to a currently recommended basic PTP model to predict hemodynamically obstructive CAD. (Danish Study of Non-Invasive Diagnostic Testing in Coronary Artery Disease [Dan-NICAD]; NCT02264717; Danish Study of Non-Invasive Diagnostic Testing in Coronary Artery Disease 2 [Dan-NICAD 2]; NCT03481712, Danish Study of Non-Invasive Diagnostic Testing in Coronary Artery Disease 3 [Dan-NICAD 3]; NCT04707859).

CT-derived Fractional Flow Reserve: How, When, and Where to use this Novel Cardiac Imaging Tool.

Fractional flow reserve computed tomography (FFRCT) is a novel imaging modality. It utilizes computational fluid dynamics analysis of coronary blood flow obtained from CCTA images to estimate the decrease in pressure across coronary stenosis during the maximum hyperemia. The FFRCT can serve as a valuable tool in the assessment of coronary artery disease (CAD). This non-invasive option can be used as an alternative to the invasive fractional Flow Reserve (FFR) evaluation, which is presently considered the gold standard for evaluating the physiological significance of coronary stenoses. It can help in several clinical situations, including Assessment of Acute and stable chest pain, virtual planning for coronary stenting, and treatment decision-making. Although FFRCT has demonstrated potential clinical applications as a non-invasive imaging technique, it is also crucial to acknowledge its limitations in clinical practice. As a result, it is imperative to meticulously evaluate the advantages and drawbacks of FFRCT individually and contemplate its application in combination with other diagnostic examinations and clinical data.

Agreement of Fractional Flow Reserve Estimated by Computed Tomography With Invasively Measured Fractional Flow Reserve: A Systematic Review and Meta-Analysis.

Fractional flow reserve (FFR) is the ratio of blood pressure measured distal to a stenosis and pressure proximal to a stenosis. FFR can be estimated noninvasively using computed tomography (CT) although the usefulness of this technique remains controversial. This meta-analysis evaluated the agreement of FFR estimated by CT (FFR-CT) with invasively measured FFR. The study also evaluated the diagnostic accuracy of FFR-CT, defined as the ability of FFR-CT to classify lesions as hemodynamically significant (invasive FFR ≤0.8) or insignificant (invasive FFR >0.8). Forty-three studies reporting on 7291 blood vessels from 5236 patients were included. A moderate positive linear relationship between FFR-CT and invasively measured FFR was observed (Spearman correlation coefficient: 0.67). Agreement between the 2 measures increased as invasively measured FFR values approached 1. The overall diagnostic accuracy, sensitivity and specificity of FFR-CT were 82.2%, 80.9%, and 83.1%, respectively. Diagnostic accuracy of 90% could be demonstrated for FFR-CT values >0.90 and <0.49. The diagnostic accuracy of off-site tools was 79.4% and the diagnostic accuracy of on-site tools was 84.1%. The agreement between FFR-CT and invasive FFR is moderate although agreement is highest in vessels with FFR-CT >0.9. Diagnostic accuracy varies widely with FFR-CT value but is above 90% for FFR-CT values >0.90 and <0.49. Furthermore, on-site and off-site tools have similar performance. Ultimately, FFR-CT may be a useful adjunct to CT coronary angiography as a gatekeeper for invasive coronary angiogram.

Value of Dynamic Computed Tomography Myocardial Perfusion in CAD: A Systematic Review and Meta-analysis.

Dynamic stress computed tomography (CT) perfusion is a non-invasive method for quantifying myocardial ischemia by assessing myocardial blood flow (MBF). In this meta-analysis, we evaluated the diagnostic accuracy of dynamic CT perfusion for the detection of significant coronary artery disease (CAD) across various CT scanners, obese patients, and its prognostic value. We systematically searched PubMed, Embase, Web of Science, and Cochrane library for published studies evaluating the accuracy of CT myocardial perfusion in diagnosing functional significant ischemia by invasive fractional flow reserve. The diagnostic performance of dynamic CT perfusion in detecting ischemia was evaluated using a summary receiver operating characteristic (sROC) curve. A total of 23 studies underwent meta- analysis. In myocardial region without ischemia, MBF was measured at 1.44 ml/min/g (95% confidence interval [CI]: 1.13-1.75), while in region with ischemia, it was 0.94 ml/min/g (95% CI: 0.80-1.08) (p<0.001). On the patient-based analysis, the area under the sROC curve of CT-MBF was 0.93, with a sensitivity of 0.84 and specificity of 0.88. Differences in CT type (dual source vs. single source), and body mass index (BMI) did not significantly affect the diagnostic performance. The pooled hazard ratio of dynamic CT perfusion for predicting adverse events was 4.98 (95%CI: 2.08-11.93, p=<0.001, I2=61%, p for heterogeneity = 0.07). Dynamic CT perfusion has high diagnostic performance in the quantitative assessment of ischemia and detection of functional myocardial ischemia as defined by invasive FFR, and may be useful in risk stratification of CAD patients.

