Computational Fractional Flow Reserve Research Articles

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

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

A simplified coronary model for diagnosis of ischemia-causing coronary stenosis

Background and objectiveThe functional assessment of the severity of coronary stenosis from coronary computed tomography angiography (CCTA)-derived fractional flow reserve (FFR) has recently attracted interest. However, existing algorithms run at high computational cost. Therefore, this study proposes a fast calculation method of FFR for the diagnosis of ischemia-causing coronary stenosis. MethodsWe combined CCTA and machine learning to develop a simplified single-vessel coronary model for rapid calculation of FFR. First, a zero-dimensional model of single-vessel coronary was established based on CCTA, and microcirculation resistance was determined through the relationship between coronary pressure and flow. In addition, a coronary stenosis model based on machine learning was introduced to determine stenosis resistance. Computational FFR (cFFR) was then obtained by combining the zero-dimensional model and the stenosis model with inlet boundary conditions for resting (cFFRr) and hyperemic (cFFRh) aortic pressure, respectively. We retrospectively analyzed 75 patients who underwent clinically invasive FFR (iFFR), and verified the model accuracy by comparison of cFFR with iFFR. ResultsThe average computing time of cFFR was less than 2 s. The correlations between cFFRr and cFFRh with iFFR were r = 0.89 (p < 0.001) and r = 0.90 (p < 0.001), respectively. Diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio for cFFRr and cFFRh were 90.7%, 95.0%, 89.1%, 76.0%, 98.0%, 8.7, 0.1 and 92.0%, 95.0%, 90.9%, 79.2%, 98.0%, 10.5, 0.1, respectively. ConclusionsThe proposed model enables rapid prediction of cFFR and exhibits high diagnostic performance in selected patient cohorts. The model thus provides an accurate and time-efficient computational tool to detect ischemia-causing stenosis and assist with clinical decision-making.

A Hybrid Model for Cardiac Perfusion: Coupling a Discrete Coronary Arterial Tree Model with a Continuous Porous-Media Flow Model of the Myocardium.

This paper presents a novel hybrid approach for the computational modeling of cardiac perfusion, combining a discrete model of the coronary arterial tree with a continuous porous-media flow model of the myocardium. The constructive constrained optimization (CCO) algorithm captures the detailed topology and geometry of the coronary arterial tree network, while Poiseuille's law governs blood flow within this network. Contrast agent dynamics, crucial for cardiac MRI perfusion assessment, are modeled using reaction-advection-diffusion equations within the porous-media framework. The model incorporates fibrosis-contrast agent interactions and considers contrast agent recirculation to simulate myocardial infarction and Gadolinium-based late-enhancement MRI findings. Numerical experiments simulate various scenarios, including normal perfusion, endocardial ischemia resulting from stenosis, and myocardial infarction. The results demonstrate the model's efficacy in establishing the relationship between blood flow and stenosis in the coronary arterial tree and contrast agent dynamics and perfusion in the myocardial tissue. The hybrid model enables the integration of information from two different exams: computational fractional flow reserve (cFFR) measurements of the heart coronaries obtained from CT scans and heart perfusion and anatomy derived from MRI scans. The cFFR data can be integrated with the discrete arterial tree, while cardiac perfusion MRI data can be incorporated into the continuum part of the model. This integration enhances clinical understanding and treatment strategies for managing cardiovascular disease.

Anatomical and Functional Discrepancy in Diabetic Patients With Intermediate Coronary Lesions - An Intravascular Ultrasound and Quantitative Flow Ratio Study.

Data regarding the performance of computational fractional flow reserve in patients with diabetes mellitus (DM) remain scarce. This study sought to explore the impact of DM on quantitative flow ratio (QFR) and its association with intravascular ultrasound (IVUS)-derived anatomical references.Methods and Results: IVUS and QFR were retrospectively analyzed in 237 non-diabetic and 93 diabetic patients with 250 and 102 intermediate lesions, respectively. Diabetics were further categorized based on adequate (HbA1c <7.0%: 47 patients with 53 lesions) or poor (HbA1c ≥7.0%: 46 patients with 49 lesions) glycemic control. Lesions with QFR ≤0.8 or minimum lumen area (MLA) ≤4.0 mm2and plaque burden (PB, %) ≥70 were considered functionally or anatomically significant, respectively. PB increased, and MLA decreased stepwise across non-diabetics, diabetics with adequate glycemic control and those with poor glycemic control. In contrast, QFR was similar among the 3 groups. PB correlated significantly with the QFR for lesions in non-diabetics, but not for lesions in diabetics. DM was independently correlated with the functionally non-significant lesions (QFR >0.8) with high-risk IVUS features (MLA ≤4.0 mm2and PB ≥70; OR 2.053, 95% CI: 1.137-3.707, P=0.017). When considering the effect of glycemic control, HbA1c was an independent predictor of anatomical-functional discordance (OR 1.347, 95% CI: 1.089-1.667, P=0.006). Anatomical-functional discordance of intermediate coronary lesions assessed by IVUS and QFR is exacerbated in patients with diabetes, especially when glycemia is poorly controlled.

