Noninvasive Fractional Flow Reserve Derived From Coronary CT Angiography

Noninvasive and fast method of calculation for instantaneous wave-free ratio based on haemodynamics and deep learning

BACKGROUND AND OBJECTIVES

Instantaneous wave-free ratio (iFR) is a new invasive indicator of myocardial ischaemia, and its diagnostic performance is as good as the "gold standard" of myocardial ischaemia diagnosis: fractional flow reserve (FFR). iFR can be approximated by iFRCT, which is calculated based on noninvasive coronary CT angiography (CTA) images and computational fluid dynamics (CFD). However, the existing methods for calculating iFRCT fail to accurately simulate the resting state of the coronary artery, resulting in low computational accuracy. Furthermore, the use of CFD technology limits its computational efficiency, making it difficult to meet clinical application needs. The role of coronary microcirculatory resistance compensation suggests that microcirculatory resistance can be adaptively reduced to compensate for increases in coronary stenotic resistance, thereby maintaining stable myocardial perfusion in the resting state. It is therefore necessary to consider this compensation mechanism to establish a high-fidelity microcirculation resistance model in the resting state in line with human physiology, and so to achieve accurate calculation of iFRCT.

METHODS

In this study we successfully collected clinical data, such as FFR, in 205 stenotic vessels from 186 patients with coronary heart disease. A neural network model was established to predict coronary artery stenosis resistance. Based on the compensation mechanism of coronary microcirculation resistance, an iterative solution algorithm for microcirculation resistance in the resting state was developed. Combining the two methods, a simplified single-branch model combining coronary stenosis and microcirculation resistance was established, and the noninvasive and rapid numerical calculation of iFRCT was performed.

RESULTS

The results showed that the mean squared error (MSE) between the pressure drop predicted by the neural network value for the coronary artery stenosis model and the ground truth in the test set was 0.053 %, and correlation analysis proved that there was a good correlation between them (r = 0.99, p < 0.001). With reference to clinical diagnosis of myocardial ischaemia (using FFR as the gold standard), the diagnostic accuracy of the iFRCT calculation model for the 205 cases was 88.29 % (r = 0.71, p < 0.001), and the total calculation time was < 8 s.

CONCLUSIONS

The results of this study demonstrate the utility of a simplified single-branch model in an iFRCT calculation method based on haemodynamics and deep learning, which is important for noninvasive and rapid diagnosis of myocardial ischaemia.

Advanced CT measures of coronary artery disease with intermediate stenosis in patients with severe aortic valve stenosis

BACKGROUND

Coronary artery disease (CAD) and severe aortic valve stenosis (AS) frequently coexist. While pre-transcatheter aortic valve replacement (TAVR) computed tomography angiography (CTA) allows to rule out obstructive CAD, interpreting hemodynamic significance of intermediate stenoses is challenging. This study investigates the incremental value of CT-derived fractional flow reserve (CT-FFR), quantitative coronary plaque characteristics (e.g., stenosis degree, plaque volume, and composition), and peri-coronary adipose tissue (PCAT) density to detect hemodynamically significant lesions among those with AS and CAD.

MATERIALS AND METHODS

We included patients with severe AS and intermediate coronary lesions (20-80% diameter stenosis) who underwent pre-TAVR CTA and invasive coronary angiogram (ICA) with resting full-cycle ratio (RFR) assessment between 08/16 and 04/22. CTA image analysis included assessment of CT-FFR, quantitative coronary plaque analysis, and PCAT density. Coronary lesions with RFR ≤ 0.89 indicated hemodynamic significance as reference standard.

RESULTS

Overall, 87 patients (age 77.9 ± 7.4 years, 38% female) with 95 intermediate coronary artery lesions were included. CT-FFR showed good discriminatory capacity (area under receiver operator curve (AUC) = 0.89, 95% confidence interval (CI) 0.81-0.96, p < 0.001) to identify hemodynamically significant lesions, superior to anatomical assessment, plaque morphology, and PCAT density. Plaque composition and PCAT density did not differ between lesions with and without hemodynamic significance. Univariable and multivariable analyses revealed CT-FFR as the only predictor for functionally significant lesions (odds ratio 1.28 (95% CI 1.17-1.43), p < 0.001). Overall, CT-FFR ≤ 0.80 showed diagnostic accuracy, sensitivity, and specificity of 88.4% (95%CI 80.2-94.1), 78.5% (95%CI 63.2-89.7), and 96.2% (95%CI 87.0-99.5), respectively.

CONCLUSION

CT-FFR was superior to CT anatomical, plaque morphology, and PCAT assessment to detect functionally significant stenoses in patients with severe AS.

CLINICAL RELEVANCE STATEMENT

CT-derived fractional flow reserve in patients with severe aortic valve stenosis may be a useful tool for non-invasive hemodynamic assessment of intermediate coronary lesions, while CT anatomical, plaque morphology, and peri-coronary adipose tissue assessment have no incremental or additional benefit. These findings might help to reduce pre-transcatheter aortic valve replacement invasive coronary angiogram.

KEY POINTS

• Interpreting the hemodynamic significance of intermediate coronary stenoses is challenging in pre-transcatheter aortic valve replacement CT. • CT-derived fractional flow reserve (CT-FFR) has a good discriminatory capacity in the identification of hemodynamically significant coronary lesions. • CT-derived anatomical, plaque morphology, and peri-coronary adipose tissue assessment did not improve the diagnostic capability of CT-FFR in the hemodynamic assessment of intermediate coronary stenoses.

Prognostic value of a novel artificial intelligence-based coronary CTA-derived ischemia algorithm among patients with normal or abnormal myocardial perfusion

BACKGROUND

Among patients with obstructive coronary artery disease (CAD) on coronary computed tomography angiography (CTA), downstream positron emission tomography (PET) perfusion imaging can be performed to assess the presence of myocardial ischemia. A novel artificial-intelligence-guided quantitative computed tomography ischemia algorithm (AI-QCTischemia) aims to predict ischemia directly from coronary CTA images. We aimed to study the prognostic value of AI-QCTischemia among patients with obstructive CAD on coronary CTA and normal or abnormal downstream PET perfusion.

METHODS

AI-QCTischemia was calculated by blinded analysts among patients from the retrospective coronary CTA cohort at Turku University Hospital, Finland, with obstructive CAD on initial visual reading (diameter stenosis ≥50%) being referred for downstream 15O-H2O-PET adenosine stress perfusion imaging. All coronary arteries with their side branches were assessed by AI-QCTischemia. Absolute stress myocardial blood flow ≤2.3 ​ml/g/min in ≥2 adjacent segments was considered abnormal. The primary endpoint was death, myocardial infarction, or unstable angina pectoris. The median follow-up was 6.2 [IQR 4.4-8.3] years.

RESULTS

662 of 768 (86%) patients had conclusive AI-QCTischemia result. In patients with normal 15O-H2O-PET perfusion, an abnormal AI-QCTischemia result (n ​= ​147/331) vs. normal AI-QCTischemia result (n ​= ​184/331) was associated with a significantly higher crude and adjusted rates of the primary endpoint (adjusted HR 2.47, 95% CI 1.17-5.21, p ​= ​0.018). This did not pertain to patients with abnormal 15O-H2O-PET perfusion (abnormal AI-QCTischemia result (n ​= ​269/331) vs. normal AI-QCTischemia result (n ​= ​62/331); adjusted HR 1.09, 95% CI 0.58-2.02, p ​= ​0.794) (p-interaction ​= ​0.039).

CONCLUSION

Among patients with obstructive CAD on coronary CTA referred for downstream 15O-H2O-PET perfusion imaging, AI-QCTischemia showed incremental prognostic value among patients with preserved perfusion by 15O-H2O-PET imaging, but not among those with reduced perfusion.

Impact of minimal lumen segmentation uncertainty on patient-specific coronary simulations: A look at FFRCT

We examined the effect of minimal lumen segmentation uncertainty on Fractional Flow Reserve obtained from Coronary Computed Tomography AngiographyFFR CT . A total of 14 patient-specific coronary models with different stenosis locations and degrees of severity were enrolled in this study. The optimal segmented coronary lumens were disturbed using intra± 6 % and inter-operator± 15 % variations on the segmentation threshold.FFR CT was evaluated in each case by 3D-OD CFD simulations. The findings suggest that the sensitivity ofFFR CT to this type of uncertainty increases distally and with the stenosis severity. Cases with moderate or severe distal coronary lesions should undergo either exact and thorough segmentation operations or invasive FFR measurements, particularly if theFFR CT is close to the cutoff (0.80). Therefore, we conclude that it is crucial to consider the lesion's location and degree of severity when evaluatingFFR CT results.

A comparative analysis of deep learning-based location-adaptive threshold method software against other commercially available software

Automatic segmentation of the coronary artery using coronary computed tomography angiography (CCTA) images can facilitate several analyses related to coronary artery disease (CAD). Accurate segmentation of the lumen or plaque region is one of the most important factors. This study aimed to analyze the performance of the coronary artery segmentation of a software platform with a deep learning-based location-adaptive threshold method (DL-LATM) against commercially available software platforms using CCTA. The dataset from intravascular ultrasound (IVUS) of 26 vessel segments from 19 patients was used as the gold standard to evaluate the performance of each software platform. Statistical analyses (Pearson correlation coefficient [PCC], intraclass correlation coefficient [ICC], and Bland-Altman plot) were conducted for the lumen or plaque parameters by comparing the dataset of each software platform with IVUS. The software platform with DL-LATM showed the bias closest to zero for detecting lumen volume (mean difference = -9.1 mm3, 95% confidence interval [CI] = -18.6 to 0.4 mm3) or area (mean difference = -0.72 mm2, 95% CI = -0.80 to -0.64 mm2) with the highest PCC and ICC. Moreover, lumen or plaque area in the stenotic region was analyzed. The software platform with DL-LATM showed the bias closest to zero for detecting lumen (mean difference = -0.07 mm2, 95% CI = -0.16 to 0.02 mm2) or plaque area (mean difference = 1.70 mm2, 95% CI = 1.37 to 2.03 mm2) in the stenotic region with significantly higher correlation coefficient than other commercially available software platforms (p < 0.001). The result shows that the software platform with DL-LATM has the potential to serve as an aiding system for CAD evaluation.