Diagnostic Performance of On-Site Automatic Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve.

Fractional flow reserve (FFR) is an invasive standard method to identify ischemia-causing coronary artery disease (CAD). With the advancement of technology, FFR can be noninvasively computed from coronary computed tomography angiography (CCTA). Recently, a novel simpler method has been developed to calculate on-site CCTA-derived FFR (CT-FFR) with a commercially available workstation. A total of 319 CAD patients who underwent CCTA, invasive coronary angiography, and FFR measurement were included. The primary outcome was the accuracy of CT-FFR for defining myocardial ischemia evaluated with an invasive FFR as a reference. The presence of ischemia was defined as FFR ≤0.80. Anatomical obstructive stenosis was defined as diameter stenosis on CCTA ≥50%, and the diagnostic performance of CT-FFR and CCTA stenosis for ischemia was compared. Among participants (mean age 64.7±9.4 years, male 77.7%), mean FFR was 0.82±0.10, and 126 (39.5%) patients had an invasive FFR value of ≤0.80. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of CT-FFR were 80.6% (95% confidence interval [CI], 80.5-80.7%), 88.1% (95% CI, 82.4-93.7%), 75.6% (95% CI, 69.6-81.7%), 70.3% (95% CI, 63.1-77.4%), and 90.7% (95% CI, 86.2-95.2%), respectively. CT-FFR had higher diagnostic accuracy (80.6% vs. 59.1%, p<0.001) and discriminant ability (area under the curve from receiver operating characteristic curve 0.86 vs. 0.64, p<0.001), compared with anatomical obstructive stenosis on CCTA. This novel CT-FFR obtained from an on-site workstation demonstrated clinically acceptable diagnostic performance and provided better diagnostic accuracy and discriminant ability for identifying hemodynamically significant lesions than CCTA alone.

Microvascular resistance reserve before and after PCI: A serial FFR and [15O] H2O PET study

Background and aimsMicrovascular Resistance Reserve (MRR) has recently been introduced as a microvasculature-specific index and hypothesized to be independent of coronary stenosis. The aim of this study was to investigate the change of MRR after percutaneous coronary intervention (PCI). MethodsIn this post-hoc analysis from the PACIFC trials, symptomatic patients underwent [15O]H2O positron emission tomography (PET) and invasive fractional flow reserve (FFR) before and after revascularization. Coronary flow reserve (CFR) from PET and invasive FFR were used to calculate MRR. ResultsAmong 52 patients (87 % male, age 59.4 ± 9.4 years), 61 vessels with a median FFR of 0.71 (95 % confidence interval: 0.55 to 0.74) and a mean MRR of 3.80 ± 1.23 were included. Following PCI, FFR, hyperemic myocardial blood flow (hMBF) and CFR increased significantly (all p-values ≤0.001). MRR remained unchanged after PCI (3.80 ± 1.23 before PCI versus 3.60 ± 0.97 after PCI; p=0.23). In vessels with a pre-PCI, FFR ≤0.70 pre- and post-PCI MRR were 3.90 ± 1.30 and 3.73 ± 1.14 (p=0.56), respectively. Similar findings were observed for vessels with a FFR between 0.71 and 0.80 (pre-PCI MRR 3.70 ± 1.17 vs. post PCI MRR 3.48 ± 0.76, p=0.19). ConclusionsOur study indicates that MRR, assessed using a hybrid approach of PET and invasive FFR, is independent of the severity of epicardial stenosis. These findings suggest that MRR is a microvasculature-specific parameter.

Feasibility and Diagnostic Performance of Functional SYNTAX Score Derived From Dynamic CT Myocardial Perfusion Imaging.

Computed tomography (CT) fractional flow reserve (FFR)-derived functional SYNTAX score (FSSCT-FFR) is a valuable method for guiding treatment strategy in patients with multivessel coronary artery disease. Dynamic CT myocardial perfusion imaging (CT-MPI) demonstrates higher diagnostic accuracy than CT-FFR in identifying hemodynamically significant coronary artery disease. We aimed to evaluate the feasibility of CT-MPI-derived FSS (FSSCT-MPI) with reference to invasive FSS. In this retrospective study, patients with multivessel coronary artery disease who underwent dynamic CT-MPI+ coronary CT angiography and invasive coronary angiography or FFR within 4 weeks were consecutively included. Invasive (FSSinvasive) and noninvasive FSS (FSSCT-MPI and FSSCT-FFR) were calculated by an online calculator, which assigned points to lesions with hemodynamic significance (defined as FFRinvasive ≤0.80, invasive coronary angiography diameter stenosis ≥90%, CT-FFR ≤0.80, and myocardial ischemia on CT-MPI). Weighted κ value and net reclassification index were calculated to determine the consistency and incremental discriminatory power of FSSCT-MPI. Receiver operating characteristic curve analysis was used for the comparison of FSSCT-MPI and FSSCT-FFR in detecting intermediate- to high-risk patients. A total of 119 patients (96 men; 64.6±10.6 years) with 305 obstructive lesions were included. The average FSSCT-MPI, FSSCT-FFR, and FSSinvasive were 15.58±13.03, 16.18±13.30, and 13.11±12.22, respectively. The agreement on risk classification based on the FSSCT-MPI tertiles was good (weighted κ, 0.808). With reference to FSSinvasive, FSSCT-MPI correctly reclassified 27 (22.7%) patients from the intermediate- to high SYNTAX score group to the low-score group (net reclassification index, 0.30; P<0.001). In patients with severe calcification, FSSCT-MPI had better diagnostic value than FSSCT-FFR in detecting intermediate- to high-risk patients when compared with FSSinvasive (area under the curve, 0.976 versus 0.884; P<0.001). Noninvasive FSS derived from CT-MPI is feasible and has strong concordance with FSSinvasive. It allows accurate categorization of FSS in patients with multivessel coronary artery disease, in particular with severe calcification.

Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD

Currently, clinically available coronary CT angiography (CCTA) derived fractional flow reserve (CT-FFR) is time-consuming and complex. We propose a novel artificial intelligence-based fully-automated, on-site CT-FFR technology, which combines the automated coronary plaque segmentation and luminal extraction model with reduced order 3 dimentional (3D) computational fluid dynamics. A total of 463 consecutive patients with 600 vessels from the updated China CT-FFR study in Cohort 1 undergoing both CCTA and invasive fractional flow reserve (FFR) within 90 d were collected for diagnostic performance evaluation. For Cohort 2, a total of 901 chronic coronary syndromes patients with index CT-FFR and clinical outcomes at 3-year follow-up were retrospectively analyzed. In Cohort 3, the association between index CT-FFR from triple-rule-out CTA and major adverse cardiac events in patients with acute chest pain from the emergency department was further evaluated. The diagnostic accuracy of this CT-FFR in Cohort 1 was 0.82 with an area under the curve of 0.82 on a per-patient level. Compared with the manually dependent CT-FFR techniques, the operation time of this technique was substantially shortened by 3 times and the number of clicks from about 60 to 1. This CT-FFR technique has a highly successful (> 99%) calculation rate and also provides superior prediction value for major adverse cardiac events than CCTA alone both in patients with chronic coronary syndromes and acute chest pain. Thus, the novel artificial intelligence-based fully automated, on-site CT-FFR technique can function as an objective and convenient tool for coronary stenosis functional evaluation in the real-world clinical setting.