Myocardial infarction detection method based on the continuous T-wave area feature and multi-lead-fusion deep features

Objective.Myocardial infarction (MI) is one of the most threatening cardiovascular diseases. This paper aims to explore a method for using an algorithm to autonomously classify MI based on the electrocardiogram (ECG).Approach.A detection method of MI that fuses continuous T-wave area (C_TWA) feature and ECG deep features is proposed. This method consists of three main parts: (1) The onset of MI is often accompanied by changes in the shape of the T-wave in the ECG, thus the area of the T-wave displayed on different heartbeats will be quite different. The adaptive sliding window method is used to detect the start and end of the T-wave, and calculate the C_TWA on the same ECG record. Additionally, the coefficient of variation of C_TWA is defined as the C_TWA feature of the ECG. (2) The multi lead fusion convolutional neural network was implemented to extract the deep features of the ECG. (3) The C_TWA feature and deep features of the ECG were fused by soft attention, and then inputted into the multi-layer perceptron to obtain the detection result.Main results.According to the inter-patient paradigm, the proposed method reached a 97.67% accuracy, 96.59% precision, and 98.96% recall on the PTB dataset, as well as reached 93.15% accuracy, 93.20% precision, and 95.14% recall on the clinical dataset.Significance.This method accurately extracts the feature of the C_TWA, and combines the deep features of the signal, thereby improving the detection accuracy and achieving favorable results on clinical datasets.

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

BACKGROUND

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).

METHODS AND RESULTS

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%.

CONCLUSIONS

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.

Deep-learning-based real-time individualization for reduce-order haemodynamic model

The reduced-order lumped parameter model (LPM) has great computational efficiency in real-time numerical simulations of haemodynamics but is limited by the accuracy of patient-specific computation. This study proposed a method to achieve the individual LPM modeling with high accuracy to improve the practical clinical applicability of LPM. Clinical data was collected from two medical centres comprising haemodynamic indicators from 323 individuals, including brachial artery pressure waveforms, cardiac output data, and internal carotid artery flow waveforms. The data were expanded to 5000 synthesised cases that all fell within the physiological range of each indicator. LPM of the human blood circulation system was established. A double-path neural network (DPNN) was designed to input the waveforms of each haemodynamic indicator and their key features and then output the individual parameters of the LPM, which was labelled using a conventional optimization algorithm. Clinically collected data from the other 100 cases were used as the test set to verify the accuracy of the individual LPM parameters predicted by DPNN. The results show that DPNN provided good convergence in the training process. In the test set, compared with clinical measurements, the mean differences between each haemodynamic indicator and the estimate calculated by the individual LPM based on the DPNN were about 10 %. Furthermore, DPNN prediction only takes 4 s for 100 cases. The DPNN proposed in this study permits real-time and accurate individualization of LPM's. When facing medical issues involving haemodynamics, it lays the foundation for patient-specific numerical simulation, which may be beneficial for potential clinical application.

Prognostic value of a novel artificial intelligence-based coronary computed tomography angiography-derived ischaemia algorithm for patients with suspected coronary artery disease

AIMS

Coronary computed tomography angiography (CTA) imaging is used to diagnose patients with suspected coronary artery disease (CAD). A novel artificial intelligence-guided quantitative computed tomography ischaemia algorithm (AI-QCTischaemia) aims to identify myocardial ischaemia directly from CTA images and may be helpful to improve risk stratification. The aims were to investigate (i) the prognostic value of AI-QCTischaemia amongst symptomatic patients with suspected CAD entering diagnostic imaging with coronary CTA and (ii) the prognostic value of AI-QCTischaemia separately amongst patients with no/non-obstructive CAD (≤50% visual diameter stenosis) and obstructive CAD (>50% visual diameter stenosis).

METHODS AND RESULTS

For this cohort study, AI-QCTischaemia was calculated by blinded analysts amongst patients with suspected CAD undergoing coronary CTA. The primary endpoint was the composite of death, myocardial infarction (MI), or unstable angina pectoris (uAP) (median follow-up 6.9 years). A total of 1880/2271 (83%) patients had conclusive AI-QCTischaemia result. Patients with an abnormal AI-QCTischaemia result (n = 509/1880) vs. patients with a normal AI-QCTischaemia result (n = 1371/1880) had significantly higher crude and adjusted rates of the primary endpoint [adjusted hazard ratio (HRadj) 1.96, 95% confidence interval (CI) 1.46-2.63, P < 0.001; covariates: age/sex/hypertension/diabetes/smoking/typical angina]. An abnormal AI-QCTischaemia result was associated with significantly higher crude and adjusted rates of the primary endpoint amongst patients with no/non-obstructive CAD (n = 1373/1847) (HRadj 1.81, 95% CI 1.09-3.00, P = 0.022), but not amongst those with obstructive CAD (n = 474/1847) (HRadj 1.26, 95% CI 0.75-2.12, P = 0.386) (P-interaction = 0.032).

CONCLUSION

Amongst patients with suspected CAD, an abnormal AI-QCTischaemia result was associated with a two-fold increased adjusted rate of long-term death, MI, or uAP. AI-QCTischaemia may be useful to improve risk stratification, especially amongst patients with no/non-obstructive CAD on coronary CTA.

Coronary computed tomography angiographic detection of in-stent restenosis via deep learning reconstruction: a feasibility study

OBJECTIVES

Evaluation of in-stent restenosis (ISR), especially for small stents, remains challenging during computed tomography (CT) angiography. We used deep learning reconstruction to quantify stent strut thickness and lumen vessel diameter at the stent and compared it with values obtained using conventional reconstruction strategies.

METHODS

We examined 166 stents in 85 consecutive patients who underwent CT and invasive coronary angiography (ICA) within 3 months of each other from 2019-2021 after percutaneous coronary intervention with coronary stent placement. The presence of ISR was defined as percent diameter stenosis ≥ 50% on ICA. We compared a super-resolution deep learning reconstruction, Precise IQ Engine (PIQE), and a model-based iterative reconstruction, Forward projected model-based Iterative Reconstruction SoluTion (FIRST). All images were reconstructed using PIQE and FIRST and assessed by two blinded cardiovascular radiographers.

RESULTS

PIQE had a larger full width at half maximum of the lumen and smaller strut than FIRST. The image quality score in PIQE was higher than that in FIRST (4.2 ± 1.1 versus 2.7 ± 1.2, p < 0.05). In addition, the specificity and accuracy of ISR detection were better in PIQE than in FIRST (p < 0.05 for both), with particularly pronounced differences for stent diameters < 3.0 mm.

CONCLUSION

PIQE provides superior image quality and diagnostic accuracy for ISR, even with stents measuring < 3.0 mm in diameter.

CLINICAL RELEVANCE STATEMENT

With improvements in the diagnostic accuracy of in-stent stenosis, CT angiography could become a gatekeeper for ICA in post-stenting cases, obviating ICA in many patients after recent stenting with infrequent ISR and allowing non-invasive ISR detection in the late phase.

KEY POINTS

• Despite CT technology advancements, evaluating in-stent stenosis severity, especially in small-diameter stents, remains challenging. • Compared with conventional methods, the Precise IQ Engine uses deep learning to improve spatial resolution. • Improved diagnostic accuracy of CT angiography helps avoid invasive coronary angiography after coronary artery stenting.

Optimal diagnostic approach for using CT-derived quantitative flow ratio in patients with stenosis on coronary computed tomography angiography

BACKGROUND

Coronary computed tomography angiography (CCTA)-derived quantitative flow ratio (CT-QFR) is an on-site non-invasive technique estimating invasive fractional flow reserve (FFR). This study assesses the diagnostic performance of using most distal CT-QFR versus lesion-specific CT-QFR approach for identifying hemodynamically obstructive coronary artery disease (CAD).

METHODS

Prospectively enrolled de novo chest pain patients (n ​= ​445) with ≥50 ​% visual diameter stenosis on CCTA were referred for invasive evaluation. On-site CT-QFR was analyzed post-hoc blinded to angiographic data and obtained as both most distal (MD-QFR) and lesion-specific CT-QFR (LS-QFR). Abnormal CT-QFR was defined as ≤0.80. Hemodynamically obstructive CAD was defined as invasive FFR ≤0.80 or ≥70 ​% diameter stenosis by 3D-quantitative coronary angiography.

RESULTS

In total 404/445 patients had paired CT-QFR and invasive analyses of whom 149/404 (37 ​%) had hemodynamically obstructive CAD. MD-QFR and LS-QFR classified 188 (47 ​%) and 165 (41 ​%) patients as abnormal, respectively. Areas under the receiver-operating characteristic curve for MD-QFR was 0.83 vs. 0.85 for LS-QFR, p ​= ​0.01. Sensitivities for MD-QFR and LS-QFR were 80 ​% (95%CI: 73-86) vs. 77 ​% (95%CI: 69-83), p ​= ​0.03, respectively, and specificities were 73 ​% (95%CI: 67-78) vs. 80 ​% (95%CI: 75-85), p ​< ​0.01, respectively. Positive predictive values for MD-QFR and LS-QFR were 63 ​% vs. 69 ​%, p ​< ​0.01, respectively, and negative predictive values for MD-QFR and LS-QFR were 86 ​% vs. 85 ​%, p ​= ​0.39, respectively).

CONCLUSION

Using a lesion-specific CT-QFR approach has superior discrimination of hemodynamically obstructive CAD compared to a most distal CT-QFR approach. CT-QFR identified most cases of hemodynamically obstructive CAD while a normal CT-QFR excluded hemodynamically obstructive CAD in the majority of patients.

Impact of vessel morphology on CT-derived fractional-flow-reserve in non-obstructive coronary artery disease in right coronary artery

OBJECTIVES

Computed tomography (CT)-derived fractional flow reserve (FFRCT) decreases continuously from proximal to distal segments of the vessel due to the influence of various factors even in non-obstructive coronary artery disease (NOCAD). It is known that FFRCT is dependent on vessel-length, but the relationship with other vessel morphologies remains to be explained.

PURPOSE

To investigate morphological aspects of the vessels that influence FFRCT in NOCAD in the right coronary artery (RCA).

METHODS

A total of 443 patients who underwent both FFRCT and invasive coronary angiography, with < 50% RCA stenosis, were evaluated. Enrolled RCA vessels were classified into two groups according to distal FFRCT: FFRCT ≤ 0.80 (n = 60) and FFRCT > 0.80 (n = 383). Vessel morphology (vessel length, lumen diameter, lumen volume, and plaque volume) and left-ventricular mass were assessed. The ratio of lumen volume and vessel length was defined as V/L ratio.

RESULTS

Whereas vessel-length was almost the same between FFRCT ≤ 0.80 and > 0.80, lumen volume and V/L ratio were significantly lower in FFRCT ≤ 0.80. Distal FFRCT correlated with plaque-related parameters (low-attenuation plaque, intermediate-attenuation plaque, and calcified plaque) and vessel-related parameters (proximal and distal vessel diameter, vessel length, lumen volume, and V/L ratio). Among all vessel-related parameters, V/L ratio showed the highest correlation with distal FFRCT (r = 0.61, p < 0.0001). Multivariable analysis showed that calcified plaque volume was the strongest predictor of distal FFRCT, followed by V/L ratio (β-coefficient = 0.48, p = 0.03). V/L ratio was the strongest predictor of a distal FFRCT ≤ 0.80 (cut-off 8.1 mm3/mm, AUC 0.88, sensitivity 90.0%, specificity 76.7%, 95% CI 0.84-0.93, p < 0.0001).

CONCLUSIONS

Our study suggests that V/L ratio can be a measure to predict subclinical coronary perfusion disturbance.

CLINICAL RELEVANCE STATEMENT

A novel marker of the ratio of lumen volume to vessel length (V/L ratio) is the strongest predictor of a distal CT-derived fractional flow reserve (FFRCT) and may have the potential to improve the diagnostic accuracy of FFRCT.

KEY POINTS

• Physiological FFRCT decline depends not only on vessel length but also on the lumen volume in non-obstructive coronary artery disease in the right coronary artery. • FFRCT correlates with plaque-related parameters (low-attenuation plaque, intermediate-attenuation plaque, and calcified plaque) and vessel-related parameters (proximal and distal vessel diameter, vessel length, lumen volume, and V/L ratio). • Of vessel-related parameters, V/L ratio is the strongest predictor of a distal FFRCT and an optimal cut-off value of 8.1 mm3/mm.

Feasibility and Utility of Anatomical and Physiological Evaluation of Coronary Disease With Cardiac CT in Severe Aortic Stenosis (FUTURE-AS Registry): Rationale and Design

Background

Coronary artery disease (CAD) in patients with severe aortic stenosis (AS) is common and may be associated with worse outcomes. Computed tomography coronary angiography (CTCA) and fractional flow reserve derived from computed tomography (FFRCT) are tools for comprehensive coronary assessment. The utility and safety of CTCA and FFRCT in the work-up for transcatheter aortic valve replacement (TAVR) is not established, especially in an evolving landscape that involves younger TAVR patients. The FUTURE-AS Registry will assess the utility and safety of cardiac-optimized CTCA and FFRCT to evaluate CAD and guide referral for downstream invasive coronary angiography (ICA) in patients with severe AS being considered for TAVR.

Methods

FUTURE-AS is an international, prospective, multicenter registry of patients with severe AS referred for TAVR being assessed for CAD with CTCA and FFRCT. The primary end point is the per-patient sensitivity and negative predictive value of CTCA and FFRCT for identifying anatomical and physiologically significant CAD compared to ICA and invasive FFR. The safety end point is the incidence of symptomatic hypotension or bradycardia requiring intervention following the administration of nitroglycerin or β-blocker medications. Feasibility end points include the incidence of noninterpretable CTCA scans and CTCA scans not adequate for FFR analysis. Other utility end points include specificity, positive predictive value, and accuracy of CTCA and FFRCT. Lastly, the potential of a CTCA and FFRCT guided strategy to defer pre-TAVR ICA will be assessed.

Conclusions

FUTURE-AS will characterize the utility, safety, and feasibility of CTCA and FFRCT for coronary assessment pre-TAVR.

Computed Tomography-Derived Physiology Assessment: State-of-the-Art Review

Coronary computed tomography angiography (CCTA) and CCTA-derived fractional flow reserve (FFRCT) are the best non-invasive techniques to assess coronary artery disease (CAD) and myocardial ischemia. Advances in these technologies allow a paradigm shift to the use of CCTA and FFRCT for advanced plaque characterization and planning myocardial revascularization.

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.

Distal-vessel fractional flow reserve by computed tomography to monitor epicardial coronary artery disease

AIMS

Coronary computed tomography angiography (CTA) and fractional flow reserve by computed tomography (FFR-CT) are increasingly utilized to characterize coronary artery disease (CAD). We evaluated the feasibility of distal-vessel FFR-CT as an integrated measure of epicardial CAD that can be followed serially, assessed the CTA parameters that correlate with distal-vessel FFR-CT, and determined the combination of clinical and CTA parameters that best predict distal-vessel FFR-CT and distal-vessel FFR-CT changes.

METHODS AND RESULTS

Patients (n = 71) who underwent serial CTA scans at ≥2 years interval (median = 5.2 years) over a 14-year period were included in this retrospective study. Coronary arteries were analysed blindly using artificial intelligence-enabled quantitative coronary CTA. Two investigators jointly determined the anatomic location and corresponding distal-vessel FFR-CT values at CT1 and CT2. A total of 45.3% had no significant change, 27.8% an improvement, and 26.9% a worsening in distal-vessel FFR-CT at CT2. Stepwise multiple logistic regression analysis identified a four-parameter model consisting of stenosis diameter ratio, lumen volume, low density plaque volume, and age, that best predicted distal-vessel FFR-CT ≤ 0.80 with an area under the curve (AUC) = 0.820 at CT1 and AUC = 0.799 at CT2. Improvement of distal-vessel FFR-CT was captured by a decrease in high-risk plaque and increases in lumen volume and remodelling index (AUC = 0.865), whereas increases in stenosis diameter ratio, medium density calcified plaque volume, and total cholesterol presaged worsening of distal-vessel FFR-CT (AUC = 0.707).

CONCLUSION

Distal-vessel FFR-CT permits the integrative assessment of epicardial atherosclerotic plaque burden in a vessel-specific manner and can be followed serially to determine changes in global CAD.

Non-invasive fractional flow reserve derived from reduced-order coronary model and machine learning prediction of stenosis flow resistance

BACKGROUND AND OBJECTIVE

Recently, computational fluid dynamics enables the non-invasive calculation of fractional flow reserve (FFR) based on 3D coronary model, but it is time-consuming. Currently, machine learning technique has emerged as an efficient and reliable approach for prediction, which allows saving a lot of analysis time. This study aimed at developing a simplified FFR prediction model for rapid and accurate assessment of functional significance of stenosis.

METHODS

A reduced-order lumped parameter model (LPM) of coronary system and cardiovascular system was constructed for rapidly simulating coronary flow, in which a machine learning model was embedded for accurately predicting stenosis flow resistance at a given flow from anatomical features of stenosis. Importantly, the LPM was personalized in both structures and parameters according to coronary geometries from computed tomography angiography and physiological measurements such as blood pressure and cardiac output for personalized simulations of coronary pressure and flow. Coronary lesions with invasive FFR ≤ 0.80 were defined as hemodynamically significant.

RESULTS

A total of 91 patients (93 lesions) who underwent invasive FFR were involved in FFR derived from machine learning (FFRML) calculation. Of the 93 lesions, 27 lesions (29.0%) showed lesion-specific ischemia. The average time of FFRML simulation was about 10 min. On a per-vessel basis, the FFRML and FFR were significantly correlated (r = 0.86, p < 0.001). The diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 91.4%, 92.6%, 90.9%, 80.6% and 96.8%, respectively. The area under the receiver-operating characteristic curve of FFRML was 0.984.

CONCLUSION

In this selected cohort of patients, the FFRML improves the computational efficiency and ensures the accuracy. The favorable performance of FFRML approach greatly facilitates its potential application in detecting hemodynamically significant coronary stenosis in future routine clinical practice.

Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study

Objective

To develop and validate a hybrid model incorporating CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics signatures for predicting progression of white matter hyperintensity (WMH).

Methods

A total of 226 patients who received coronary computer tomography angiography (CCTA) and brain magnetic resonance imaging from two hospitals were divided into a training set (n = 116), an internal validation set (n = 30), and an external validation set (n = 80). Patients who experienced progression of WMH were identified from subsequent MRI results. We calculated CT-FFR and pFAI from CCTA images using semi-automated software, and segmented the pericoronary adipose tissue (PCAT) and myocardial ROI. A total of 1,073 features were extracted from each ROI, and were then refined by Elastic Net Regression. Firstly, different machine learning algorithms (Logistic Regression [LR], Support Vector Machine [SVM], Random Forest [RF], k-nearest neighbor [KNN] and eXtreme Gradient Gradient Boosting Machine [XGBoost]) were used to evaluate the effectiveness of radiomics signatures for predicting WMH progression. Then, the optimal machine learning algorithm was used to compare the predictive performance of individual and hybrid models based on independent risk factors of WMH progression. Receiver operating characteristic (ROC) curve analysis, calibration and decision curve analysis were used to evaluate predictive performance and clinical value of the different models.

Results

CT-FFR, pFAI, and radiomics signatures were independent predictors of WMH progression. Based on the machine learning algorithms, the PCAT signatures led to slightly better predictions than the myocardial signatures and showed the highest AUC value in the XGBoost algorithm for predicting WMH progression (AUC: 0.731 [95% CI: 0.603-0.838] vs.0.711 [95% CI: 0.584-0.822]). In addition, pFAI provided better predictions than CT-FFR (AUC: 0.762 [95% CI: 0.651-0.863] vs. 0.682 [95% CI: 0.547-0.799]). A hybrid model that combined CT-FFR, pFAI, and two radiomics signatures provided the best predictions of WMH progression [AUC: 0.893 (95%CI: 0.815-0.956)].