Evaluation of fractional flow reserve and atherosclerotic plaque characteristics on coronary non-contrast T1-weighted magnetic resonance imaging

Background and aimsThe relationship between high-risk coronary plaque characteristics regardless of the severity of lesion stenosis and myocardial ischemia remains unsettled. High-intensity plaques (HIPs) on non-contrast T1-weighted magnetic resonance imaging (T1WI) have been characterized as high-risk coronary plaques. We sought to elucidate whether the presence of coronary HIPs on T1WI influences fractional flow reserve (FFR) in the distal segment of the vessel. MethodsWe retrospectively analyzed 281 vessels in 231 patients with chronic coronary syndrome who underwent invasive FFR measurement and coronary T1WI using a multicenter registry. The plaque-to-myocardial signal intensity ratio (PMR) of the most stenotic lesion was evaluated; a coronary plaque with PMR ≥1.4 was defined as a HIP. ResultsThe median PMR of coronary plaques on T1WI in vessels with FFR ≤0.80 was significantly higher than that of plaques with FFR >0.80 (1.17 [interquartile range (IQR): 0.99–1.44] vs. 0.97 [IQR: 0.85–1.09]; p < 0.001). Multivariable analysis showed that an increase in PMR of the most stenotic segment was associated with lower FFR (beta-coefficient, −0.050; p < 0.001). The presence of coronary HIPs was an independent predictor of FFR ≤0.80 (odds ratio (OR), 6.18; 95% confidence interval (CI), 1.93–19.77; p = 0.002). Even after adjusting for plaque composition characteristics based on computed tomography angiography, the presence of coronary HIPs was an independent predictor of FFR ≤0.80 (OR, 4.48; 95% CI, 1.19–16.80; p = 0.026). ConclusionsCoronary plaques with high PMR are associated with low FFR in the corresponding vessel, indicating that plaque morphology might influence myocardial ischemia severity.

Diagnostic accuracy of Murray law-based quantitative flow ratio in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.

Murray law-based quantitative flow ratio (μQFR) is a novel computational method that enables accurate estimation of fractional flow reserve (FFR) using a single angiographic projection. However, its diagnostic value in patients with severe aortic stenosis (AS) remains unclear. We included 25 consecutive patients who underwent transcatheter aortic valve replacement (TAVR) for severe AS with intermediate or greater (30-90%) coronary artery disease (CAD). Pre- and post-TAVR μQFR, QFR, instantaneous flow reserve (iFR), and post-TAVR invasive FFR values were measured. We evaluated the diagnostic performance of pre-TAVR μQFR, QFR, and iFR using post-TAVR FFR ≤ 0.80 as a reference standard of ischemia. Pre-TAVR μQFR was significantly correlated with post-TAVR FFR (r = 0.73, p < 0.0001). The area under the curve of pre-TAVR μQFR on post-TAVR FFR ≤ 0.8 was 0.91 (95% confidence interval [CI] 0.77-0.98), comparable to that of pre-TAVR iFR (0.86 [95% CI 0.71-0.98], p = 0.97). The accuracy, sensitivity, specificity, and positive and negative predictive values of pre-TAVR μQFR on post-TAVR FFR ≤ 0.8 were 84.2% (95% CI 68.7-93.4), 61.6% (95% CI 31.6-86.1), 96.0% (95% CI 79.6-99.9), 88.9% (95% CI 52.9-98.3), and 82.8% (95% CI 70.6-90.6), respectively. For pre-TAVR iFR, these values were 76.5% (95% CI 58.8-89.3), 90.9% (95% CI 58.7-99.8), 69.6% (95% CI 47.1-86.8), 58.8% (95% CI 42.8-73.1), and 94.1% (95% CI 70.8-99.1), respectively. μQFR could be useful for the physiological evaluation of patients with severe AS with concomitant CAD.

Association between vessel-specific coronary Aggregated plaque burden, Agatston score and hemodynamic significance of coronary disease (The CAPTivAte study)