Conclusion

pFAI was more effective than CT-FFR, and PCAT signatures were more effective than myocardial signatures in predicting WMH progression. A hybrid model that combines pFAI, CT-FFR, and two radiomics signatures has potential use for identifying WMH progression.

Predicting progression of white matter hyperintensity using coronary artery calcium score based on coronary CT angiography-feasibility and accuracy

Objective

Coronary artery disease (CAD) usually coexists with subclinical cerebrovascular diseases given the systematic nature of atherosclerosis. In this study, our objective was to predict the progression of white matter hyperintensity (WMH) and find its risk factors in CAD patients using the coronary artery calcium (CAC) score. We also investigated the relationship between the CAC score and the WMH volume in different brain regions.

Methods

We evaluated 137 CAD patients with WMH who underwent coronary computed tomography angiography (CCTA) and two magnetic resonance imaging (MRI) scans from March 2018 to February 2023. Patients were categorized into progressive (n = 66) and nonprogressive groups (n = 71) by the change in WMH volume from the first to the second MRI. We collected demographic, clinical, and imaging data for analysis. Independent risk factors for WMH progression were identified using logistic regression. Three models predicting WMH progression were developed and assessed. Finally, patients were divided into groups based on their total CAC score (0 to <100, 100 to 400, and > 400) to compare their WMH changes in nine brain regions.

Results

Alcohol abuse, maximum pericoronary fat attenuation index (pFAI), CT-fractional flow reserve (CT-FFR), and CAC risk grade independently predicted WMH progression (p < 0.05). The logistic regression model with all four variables performed best (training: AUC = 0.878, 95% CI: 0.790, 0.938; validation: AUC = 0.845, 95% CI: 0.734, 0.953). An increased CAC risk grade came with significantly higher WMH volume in the total brain, corpus callosum, and frontal, parietal and occipital lobes (p < 0.05).

Conclusion

This study demonstrated the application of the CCTA-derived CAC score to predict WMH progression in elderly people (≥60 years) with CAD.

Pancreatic duct pressure: A review of technical aspects and clinical significance

Pancreatic duct pressure (PDP) dynamics comprise an intricately modulated system that helps maintain homeostasis of pancreatic function. It is affected by various factors, including the rate of pancreatic fluid secretion, patency of the ductal system, sphincter of Oddi function, and pancreatic fluid characteristics. Disease states such as acute and chronic pancreatitis can alter the normal PDP dynamics. Ductal hypertension or increased PDP is suspected to be involved in the pathogenesis of pancreatic pain, endocrine and exocrine pancreatic insufficiency, and recurrent pancreatitis. This review provides a comprehensive appraisal of the available literature on PDP, including the methods used in the measurement and clinical implications of elevated PDP.

Diagnostic performance of a novel automated CT-derived FFR technology in detecting hemodynamically significant coronary artery stenoses: A multicenter trial in China

BACKGROUND AND AIMS

Computed tomography-derived fractional flow reserve (CT-derived FFR) algorithms have emerged as promising noninvasive methods for identifying hemodynamically significant coronary artery disease (CAD). However, its broad adaption is limited by the complex workflow, slow processing, and supercomputer requirement. Therefore, CT-derived FFR solutions capable of producing fast and accurate results could help deliver time-sensitive results rapidly and potentially alter patient management. The current study aimed to determine the diagnostic performance of a novel CT-derived FFR algorithm, esFFR, on patients with CAD was evaluated.

METHODS

329 patients from 6 medical centers in China were included in this prospective study. CT-derived FFR calculations were performed on 350 vessels using the esFFR algorithm using patients' presenting coronary computed tomography angiography (CCTA) images, and results and processing speed were recorded. Using invasive FFR measurements from direct coronary angiography as the reference standard, the diagnostic performance of esFFR and CCTA in detecting hemodynamically significant lesions were compared. Post-hoc analyses were performed for patients with calcified lesions or stenoses within the CT-derived FFR diagnostic "gray zone."

RESULTS

The esFFR values correlated well with invasive FFR. The sensitivity, specificity, accuracy, positive and negative predictive value for esFFR were all above 90%. The overall performance of esFFR was superior to CCTA. Coronary calcification had minimal effects on esFFR's diagnostic performance. It also maintained 85% of diagnostic accuracy for "gray zone" lesions, which historically was <50%. The average esFFR processing speed was 4.6 ± 1.3 minutes.

CONCLUSIONS

The current study demonstrated esFFR had high diagnostic efficacy and fast processing speed in identifying hemodynamically significant CAD.

Left and right coronary artery blood flow distribution method based on dominant type

The purpose of the current study was to investigate the effects of left/right coronary artery flow distribution on calculation of fractional flow reserve derived from coronary computed tomography angiography (FFRct) in different dominant types. First, 195 patients were collected to count the distribution ratios of the three categories: right dominance (RD), balanced dominance (BD), and left dominance (LD). Ratios of diameters of the left/right coronary arteries (DLCA :DRCA ) of the three types were calculated and used to represent the ratio of flow distribution (QLCA :QRCA ) in the dominant type method. The other method was known as the fixed ratio method (QLCA :QRCA  = 6:4). Second, a total of 73 patients with coronary artery disease (CAD) were enrolled for numerical calculation. A 0D/3D geometric multiscale model was used for the numerical simulation of FFR and the results of the fixed ratio method and the dominant type method were recorded as F-FFRct and D-FFRct. Lastly, invasive FFR(clinic-FFR)was used as a standard to evaluate the consistency and diagnostic performance of F-FFRct and D-FFRct. Corresponding flow distributions for the dominant type method were QLCA :QRCA  = 5:5 for RD, QLCA :QRCA  = 5.5:4.5 for BD, and QLCA :QRCA  = 6:4 for LD. D-FFRct showed a better correlation than F-FFRct (r = 0.85 vs. r = 0.81, both p < .001); the AUC (95%CI) were 0.974 (0.906-0.997, p < .0001) and 0.960 (0.886-0.992, p < .0001). Accuracy, specificity, sensitivity, positive predictive value (PPV) and negative predictive values (NPV) for D-FFRct and F-FFRct were 94.52%, 93.75%, 94.74%, 83.33%, 98.18% and 90.41%, 87.50%, 91.23%, 73.68%, 96.30%, respectively. Overall, the left/right coronary artery flow distribution was affected by the dominant type and the dominant type method was superior to the fixed ratio method in detecting coronary ischemic lesions.

Noninvasive computed tomography derived fractional flow reserve simulation based on microvascular tree model reconstruction

To establish a novel method for noninvasive computed tomography derived fractional flow reserve (CT-FFR) simulation based on microvascular tree model reconstruction and to evaluate the feasibility and diagnostic performance of the novel method in coronary artery disease compared with invasive fractional flow reserve (FFR). Twenty patients (20 vessels) who underwent coronary computed tomography angiography (CCTA) and invasive FFR were retrospectively studied. The anatomic epicardial coronary artery model was reconstructed based on CCTA image, and the microvascular tree model was simulated based on patient-specific anatomical structures and physiological principles. Numerical simulation was subsequently performed using the CFD method with full consideration of the variation of viscosity in microvascular. Two patients with the FFR value of .80 were selected for adjusting the parameters of the model, while the remaining 18 patients were selected as a validation cohort. After simulation, CT-FFR was compared with invasive FFR with a threshold of .80. Eleven (55%) patients had an abnormal FFR that was less than or equal to .80. Compared with invasive FFR, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of CT-FFR with an optimal threshold of .80 were 100%, 77.8%, 81.8%, 100%, 88.89%, respectively. There were a good correlation and consistency between CT-FFR and invasive FFR. Time per patient of CT-FFR analysis was less than 15 min. CT-FFR based on microvascular tree model reconstruction is feasible with good diagnostic performance. It requires a short processing time with excellent accuracy. Large multicenter prospective studies are required for further demonstrating the diagnostic performance of this novel model in myocardium ischemia evaluation.

Postendoscopic Retrograde Cholangiopancreatography Pancreatitis Pathophysiology and Prevention

Endoscopic retrograde cholangiopancreatography (ERCP) is an essential procedure for the management of pancreato-biliary disorders. Pancreatitis remains the most frequent complication of the ERCP procedure, and it is, therefore, necessary to recognize the pathophysiology and risk factors contributing to the development of pancreatitis and understand the methods to prevent this complication.

Coronary computed tomography angiography imaging features combined with computed tomography-fractional flow reserve, pericoronary fat attenuation index, and radiomics for the prediction of myocardial ischemia

BACKGROUND

This study aimed to predict myocardial ischemia (MIS) by constructing models with imaging features, CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics based on coronary computed tomography angiography (CCTA).

METHODS AND RESULTS

This study included 96 patients who underwent CCTA and single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI). According to SPECT-MPI results, there were 72 vessels with MIS in corresponding supply area and 105 vessels with no-MIS. The conventional model [lesion length (LL), MDS (maximum stenosis diameter × 100% / reference vessel diameter), MAS (maximum stenosis area × 100% / reference vessel area) and CT value], radiomics model (radiomics features), and multi-faceted model (all features) were constructed using support vector machine. Conventional and radiomics models showed similar predictive efficacy [AUC: 0.76, CI 0.62-0.90 vs. 0.74, CI 0.61-0.88; p > 0.05]. Adding pFAI to the conventional model showed better predictive efficacy than adding CT-FFR (AUC: 0.88, CI 0.79-0.97 vs. 0.80, CI 0.68-0.92; p < 0.05). Compared with conventional and radiomics model, the multi-faceted model showed the highest predictive efficacy (AUC: 0.92, CI 0.82-0.98, p < 0.05).

CONCLUSION

pFAI is more effective for predicting MIS than CT-FFR. A multi-faceted model combining imaging features, CT-FFR, pFAI, and radiomics is a potential diagnostic tool for MIS.

High-Speed On-Site Deep Learning-Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard

BACKGROUND. Estimation of fractional flow reserve from coronary CTA (FFR-CT) is an established method of assessing the hemodynamic significance of coronary lesions. However, clinical implementation has progressed slowly, partly because of off-site data transfer with long turnaround times for results. OBJECTIVE. The purpose of this study was to evaluate the diagnostic performance of FFR-CT computed on-site with a high-speed deep learning-based algorithm with invasive hemodynamic indexes as the reference standard. METHODS. This retrospective study included 59 patients (46 men, 13 women; mean age, 66.5 ± 10.2 years) who underwent coronary CTA (including calcium scoring) followed within 90 days by invasive angiography with invasive fractional flow reserve (FFR) and/or instantaneous wave-free ratio measurements from December 2014 to October 2021. Coronary artery lesions were considered to have hemodynamically significant stenosis in the presence of invasive FFR of 0.80 or less and/or instantaneous wave-free ratio of 0.89 or less. A single cardiologist evaluated the CTA images using an on-site deep learning-based semiautomated algorithm entailing a 3D computational flow dynamics model to determine FFR-CT for coronary artery lesions detected with invasive angiography. Time for FFR-CT analysis was recorded. FFR-CT analysis was repeated by the same cardiologist in 26 randomly selected examinations and by a different cardiologist in 45 randomly selected examinations. Diagnostic performance and agreement were assessed. RESULTS. A total of 74 lesions were identified with invasive angiography. FFR-CT and invasive FFR had strong correlation (r = 0.81) and, in Bland-Altman analysis, bias of 0.01 and 95% limits of agreement of -0.13 to 0.15. FFR-CT had AUC for hemodynamically significant stenosis of 0.975. At a cutoff of 0.80 or less, FFR-CT had 95.9% accuracy, 93.5% sensitivity, and 97.7% specificity. In 39 lesions with severe calcifications (≥ 400 Agatston units), FFR-CT had AUC of 0.991 and at a cutoff of 0.80, 94.7% sensitivity, 95.0% specificity, and 94.9% accuracy. Mean analysis time per patient was 7 minutes 54 seconds. Intraobserver agreement (intraclass correlation coefficient, 0.85; bias, -0.01; 95% limits of agreement, -0.12 and 0.10) and interobserver agreement (intraclass correlation coefficient, 0.94; bias, -0.01; 95% limits of agreement, -0.08 and 0.07) were good to excellent. CONCLUSION. A high-speed on-site deep learning-based FFR-CT algorithm had excellent diagnostic performance for hemodynamically significant stenosis with high reproducibility. CLINICAL IMPACT. The algorithm should facilitate implementation of FFR-CT technology into routine clinical practice.

Application of Patient-Specific Computational Fluid Dynamics in Anomalous Aortic Origin of Coronary Artery: A Systematic Review

Anomalous aortic origin of a coronary artery (AAOCA) is a rare congenital heart condition with fixed and dynamic stenotic elements, potentially causing ischemia. Invasive coronary angiography under stress is the established method for assessing hemodynamics in AAOCA, yet it is costly, technically intricate, and uncomfortable. Computational fluid dynamics (CFD) simulations offer a noninvasive alternative for patient-specific hemodynamic analysis in AAOCA. This systematic review examines the role of CFD simulations in AAOCA, encompassing patient-specific modeling, noninvasive imaging-based boundary conditions, and flow characteristics. Screening articles using AAOCA and CFD-related terms prior to February 2023 yielded 19 publications, covering 370 patients. Over the past four years, 12 (63%) publications (259 patients) employed dedicated CFD models, whereas 7 (37%) publications (111 patients) used general-purpose CFD models. Dedicated CFD models were validated for fixed stenosis but lacked dynamic component representation. General-purpose CFD models exhibited variability and limitations, with fluid-solid interaction models showing promise. Interest in CFD modeling of AAOCA has surged recently, mainly utilizing dedicated models. However, these models inadequately replicate hemodynamics, necessitating novel CFD approaches to accurately simulate pathophysiological changes in AAOCA under stress conditions.

Utility of FFRCT in Patients with Chest Pain

PURPOSE OF REVIEW

The goal of this article is to review the data supporting the use of fractional flow reserve derived from coronary computed tomography angiography (FFRCT) in patients with chest pain.

REVIEW FINDINGS

Numerous clinical trials have demonstrated that the diagnostic accuracy of coronary computed tomography angiography (CCTA) can be improved with the use of FFRCT, primarily due to its superior specificity when compared to CCTA alone. This promising development may help reduce the need for invasive angiography in patients presenting with chest pain. Furthermore, some studies have indicated that incorporating FFRCT into decision-making is safe, with an FFRCT value of ≥ 0.8 being associated with favorable outcomes. While FFRCT has been shown to be feasible in patients with acute chest pain, further large-scale studies are warranted to confirm its utility. The emergence of FFRCT as a tool for the management of patients with chest pain is promising. However, potential limitations require the interpretation of FFRCT in conjunction with clinical context.

The Use and Efficacy of FFR-CT: Real-World Multicenter Audit of Clinical Data With Cost Analysis

BACKGROUND

Fractional flow reserve-computed tomography (FFR-CT) is endorsed by UK and U.S. chest pain guidelines, but its clinical effectiveness and cost benefit in real-world practice are unknown.

OBJECTIVES

The purpose of this study was to audit the use of FFR-CT in clinical practice against England's National Institute for Health and Care Excellence guidance and assess its diagnostic accuracy and cost.

METHODS

A multicenter audit was undertaken covering the 3 years when FFR-CT was centrally funded in England. For coronary computed tomographic angiograms (CCTAs) submitted for FFR-CT analysis, centers provided data on symptoms, CCTA and FFR-CT findings, and subsequent management. Audit standards included using FFR-CT only in patients with stable chest pain and equivocal stenosis (50%-69%). Diagnostic accuracy was evaluated against invasive FFR, when performed. Follow-up for nonfatal myocardial infarction and all-cause mortality was undertaken. The cost of an FFR-CT strategy was compared to alternative stress imaging pathways using cost analysis modeling.

RESULTS

A total of 2,298 CCTAs from 12 centers underwent FFR-CT analysis. Stable chest pain was the main symptom in 77%, and 40% had equivocal stenosis. Positive and negative predictive values of FFR-CT were 49% and 76%, respectively. A total of 46 events (2%) occurred over a mean follow-up period of 17 months; FFR-CT (cutoff: 0.80) was not predictive. The FFR-CT strategy costs £2,102 per patient compared with an average of £1,411 for stress imaging.

CONCLUSIONS

In clinical practice, the National Institute for Health and Care Excellence criteria for using FFR-CT were met in three-fourths of patients for symptoms and 40% for stenosis. FFR-CT had a low positive predictive value, making its use potentially more expensive than conventional stress imaging strategies.

Coronary CT-based FFR in patients with acute myocardial infarction might predict follow-up invasive FFR: The XPECT-MI study

BACKGROUND

We aimed to evaluate whether invasive fractional flow reserve (FFRi) of non-infarction related (non-IRA) lesions changes over time in ST-elevation myocardial infarction (STEMI) patients. Moreover, we assessed the diagnostic performance of coronary CT angiography-derived FFR(FFRCT) following the index event in predicting follow-up FFRi.

METHODS

We prospectively enrolled 38 STEMI patients (mean age 61.6 ​± ​9 years, 23.1% female) who underwent non-IRA baseline and follow-up FFRi measurements and a baseline FFRCT (within ≤10 days after STEMI). Follow-up FFRi was performed at 45-60 days (FFRi and FFRCT value of ≤0.8 was considered positive).

RESULTS

FFRi values showed significant difference between baseline and follow-up (median and interquartile range (IQR) 0.85 [0.78-0.92] vs. 0.81 [0.73-0.90] p ​= ​0.04, respectively). Median FFRCT was 0.81 [0.68-0.93]. In total, 20 lesions were positive on FFRCT. A stronger correlation and smaller bias were found between FFRCT and follow-up FFRi (ρ ​= ​0.86,p ​< ​0.001,bias:0.01) as compared with baseline FFRi (ρ ​= ​0.68, p ​< ​0.001,bias:0.04). Comparing follow-up FFRi and FFRCT, no false negatives but two false positive cases were found. The overall accuracy was 94.7%, with sensitivity and specificity of 100.0% and 90.0% for identifying lesions ≤0.8 on FFRi. Accuracy, sensitivity, and specificity were 81.5%, 93.3%, and 73.9%, respectively, for identifying significant lesions on baseline FFRi using index FFRCT.

CONCLUSION

FFRCT in STEMI patients close to the index event could identify hemodynamically relevant non-IRA lesions with higher accuracy than FFRi measured at the index PCI, using follow-up FFRi as the reference standard. Early FFRCT in STEMI patients might represent a new application for cardiac CT to improve the identification of patients who benefit most from staged non-IRA revascularization.

A novel physics-based model for fast computation of blood flow in coronary arteries

Blood flow and pressure calculated using the currently available methods have shown the potential to predict the progression of pathology, guide treatment strategies and help with postoperative recovery. However, the conspicuous disadvantage of these methods might be the time-consuming nature due to the simulation of virtual interventional treatment. The purpose of this study is to propose a fast novel physics-based model, called FAST, for the prediction of blood flow and pressure. More specifically, blood flow in a vessel is discretized into a number of micro-flow elements along the centerline of the artery, so that when using the equation of viscous fluid motion, the complex blood flow in the artery is simplified into a one-dimensional (1D) steady-state flow. We demonstrate that this method can compute the fractional flow reserve (FFR) derived from coronary computed tomography angiography (CCTA). 345 patients with 402 lesions are used to evaluate the feasibility of the FAST simulation through a comparison with three-dimensional (3D) computational fluid dynamics (CFD) simulation. Invasive FFR is also introduced to validate the diagnostic performance of the FAST method as a reference standard. The performance of the FAST method is comparable with the 3D CFD method. Compared with invasive FFR, the accuracy, sensitivity and specificity of FAST is 88.6%, 83.2% and 91.3%, respectively. The AUC of FFR_FAST is 0.906. This demonstrates that the FAST algorithm and 3D CFD method show high consistency in predicting steady-state blood flow and pressure. Meanwhile, the FAST method also shows the potential in detecting lesion-specific ischemia.

Optimal Measurement Sites of Coronary-Computed Tomography Angiography-derived Fractional Flow Reserve: The Insight From China CT-FFR Study

OBJECTIVES

To investigate the optimal measurement site of coronary-computed tomography angiography-derived fractional flow reserve (FFR CT ) for the assessment of coronary artery disease (CAD) in the whole clinical routine practice.