BackgroundCT coronary angiography (CTCA) is a guideline-endorsed assessment for patients with stable angina and suspected coronary disease. Although associated with excellent negative predictive value in ruling out obstructive coronary disease, there are limitations in the ability of CTCA to predict hemodynamically significant coronary disease. The CAPTivAte study aims to assess the utility of Aggregated Plaque Burden (APB) in predicting ischemia based on Fractional Flow Reserve (FFR). MethodsIn this retrospective study, patients who had a CTCA and invasive FFR of the LAD were included. The entire length of the LAD was analyzed using semi-automated software which characterized total plaque burden and plaque morphological subtype (including Low Attenuation Plaque (LAP), Non-calcific plaque (NCP) and Calcific Plaque (CP). Aggregated Plaque Burden (APB) was calculated. Univariate and multivariate analysis were performed to assess the association between these CT-derived parameters and invasive FFR. ResultsThere were 145 patients included in this study. 84.8 % of patients were referred with stable angina. There was a significant linear association between APB and FFR in both univariate and multivariate analysis (Adjusted R-squared = 0.0469; p = 0.035). Mean Agatston scores are higher in FFR positive vessels compared to FFR negative vessels (371.6 (±443.8) vs 251.9 (±283.5, p = 0.0493). ConclusionCTCA-derived APB is a reliable predictor of ischemia assessed using invasive FFR and may aid clinicians in rationalizing invasive vs non-invasive management strategies. Vessel-specific Agatston scores are significantly higher in FFR-positive vessels than in FFR-negative vessels. Associations between HU-derived plaque subtype and invasive FFR were inconclusive in this study.

Diagnostic Performance of Angiography-Derived Quantitative Flow Ratio in Complex Coronary Lesions.

Quantitative flow ratio derived from computed tomography angiography (CT-QFR) and invasive coronary angiography (Murray law-based quantitative flow ratio [μQFR]) are novel approaches enabling rapid computation of fractional flow reserve without the use of pressure guidewires and vasodilators. However, the feasibility and diagnostic performance of both CT-QFR and μQFR in evaluating complex coronary lesions remain unclear. Between September 2014 and September 2021, 240 patients with 30% to 90% coronary diameter stenosis who underwent both coronary computed tomography angiography and invasive coronary angiography with fractional flow reserve within 60 days were retrospectively enrolled. The diagnostic performance of CT-QFR and μQFR in detecting functional ischemia among all lesions, especially complex coronary lesions, was analyzed using fractional flow reserve as the reference standard. CT-QFR and μQFR analyses were performed on 309 and 289 vessels, respectively. The diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for CT-QFR in all lesions at the per-vessel level were 91% (with a 95% CI of 84%-96%), 92% (95% CI, 88%-95%), 83% (95% CI, 75%-90%), 96% (95% CI, 93%-98%), and 92% (95% CI, 88%-95%), with values for μQFR of 90% (95% CI, 81%-95%), 97% (95% CI, 93%-99%), 92% (95% CI, 84%-97%), 96% (95% CI, 92%-98%), and 94% (95% CI, 91%-97%), respectively. Among bifurcation, tandem, and moderate-to-severe calcified lesions, the diagnostic values of CT-QFR and μQFR showed great correlation and agreement with those of invasive fractional flow reserve, achieving an area under the receiver operating characteristic curve exceeding 0.9 for each complex lesion at the vessel level. Furthermore, the accuracies of CT-QFR and μQFR in the gray zone were 85% and 84%, respectively. Angiography-derived quantitative flow ratio (CT-QFR and μQFR) demonstrated remarkable diagnostic performance in complex coronary lesions, indicating its pivotal role in the management of patients with coronary artery disease.

Comparison of machine learning-based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease.