MATERIALS AND METHODS

This retrospective multicenter study included 396 CAD patients who underwent coronary-computed tomography angiography, FFR CT , and invasive FFR. FFR CT was measured at 1 cm (FFR CT -1 cm), 2 cm (FFR CT -2 cm), 3 cm (FFR CT -3 cm), and 4 cm (FFR CT -4 cm) distal to coronary stenosis, respectively. FFR CT and invasive FFR ≤0.80 were defined as lesion-specific ischemia. The diagnostic performance of FFR CT to detect ischemia was obtained using invasive FFR as the reference standard. Reduced invasive coronary angiography rate and revascularization efficiency were calculated. After a median follow-up of 35 months in 267 patients for major adverse cardiovascular events (MACE), Cox hazard proportional models were performed with FFR CT values at each measurement site.

RESULTS

For discriminating lesion-specific ischemia, the areas under the curve of FFR CT -1 cm (0.91) as well as FFR CT -2 cm (0.91) were higher than those of FFR CT -3 cm (0.89) and FFR CT -4 cm (0.88), respectively (all P <0.05). The higher reduced invasive coronary angiography rate (81.6%) was found at FFR CT -1 cm than FFR CT -2 cm (81.6% vs. 62.6%, P <0.05). Revascularization efficiency did not differ between FFR CT -1 cm and FFR CT -2 cm (80.8% vs. 65.5%, P =0.019). In 12.4% (33/267) MACE occurred and only values of FFR CT -2 cm were independently predictive of MACE (hazard ratio: 0.957 [95% CI: 0.925-0.989]; P =0.010).

CONCLUSIONS

This study indicates FFR CT -2 cm is the optimal measurement site with superior diagnostic performance and independent prognostic role.

A high-fidelity geometric multiscale hemodynamic model for predicting myocardial ischemia

BACKGROUND AND OBJECTIVES

Coronary computed tomography angiography (CCTA) derived fractional flow reserve (CT-FFR) requires a maximal hyperemic state to be modeled by assuming the total coronary resistance decreased to a constant 0.24 of that under the resting state. However, this assumption neglects the vasodilator capacity of individual patients. Herein, we proposed a high-fidelity geometric multiscale model (HFMM) to characterize coronary pressure and flow under the resting state, seeking to better predict myocardial ischemia by using CCTA-derived instantaneous wave-free ratio (CT-iFR).

METHODS

Fifty-seven patients (62 lesions) who had undergone CCTA and were then referred to invasive FFR were prospectively enrolled. The coronary microcirculation resistance hemodynamic model (RHM) under the resting condition was established on a patient-specific basis. Coupled with a closed-loop geometric multiscale model (CGM) of their individual coronary circulations, the HFMM model was established to non-invasively derive the CT-iFR from CCTA images.

RESULTS

With the invasive FFR being the reference standard, accuracy of the obtained CT-iFR in identifying myocardial ischemia was greater than those of the CCTA and non-invasively derived CT-FFR (90.32% vs. 79.03% vs. 84.3%). The overall computational time of CT-iFR was 61 ± 6 min, faster than that of the CT-FFR (8 h). The sensitivity, specificity, positive predictive value, and negative predictive value of the CT-iFR in discriminating an invasive FFR > 0.8 were 78% (95% CI: 40-97%), 92% (95% CI: 82-98%), 64% (95% CI: 39-83%), and 96% (95% CI:88-99%), respectively.

CONCLUSIONS

A high-fidelity geometric multiscale hemodynamic model was developed for rapid and accurate estimation of CT-iFR. Compared with CT-FFR, CT-iFR is of less computational cost and enables assessment of tandem lesions.

Beyond CFD: Emerging methodologies for predictive simulation in cardiovascular health and disease

Physics-based computational models of the cardiovascular system are increasingly used to simulate hemodynamics, tissue mechanics, and physiology in evolving healthy and diseased states. While predictive models using computational fluid dynamics (CFD) originated primarily for use in surgical planning, their application now extends well beyond this purpose. In this review, we describe an increasingly wide range of modeling applications aimed at uncovering fundamental mechanisms of disease progression and development, performing model-guided design, and generating testable hypotheses to drive targeted experiments. Increasingly, models are incorporating multiple physical processes spanning a wide range of time and length scales in the heart and vasculature. With these expanded capabilities, clinical adoption of patient-specific modeling in congenital and acquired cardiovascular disease is also increasing, impacting clinical care and treatment decisions in complex congenital heart disease, coronary artery disease, vascular surgery, pulmonary artery disease, and medical device design. In support of these efforts, we discuss recent advances in modeling methodology, which are most impactful when driven by clinical needs. We describe pivotal recent developments in image processing, fluid-structure interaction, modeling under uncertainty, and reduced order modeling to enable simulations in clinically relevant timeframes. In all these areas, we argue that traditional CFD alone is insufficient to tackle increasingly complex clinical and biological problems across scales and systems. Rather, CFD should be coupled with appropriate multiscale biological, physical, and physiological models needed to produce comprehensive, impactful models of mechanobiological systems and complex clinical scenarios. With this perspective, we finally outline open problems and future challenges in the field.

Impact of ramus coronary artery on computed tomography derived fractional flow reserve (FFRCT ) in no apparent coronary artery disease

BACKGROUND

The ramus artery contributes to the development of turbulence, which may influence computed tomography (CT) derived fractional flow reserve (FFR_CT ) even without coronary artery disease (CAD). The relationship between ramus-induced turbulence and FFR_CT is unclear.

METHOD AND RESULTS

A total of 120 patients with <20% coronary stenosis assessed by both FFR_CT and invasive coronary angiography were evaluated. The patients were divided into three groups: absent-ramus (n = 72), small-ramus that could not be analyzed by FFR_CT (n = 18), and large-ramus that could be analyzed by FFR_CT (n = 30). FFR_CT measurements were performed at the proximal and distal segments of the left anterior descending (LAD), left circumflex (LCX), and ramus artery. With absent-ramus and small-absent ramus groups, FFR_CT was measured at the distal end of the left main trunk at the same level for the proximal segments of the LAD and LCX. In absent-ramus group, proximal FFR_CT showed no significant differences between three vessels (LAD = .96 ± .02; MID = .97 ± .02; LCX = .97 ± .02). However, in small and large-ramus groups, proximal FFR_CT was significantly higher in the ramus artery than LAD and LCX (small-ramus, LAD = .95 ± .03, Ramus = .97 ± .02, LCX = .95 ± .03; large-ramus: LAD = .95 ± .03, Ramus = .98 ± .01; LCX = .96 ± .03; p < .05). A large ramus was associated with a higher prevalence of a distal FFR_CT ≤.80 (odds ratio 7.0, 95% CI 1.2-40.1, p = .03). A proximal ramus diameter predicted distal FFR_CT ≤.80 (cut-off 2.1 mm, AUC .76, sensitivity 100%, specificity 52%, 95% CI .61-.90).

CONCLUSIONS

The presence of a large-ramus artery may cause an FFR_CT decline in no apparent CAD.

Computed Tomography-Derived Physiology Assessment: State-of-the-Art Review

Coronary computed tomography angiography (CCTA) and CCTA-derived fractional flow reserve (FFRCT) are the best non-invasive techniques to assess coronary artery disease (CAD) and myocardial ischemia. Advances in these technologies allow a paradigm shift to the use of CCTA and FFRCT for advanced plaque characterization and planning myocardial revascularization.

Association of Lipoprotein (a) With Coronary-Computed Tomography Angiography-Assessed High-Risk Coronary Disease Attributes and Cardiovascular Outcomes

BACKGROUND

Lipoprotein(a) [Lp(a)] is a risk factor for cardiovascular events. This study evaluated the relationship between Lp(a) and high-risk attributes by coronary computed tomography angiography as well as their prognostic value.

METHODS

Lp(a) and coronary computed tomography angiography from 377 consecutive patients at Zhongshan Hospital (Shanghai, China) were evaluated. High-risk attributes were defined as high-risk morphological attributes (low attenuation plaque, positive remodeling, napkin-ring sign, spotty calcification, minimum lumen area <4 mm², or plaque burden [ratio between cross-sectional plaque area at the site of maximum stenosis and cross-sectional vessel area] ≥70%); inflammatory attribute represented by fat attenuation index; high-risk physiological attributes [lesion-specific ischemia defined by fractional flow reserve by coronary computed tomography angiography ≤0.8, physiologic diffuseness defined by fractional flow reserve by coronary computed tomography angiography pullback pressure gradient]. Total plaque volume in mm³ was also quantified. Quintiles or binary classification of Lp(a) levels were used to evaluate its relationships with plaque features and clinical outcomes with ANOVA, Cox models, and log-rank tests, as appropriate. The major adverse cardiovascular event included cardiovascular death, nonfatal myocardial infarction, and target vessel revascularization.

RESULTS

Lp(a) was significantly associated with total plaque volume (P=0.004), fat attenuation index (P=0.031), and fractional flow reserve by coronary computed tomography angiography pullback pressure gradient (P=0.038). Patients with a high Lp(a) level had a higher total plaque volume (393.3 mm³ versus 293.9 mm³, P<0.001), lower pullback pressure gradient (0.62 versus 0.69, P=0.023), higher fat attenuation index (-70.5HU versus -73.9HU, P=0.004), and higher incidence of major adverse cardiovascular event (14.5% versus 6.3%, adjusted hazard ratio: 2.52, 95% CI: 1.12-5.63, P=0.025). In a 4-group classification according to Lp(a) and high-risk attributes, patients with high Lp(a) and ≥3 high-risk attributes had the highest risk of major adverse cardiovascular event (25.9%; overall P<0.001). Causal mediation analysis revealed that around 40% of the prognostic effect of Lp(a) was mediated by high-risk attributes.

CONCLUSIONS

Lp(a) level is associated with coronary computed tomography angiography high-risk characteristics, including morphologic, physiologic, and inflammatory attributes as well as major adverse cardiovascular event. This effect is partly mediated by inflammation and vulnerable plaque.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT05323227.