To compare the diagnostic performance of machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and cardiac magnetic resonance (MR) perfusion mapping for functional assessment of coronary stenosis. Between October 2020 and March 2022, consecutive participants with stable coronary artery disease (CAD) were prospectively enrolled and underwent coronary CTA, cardiac MR, and invasive fractional flow reserve (FFR) within 2weeks. Cardiac MR perfusion analysis was quantified by stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Hemodynamically significant stenosis was defined as FFR ≤ 0.8 or > 90% stenosis on invasive coronary angiography (ICA). The diagnostic performance of CT-FFR, MBF, and MPR was compared, using invasive FFR as a reference. The study protocol was completed in 110 participants (mean age, 62years ± 8; 73 men), and hemodynamically significant stenosis was detected in 36 (33%). Among the quantitative perfusion indices, MPR had the largest area under receiver operating characteristic curve (AUC) (0.90) for identifying hemodynamically significant stenosis, which is in comparison with ML-based CT-FFR on the vessel level (AUC 0.89, p = 0.71), with comparable sensitivity (89% vs 79%, p = 0.20), specificity (87% vs 84%, p = 0.48), and accuracy (88% vs 83%, p = 0.24). However, MPR outperformed ML-based CT-FFR on the patient level (AUC 0.96 vs 0.86, p = 0.03), with improved specificity (95% vs 82%, p = 0.01) and accuracy (95% vs 81%, p < 0.01). ML-based CT-FFR and quantitative cardiac MR showed comparable diagnostic performance in detecting vessel-specific hemodynamically significant stenosis, whereas quantitative perfusion mapping had a favorable performance in per-patient analysis. ML-based CT-FFR and MPR derived from cardiac MR performed well in diagnosing vessel-specific hemodynamically significant stenosis, both of which showed no statistical discrepancy with each other. • Both machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and quantitative perfusion cardiac MR performed well in the detection of hemodynamically significant stenosis. • Compared with stress myocardial blood flow (MBF) from quantitative perfusion cardiac MR, myocardial perfusion reserve (MPR) provided higher diagnostic performance for detecting hemodynamically significant coronary artery stenosis. • ML-based CT-FFR and MPR from quantitative cardiac MR perfusion yielded similar diagnostic performance in assessing vessel-specific hemodynamically significant stenosis, whereas MPR had a favorable performance in per-patient analysis.

Accuracy and temporal analysis of non-Newtonian models of blood in the computational FFR – Numerical implementation

Non-invasive assessment of stenosed coronary is a goal for clinical applications. Thus, engineering aligned with medicine has been the solution to solve this problem. This work, serving as a proof of concept, has the main goal to predict the non-invasive fractional flow reserve (FFR) in an atherosclerotic coronary artery. A 3-element Windkessel and Womersley models were used to model boundary conditions of the patient-specific coronary arteries: the pressure at the outlets of the artery and the velocity at the inlet, respectively. Apart from implementing the previous conditions in the numerical code, the novelty of this work is to implement and use, in numerical simulations, Newtonian and non-Newtonian models for blood such as Carreau, Carreau-Yasuda, Casson and Simplified Phan-Thien/Tanner to analyse the accuracy of the computed FFR and the computational time of the simulations. Results indicate that the non-Newtonian viscoelastic model, Simplified Phan-Thien/Tanner, took the longest time but produced the most accurate results - 8h and 2.7% relative error between invasive and non-invasive FFR. The shear-thinning non-Newtonian models required lower computational times but returned a higher average error (3.9h; 7.6% error) which is considered significant. The Newtonian model required the least time but had the highest error value (2.9h; 7.8% error). Thus, although the computational time considering the non-Newtonian viscoelastic model is the highest, this model must be considered in the numerical simulations to obtain the FFR since it is the most accurate for this patient case. This research is a step forward to numerically quantify the non-invasive FFR of a patient.

Artificial intelligence coronary computed tomography, coronary computed tomography angiography using fractional flow reserve, and physician visual interpretation in the per-vessel prediction of abnormal invasive adenosine fractional flow reserve

Abstract Aims A comparison of diagnostic performance comparing AI-QCTISCHEMIA, coronary computed tomography angiography using fractional flow reserve (CT-FFR), and physician visual interpretation on the prediction of invasive adenosine FFR have not been evaluated. Furthermore, the coronary plaque characteristics impacting these tests have not been assessed. Methods and results In a single centre, 43-month retrospective review of 442 patients referred for coronary computed tomography angiography and CT-FFR, 44 patients with CT-FFR had 54 vessels assessed using intracoronary adenosine FFR within 60 days. A comparison of the diagnostic performance among these three techniques for the prediction of FFR ≤ 0.80 was reported. The mean age of the study population was 65 years, 76.9% were male, and the median coronary artery calcium was 654. When analysing the per-vessel ischaemia prediction, AI-QCTISCHEMIA had greater specificity, positive predictive value (PPV), diagnostic accuracy, and area under the curve (AUC) vs. CT-FFR and physician visual interpretation CAD-RADS. The AUC for AI-QCTISCHEMIA was 0.91 vs. 0.76 for CT-FFR and 0.62 for CAD-RADS ≥ 3. Plaque characteristics that were different in false positive vs. true positive cases for AI-QCTISCHEMIA were max stenosis diameter % (54% vs. 67%, P &amp;lt; 0.01); for CT-FFR were maximum stenosis diameter % (40% vs. 65%, P &amp;lt; 0.001), total non-calcified plaque (9% vs. 13%, P &amp;lt; 0.01); and for physician visual interpretation CAD-RADS ≥ 3 were total non-calcified plaque (8% vs. 12%, P &amp;lt; 0.01), lumen volume (681 vs. 510 mm3, P = 0.02), maximum stenosis diameter % (40% vs. 62%, P &amp;lt; 0.001), total plaque (19% vs. 33%, P = 0.002), and total calcified plaque (11% vs. 22%, P = 0.003). Conclusion Regarding per-vessel prediction of FFR ≤ 0.8, AI-QCTISCHEMIA revealed greater specificity, PPV, accuracy, and AUC vs. CT-FFR and physician visual interpretation CAD-RADS ≥ 3.

Diagnostic Performance of Diastolic Hyperemia-Free Ratio Compared With Invasive Fractional Flow Reserve for Evaluation of Coronary Artery Disease

Hyperemic and nonhyperemic pressure ratios are frequently used to assess the hemodynamic significance of coronary artery disease and to guide the need for myocardial revascularization. However, there are limited data on the diagnostic performance of the diastolic hyperemia-free ratio (DFR). We evaluated the diagnostic performance of the DFR compared with invasive fractional flow reserve (FFR). We performed a prospective, single-center study of 308 patients (343 lesions) who underwent DFR and FFR for evaluation of visually estimated 40% to 90% stenoses. Diagnostic performance of the DFR compared with FFR was evaluated using linear regression, Bland-Altman analysis, and receiver operating characteristic curves. The overall diagnostic accuracy of the DFR was 83%; the accuracy rates were 86%, 40%, and 95% when the DFR was <0.86, 0.88 to 0.90, and >0.93, respectively. The sensitivity, specificity, positive predicative value, and negative predictive value were 60%, 91%, 71%, and 87%, respectively. The Pearson correlation coefficient was 0.75 (p <0.05). The Bland-Altman analysis showed a mean difference of 0.09, and the area under the receiver operating characteristic curve was 0.88 (95% confidence interval 0.84 to 0.92, p <0.05). In conclusion, the DFR has a good diagnostic performance compared with FFR but 17% of the measurements were discordant. The diagnostic accuracy of the DFR was only 40% when the DFR was 0.88 to 0.90, suggesting that FFR may be useful in these arteries.

A comprehensive approach to prediction of fractional flow reserve from deep-learning-augmented model

The underuse of invasive fractional flow reserve (FFR) in clinical practice has motivated research towards non-invasive prediction of FFR. Although the non-invasive derivation of FFR (FFRCT) using computational fluid dynamics (CFD) principles has become a common practice, its clinical application has been limited due to the considerable time required for computation of resulting changes in haemodynamic conditions. An alternative to CFD technology is incorporating a neural network into the computational process to reduce the time necessary for running an effective model.In this study we propose a cascade of data-driven and physic-based neural networks (DP-NN) for predicting FFR (DL-FFRCT). The first network of cascade network DP-NN includes geometric features, and the second network includes physical features. We compare the differences between data-driven neural network (D-NN) and DP-NN for predicting FFR. The training and testing datasets were obtained by solving the three-dimensional incompressible Navier–Stokes equations. Coronary flow and geometric features were used as inputs to train D-NN. In DP-NN the training process involves first training a D-NN to output resting ΔP as one input feature to the DP-NN. Secondly, the physics-based microcirculatory resistance as another input feature to the DP-NN.Using clinically measured FFR as the “gold standard", we validated the computational accuracy of DL-FFRCT in 77 patients. Compared to D-NN, DP-NN improved the prediction of ΔP (R2 = 0.87 vs. R2 = 0.92). Statistical analysis demonstrated that the diagnostic accuracy of DL-FFRCT was not inferior to FFRCT (85.71 % vs. 88.3 %) and the computational time was reduced by a factor of approximately 3000 (4.26 s vs. 3.5 h).DP-NN represents a near real-time, interpretable, and highly accurate deep-learning network, which contributes to the development of high-performance computational methods for haemodynamics. We anticipate that DP-NN will enable near real-time prediction of DL-FFRCT in personalized narrow blood vessels and provide guidance for cardiovascular disease treatments.