[Contribution of coroscanner in chronic coronary syndrome]

Cardiac division imaging by coroscanner has progressed a lot in 20 years to gradually become an important and potentially indispensable tool of chronic coronary cardiology. The European and American recommendations are Grade I for the assessment of symptomatic patients at intermediate to high risk, at the same level as traditional functional tests. The development of sophisticated post-treatment algorithms that apply the equations of fluid mechanics makes it possible to calculate an FFR value at any point from the CT image of the coronary artery. This FFR-CT is correctly correlated with invasive FFR compared to a threshold value of 0.80 and helps guide therapeutic choices. Thus, the coroscanner is a complement or an alternative to traditional functional tests and is positioned as a filter of access to coronary angiography, especially since it combines, from the same examination, a precise anatomical description, and a functional evaluation of the various possible lesions. Which is the Holy Grail of coronary cardiology.

Coronary Computed Tomography Angiography-derived Fractional Flow Reserve: An Expert Consensus Document of Chinese Society of Radiology

Invasive fractional flow reserve (FFR) measured by a pressure wire is a reference standard for evaluating functional stenosis in coronary artery disease. Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) uses advanced computational analysis methods to noninvasively obtain FFR results from a single conventional coronary computed tomography angiography data to evaluate the hemodynamic significance of coronary artery disease. More and more evidence has found good correlation between the results of noninvasive CT-FFR and invasive FFR. CT-FFR has proven its potential in optimizing patient management, improving risk stratification and prognosis, and reducing total health care costs. However, there is still a lack of standardized interpretation of CT-FFR technology in real-world clinical settings. This expert consensus introduces the principle, workflow, and interpretation of CT-FFR; summarizes the state-of-the-art application of CT-FFR; and provides suggestions and recommendations for the application of CT-FFR with the aim of promoting the standardized application of CT-FFR in clinical practice.

A Novel Method for Improving the Accuracy of MR-derived Patient-specific Vascular Models using X-ray Angiography

MR imaging, a noninvasive radiation-free imaging modality commonly used during clinical follow up, has been widely utilized to reconstruct realistic 3D vascular models for patient-specific analysis. In recent work, we used patient-specific hemodynamic analysis of the circle of Willis to noninvasively assess stroke risk in pediatric Moyamoya disease (MMD)-a progressive steno-occlusive cerebrovascular disorder that leads to recurrent stroke. The objective was to identify vascular regions with critically high wall shear rate (WSR) that signifies elevated stroke risk. However, sources of error such as insufficient resolution of MR images can negatively impact vascular model accuracy, especially in areas of severe pathological narrowing, and thus diminish clinical relevance of simulation results, as local hemodynamics are sensitive to vessel geometry. To improve the accuracy of MR-derived vascular models, we have developed a novel method for adjusting model vessel geometry utilizing 2D X-ray angiography (XA), which is considered the gold standard for clinically assessing vessel caliber. In this workflow, "virtual angiographies" (VAs) of 3D MR-derived vascular models are conducted, producing 2D projections that are compared with corresponding XA images to guide the local adjustment of modeled vessels. This VA-comparison-adjustment loop is iterated until the two agree, as confirmed by an expert neuroradiologist. Using this method, we generated models of the circle of Willis of two patients with a history of unilateral stroke. Blood flow simulations were performed using a Navier-Stokes solver within an isogeometric analysis framework, and WSR distributions were quantified. Results for one patient show as much as 45% underestimation of local WSR in the stenotic left anterior cerebral artery (LACA), and up to a 56% underestimation in the right anterior cerebral artery when using the initial MR-derived model compared to the XA-adjusted model. To evaluate whether XA-based adjustment improves model accuracy, vessel cross-sectional areas of the pre- and post-adjustment models were compared to those seen in 3D CTA images of the same patient. CTA has superior resolution and signal-to-noise ratio compared to MR imaging but is not commonly used in the clinic due to radiation exposure concerns, especially in pediatric patients. While the vessels in the initial model had normalized root mean squared deviations (NRMSDs) ranging from 26% to 182% and 31% to 69% in two patients with respect to CTA, the adjusted vessel NRMSDs were comparatively smaller (32% to 53% and 11% to 42%). In the mildly stenotic LACA of patient 1, the NRMSDs for the pre- and post-adjusted models were 49% and 32%, respectively. These findings suggest that our XA-based adjustment method can considerably improve the accuracy of vascular models, and thus, stroke-risk prediction. An accurate, individualized assessment of stroke risk would be of substantial help in guiding the timing of preventive surgical interventions in pediatric MMD patients.

Improved Functional Assessment of Ischemic Severity Using 3D Printed Models

Objective

To develop a novel in vitro method for evaluating coronary artery ischemia using a combination of non-invasive coronary CT angiograms (CCTA) and 3D printing (FFR_3D).

Methods

Twenty eight patients with varying degrees of coronary artery disease who underwent non-invasive CCTA scans and invasive fractional flow reserve (FFR) of their epicardial coronary arteries were included in this study. Coronary arteries were segmented and reconstructed from CCTA scans using Mimics (Materialize). The segmented models were then 3D printed using a Carbon M1 3D printer with urethane methacrylate (UMA) family of rigid resins. Physiological coronary circulation was modeled in vitro as flow-dependent stenosis resistance in series with variable downstream resistance. A range of physiological flow rates (Q) were applied using a peristaltic steady flow pump and titrated with a flow sensor. The pressure drop (ΔP) and the pressure ratio (P_d/P_a) were assessed for patient-specific aortic pressure (P_a) and differing flow rates (Q) to evaluate FFR_3D using the 3D printed model.

Results

There was a good positive correlation (r = 0.87, p < 0.0001) between FFR_3D and invasive FFR. Bland-Altman analysis revealed a good concordance between the FFR_3D and invasive FFR values with a mean bias of 0.02 (limits of agreement: −0.14 to 0.18; p = 0.2).

Conclusions

3D printed patient-specific models can be used in a non-invasive in vitro environment to quantify coronary artery ischemia with good correlation and concordance to that of invasive FFR.

Angiographic Lesion Morphology Provides Incremental Value to Generalize Quantitative Flow Ratio for Predicting Myocardial Ischemia

Aim

The quantitative flow ratio (QFR) is favorable for functional assessment of coronary artery stenosis without pressure wires and induction of hyperemia. The aim of this study was to explore whether angiographic lesion morphology provides incremental value to generalize QFR for predicting myocardial ischemia in unselected patients.

Methods

This study was a substudy to the CT-FFR CHINA trial, referring 345 participants from five centers with suspected coronary artery disease on coronary CT angiography for diagnostic invasive coronary angiography (ICA). Fractional flow reserve (FFR) was measured in all vessels with 30–90% diameter stenosis. QFR was calculated in 186 lesions from 159 participants in a blinded manner. In addition, parameters to characterize lesion features were recorded or measured, including left anterior descending arteries (LADs)-involved lesions, side branch located at stenotic lesion (BL), multiple lesions (ML), minimal lumen diameter (MLD), reference lumen diameter (RLD), percent diameter stenosis (%DS), lesion length (LL), and LL/MLD4. Logistic regression was used to construct two kinds of models by combining single or two lesion parameters with the QFR. The performances of these models were compared with that of QFR on a per-vessel level.

Results

A total of 148 participants (mean age: 59.5 years; 101 men) with 175 coronary arteries were included for final analysis. In total, 81 (46%) vessels were considered hemodynamically significant. QFR correctly classified 82.29% of the vessels using FFR with a cutoff of 0.80 as reference standard. The area under the receiver operating characteristic curve (AUC) of QFR was 0.86 with a sensitivity, specificity, positive predictive value, and negative predictive value of 80.25, 84.04, 81.25, and 83.16%, respectively. The combined models (QFR + LAD + MLD, QFR + LAD + %DS, QFR + BL + MLD, and QFR + BL + %DS) outperformed QFR with higher AUCs (0.91 vs. 0.86, P = 0.02; 0.91 vs. 0.86, P = 0.02; 0.91 vs. 0.86, P = 0.02; 0.90 vs. 0.86, P = 0.03, respectively). Compared with QFR, the sensitivity of the combined models (QFR + BL and QFR + MLD) was improved (91.36 vs. 80.25%, 91.36 vs. 80.25%, respectively, both P &lt; 0.05) without compromised specificity or accuracy.

Conclusion

Combined with angiographic lesion parameters, QFR can be optimized for predicting myocardial ischemia in unselected patients.

Harmonizing multimodality imaging results using Bayesian analysis: the case of CT coronary angiography and CT-derived fractional flow reserve

Coronary computed tomographic angiography (CCTA) may provide both anatomic and CT fractional flow reserve data (CTFFR). The objective is to use Bayesian analysis to develop a model wherein the probability of significant coronary artery disease (CAD) by CTFFR can be determined given the prior probability (P) of the combined clinical and CCTA result. 172 patients referred for CCTA and subsequently underwent coronary angiography were automatically referred to CTFFR analysis. A clinical P risk score (CRS) was calculated per patient. CCTA exams were scored using CAD-RADS classification. CTFFR results were generated. CAD was defined as ≥ 3 RAD class for CCTA and ≤ .80 by CTFFR. P was calculated using CCTA and CTFFR accuracy from a prior clinical trial: post-test P for the CCTA result used the CRS as the prior risk, and CTFFR P used the post-test CRS + CCTA P as the prior risk (tri-variable). Patients were classified for each model into low (< 5%), intermediate, (5-70%) and high (> 70%) risk groups. There were 100 patients (58%), who had significant CAD at angiography. 58 patients had discordant CCTA/CTFFR results. The inclusion of the CRS and CRS + CCTA in the prior progressively reduced the intermediate risk cohort from 83 to 41% (p < 0.0001). Correct classifications (low-risk, negative angiogram plus high-risk, positive angiogram) increased by model: CRS = 12%, CRS + CCTA = 25%, CRS + CTFFR = 33%, CRS + CCTA + CTFFR = 44% (p < 0.001). Incorrect classifications were reduced to 15%. The tri-variable model performed better than either CCTA or CTFFR alone for all patients and for the sub-group with discordant imaging results. Discrepant CCTA and CTFFR results are present in one third of patients. The use of both the CRS and CCTA as the prior risk synergistically maximized the accuracy of the accuracy of the CTFFR technique.

Diagnostic and Clinical Value of FFRCT in Stable Chest Pain Patients With Extensive Coronary Calcification: The FACC Study

BACKGROUND

The influence of extensive coronary calcifications on the diagnostic and prognostic value of coronary computed tomography angiography-derived fractional flow reserve (FFR_CT) has been scantily investigated.

OBJECTIVES

The purpose of this study was to investigate the diagnostic and short-term role of FFR_CT in chest pain patients with Agatston score (AS) >399.

METHODS

This was a prospective multicenter study of 260 stable patients with suspected coronary artery disease (CAD) and AS >399. FFR_CT was measured blinded by an independent core laboratory. All patients underwent invasive coronary angiography (ICA) and FFR if indicated. The agreement of FFR_CT ≤0.80 with hemodynamically significant CAD on ICA/FFR (≥50% left main or ≥70% epicardial artery stenosis and/or FFR ≤0.80) was assessed. Patients undergoing FFR had colocation FFR_CT measured, and the lowest per-patient FFR_CT was registered in all patients. The association among per-patient FFR_CT, coronary revascularization, and major clinical events (all-cause mortality, myocardial infarction, or unstable angina hospitalization) at 90-day follow-up was evaluated.

RESULTS

Median age and AS were 68.5 years (IQR: 63-74 years) and 895 (IQR: 587-1,513), respectively. FFR_CT was ≤0.80 in 204 patients (78%). Colocation FFR_CT (n = 112) showed diagnostic accuracy, sensitivity, and specificity to identify hemodynamically significant CAD of 71%, 87%, and 54%. The area under the receiver-operating characteristics curve (AUC) was 0.75. When using the lowest FFR_CT (n = 260), per-patient accuracy, sensitivity, and specificity were 57%, 95%, and 32%, respectively. The AUC was 0.84. A total of 85 patients underwent revascularization, and FFR_CT was ≤0.80 in 96% of these. During follow-up, major clinical events occurred in 3 patients (1.2%), all with FFR_CT ≤0.80.

CONCLUSIONS

Most patients with AS >399 had FFR_CT ≤0.80. Using ICA/FFR as the reference revealed a moderate diagnostic accuracy of colocation FFR_CT. Compared with the lowest per-patient FFR_CT, colocation FFR_CT measurement improved diagnostic accuracy and specificity. The 90-day follow-up was favorable with few coronary revascularizations and no major clinical events occurring in patients with FFR_CT >0.80. (Use of FFR-CT in Stable Intermediate Chest Pain Patients With Severe Coronary Calcium Score [FACC]; NCT03548753).

Fractional Flow Reserve (FFR) Estimation from OCT-Based CFD Simulations: Role of Side Branches

The computational fluid dynamic method has been widely used to quantify the hemodynamic alterations in a diseased artery and investigate surgery outcomes. The artery model reconstructed based on optical coherence tomography (OCT) images generally does not include the side branches. However, the side branches may significantly affect the hemodynamic assessment in a clinical setting, i.e., the fractional flow reserve (FFR), defined as the ratio of mean distal coronary pressure to mean aortic pressure. In this work, the effect of the side branches on FFR estimation was inspected with both idealized and optical coherence tomography (OCT)-reconstructed coronary artery models. The electrical analogy of blood flow was further used to understand the impact of the side branches (diameter and location) on FFR estimation. Results have shown that the side branches decrease the total resistance of the vessel tree, resulting in a higher inlet flowrate. The side branches located at the downstream of the stenosis led to a lower FFR value, while the ones at the upstream had a minimal impact on the FFR estimation. Side branches with a diameter larger than one third of the main vessel diameter are suggested to be considered for a proper FFR estimation. The findings in this study could be extended to other coronary artery imaging modalities and facilitate treatment planning.

The quantitative relationship between coronary microcirculatory resistance and myocardial ischemia in patients with coronary artery disease

It was hypothesized that the microcirculatory resistance of resting state (R_m-res) might be a good predictor for ischemia. In this study, the quantitative relationship between R_m-res and myocardial ischemia in different stenosed degrees was explored and verified through retrospective analysis, and the diagnostic performance was evaluated. 136 patients were screened and divided into a training set (90 patients) and a validation set (46 patients). In the training set, R_m-res was calculated, and thresholds were determined by exploring the relationship between R_m-res and myocardial ischemia in different stenosed degrees. In the validation set, the diagnostic performance of the thresholds was verified. It was found that the 90 data mean difference (95%CI) of R_m-res between the ischemic group and the non-ischemic group was 63.03 (95 %CI: 25.72-100.34), p < 0.05. In the training set with stenosed degree 41-60%, 61-70%, 71-80%, and >81%, the average of R_m-res in the ischemic and non-ischemic groups were (80.79, 136.87), (96.41, 172.62), (128.99, 198.94) and (175.95, 310.79) mmHg/s/ml. The R_m-res thresholds were 87.18, 118.96, 142.35, and 177.39 mmHg/s/ml. In the validation set, the overall sensitivity, specificity, PPV, NPV, and accuracy were 73.3%, 77.4%, 61.1%, 85.7%, and 76.1%. In conclusion, R_m-res had a significant predictor on myocardial ischemia. As a smaller R_m-res represents greater myocardial mass perfusion, it is more likely that a stenosis will have a functional impact. Threshold analysis showed that R_m-res of different stenosed degrees was a quantitative predictor of myocardial ischemia, which could assist physicians with clinical treatment strategies.

Cardiac computed tomography-derived coronary artery volume to myocardial mass

In the absence of disease impacting the coronary arteries or myocardium, there exists a linear relationship between vessel volume and myocardial mass to ensure balanced distribution of blood supply. This balance may be disturbed in diseases of either the coronary artery tree, the myocardium, or both. However, in contemporary evaluation the coronary artery anatomy and myocardium are assessed separately. Recently the coronary lumen volume to myocardial mass ratio (V/M), measured noninvasively using coronary computed tomography angiography (CTCA), has emerged as an integrated measure of myocardial blood supply and demand in vivo. This has the potential to yield new insights into diseases where this balance is altered, thus impacting clinical diagnoses and management. In this review, we outline the scientific methodology underpinning CTCA-derived measurement of V/M. We describe recent studies describing alterations in V/M across a range of cardiovascular conditions, including coronary artery disease, cardiomyopathies and coronary microvascular dysfunction. Lastly, we highlight areas of unmet research need and future directions, where V/M may further enhance our understanding of the pathophysiology of cardiovascular disease.

Medical Image-Based Computational Fluid Dynamics and Fluid-Structure Interaction Analysis in Vascular Diseases

Hemodynamic factors, induced by pulsatile blood flow, play a crucial role in vascular health and diseases, such as the initiation and progression of atherosclerosis. Computational fluid dynamics, finite element analysis, and fluid-structure interaction simulations have been widely used to quantify detailed hemodynamic forces based on vascular images commonly obtained from computed tomography angiography, magnetic resonance imaging, ultrasound, and optical coherence tomography. In this review, we focus on methods for obtaining accurate hemodynamic factors that regulate the structure and function of vascular endothelial and smooth muscle cells. We describe the multiple steps and recent advances in a typical patient-specific simulation pipeline, including medical imaging, image processing, spatial discretization to generate computational mesh, setting up boundary conditions and solver parameters, visualization and extraction of hemodynamic factors, and statistical analysis. These steps have not been standardized and thus have unavoidable uncertainties that should be thoroughly evaluated. We also discuss the recent development of combining patient-specific models with machine-learning methods to obtain hemodynamic factors faster and cheaper than conventional methods. These critical advances widen the use of biomechanical simulation tools in the research and potential personalized care of vascular diseases.

Real time reduced order model for angiography fractional flow reserve

BACKGROUND AND OBJECTIVES

Fractional flow reserve (FFR) is the gold standard for quantification of coronary stenosis and pressure wire is the gold standard for measuring FFR. Recently, computational fluid dynamics (CFD) methods have been used to compute FFR less invasively using images obtained from coronary angiography. This approach is, however, computationally intensive and solutions to reduce computation time are clearly required.

METHODS

We hypothesized that FFR can be calculated instantly using a reduced order model (ROM) derived using response surface method (RSM) for simulation modeling in lieu of the computationally intensive CFD. Specifically, eleven physiological and anatomical factors known to affect FFR were selected as input variables, and Plackett-Burman analysis was performed in conjunction with CFD on model arteries to identify set of variables affecting FFR the most. Based on the Box-Behnken design, a mathematical model was developed to compute FFR using the retained set of variables.

RESULTS

The model fidelity was tested on a cohort of 90 patients (100 coronary arteries) with known pressure-wire FFR. FFR derived from this ROM had a strong correlation with pressure-wire FFR with sensitivity of 89.4%, specificity of 100% and area under curve of 0.947 (p < 0.05).

CONCLUSIONS

The ROM method can be used to reliably calculate FFR in patients with coronary stenosis and able to replace time-consuming CFD-based FFR estimation and provide instead a real-time calculation method.

Cardiac CT angiography in current practice: An American society for preventive cardiology clinical practice statement✰

In this clinical practice statement, we represent a summary of the current evidence and clinical applications of cardiac computed tomography (CT) in evaluation of coronary artery disease (CAD), from an expert panel organized by the American Society for Preventive Cardiology (ASPC), and appraises the current use and indications of cardiac CT in clinical practice. Cardiac CT is emerging as a front line non-invasive diagnostic test for CAD, with evidence supporting the clinical utility of cardiac CT in diagnosis and prevention. CCTA offers several advantages beyond other testing modalities, due to its ability to identify and characterize coronary stenosis severity and pathophysiological changes in coronary atherosclerosis and stenosis, aiding in early diagnosis, prognosis and management of CAD. This document further explores the emerging applications of CCTA based on functional assessment using CT derived fractional flow reserve, peri‑coronary inflammation and artificial intelligence (AI) that can provide personalized risk assessment and guide targeted treatment. We sought to provide an expert consensus based on the latest evidence and best available clinical practice guidelines regarding the role of CCTA as an essential tool in cardiovascular prevention - applicable to risk assessment and early diagnosis and management, noting potential areas for future investigation.