Validity and Diagnostic Performance of Computing Fractional Flow Reserve From 2-Dimensional Coronary Angiography Images

Measurement of fractional flow reserve (FFR) is the gold standard for determining the physiologic significance of coronary artery stenosis, but newer software programs can calculate the FFR from 2-dimensional angiography images. A retrospective analysis was conducted using the records of patients with intermediate coronary stenoses who had undergone adenosine FFR (aFFR). To calculate the computed FFR, a software program used simulated coronary blood flow using computational geometry constructed using at least 2 patient-specific angiographic images. Two cardiologists reviewed the angiograms and determined the computational FFR independently. Intraobserver variability was measured using κ analysis and the intraclass correlation coefficient. The correlation coefficient and Bland-Altman plots were used to assess the agreement between the calculated FFR and the aFFR. A total of 146 patients were included, with 95 men and 51 women, with a mean (SD) age of 61.1 (9.5) y. The mean (SD) aFFR was 0.847 (0.072), and 41 patients (27.0%) had an aFFR of 0.80 or less. There was a strong intraobserver correlation between the computational FFRs (r = 0.808; P < .001; κ = 0.806; P < .001). There was also a strong correlation between aFFR and computational FFR (r = 0.820; P < .001) and good agreement on the Bland-Altman plot. The computational FFR had a high sensitivity (95.1%) and specificity (90.1%) for detecting an aFFR of 0.80 or less. A novel software program provides a feasible method of calculating FFR from coronary angiography images without resorting to pharmacologically induced hyperemia.

Computational Fractional Flow Reserve From Coronary Computed Tomography Angiography-Optical Coherence Tomography Fusion Images in Assessing Functionally Significant Coronary Stenosis.

BackgroundCoronary computed tomography angiography (CTA) and optical coherence tomography (OCT) provide additional functional information beyond the anatomy by applying computational fluid dynamics (CFD). This study sought to evaluate a novel approach for estimating computational fractional flow reserve (FFR) from coronary CTA-OCT fusion images.MethodsAmong patients who underwent coronary CTA, 148 patients who underwent both pressure wire-based FFR measurement and OCT during angiography to evaluate intermediate stenosis in the left anterior descending artery were included from the prospective registry. Coronary CTA-OCT fusion images were created, and CFD was applied to estimate computational FFR. Based on pressure wire-based FFR as a reference, the diagnostic performance of Fusion-FFR was compared with that of CT-FFR and OCT-FFR.ResultsFusion-FFR was strongly correlated with FFR (r = 0.836, P < 0.001). Correlation between FFR and Fusion-FFR was stronger than that between FFR and CT-FFR (r = 0.682, P < 0.001; z statistic, 5.42, P < 0.001) and between FFR and OCT-FFR (r = 0.705, P < 0.001; z statistic, 4.38, P < 0.001). Area under the receiver operating characteristics curve to assess functionally significant stenosis was higher for Fusion-FFR than for CT-FFR (0.90 vs. 0.83, P = 0.024) and OCT-FFR (0.90 vs. 0.83, P = 0.043). Fusion-FFR exhibited 84.5% accuracy, 84.6% sensitivity, 84.3% specificity, 80.9% positive predictive value, and 87.5% negative predictive value. Especially accuracy, specificity, and positive predictive value were superior for Fusion-FFR than for CT-FFR (73.0%, P = 0.007; 61.4%, P < 0.001; 64.0%, P < 0.001) and OCT-FFR (75.7%, P = 0.021; 73.5%, P = 0.020; 69.9%, P = 0.012).ConclusionCFD-based computational FFR from coronary CTA-OCT fusion images provided more accurate functional information than coronary CTA or OCT alone.Clinical Trial Registration[www.ClinicalTrials.gov], identifier [NCT03298282].

Wall shear stress indicators influence the regular hemodynamic conditions in coronary main arterial diseases: cardiovascular abnormalities

Computational hemodynamic (CH) characteristics play a central role in the onset and expansion of atherosclerotic plaques in the coronary main arteries. This study has explored the effects of hemodynamic properties especially coronary arterial wall tangential stresses on various healthy and diseased patient-based coronary artery models based on coronary computed tomography angiography (CCTA) imaging. The key components of the work are the CCTA image acquisition, accurate three-dimensional (3 D) model segmentation, reconstruction, appropriate grid generation, CH simulations, and analysis of the results by using open-source techniques. The CH simulation results have produced hemodynamic variables, including velocity magnitude (VM), mean arterial pressure difference, wall shear stress (WSS), time-averaged WSS (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), and finally, computational fractional flow reserve (cFFR), that allow the pathophysiological conditions in patient-based coronary models. The VM, mean pressure difference, and WSS indices have yielded consistent simulation results for predicting the severity conditions of coronary diseases. We have compared our cFFR results with the published results and observed that the WSS indices were a good alternative approach for measuring the severity of coronary lesions. The CH results allow a medical expert to estimate the severity of a lumen area and stenosis physiological blood flow conditions in a non-invasive way.

Impact of coronary plaque morphology on the precision of computational fractional flow reserve derived from optical coherence tomography imaging.

Computational fractional flow reserve (FFR) was recently developed to expand the use of physiology-guided percutaneous coronary intervention (PCI). Nevertheless, current methods do not account for plaque composition. It remains unknown whether the numerical precision of computational FFR is impacted by the plaque composition in the interrogated vessels. This study is an observational, retrospective, cross-sectional study. Patients who underwent both optical coherence tomography (OCT) and FFR prior to intervention between August 2011 and October 2018 at Wakayama Medical University Hospital were included. All frames from OCT pullbacks were analyzed using a deep learning algorithm to obtain coronary plaque morphology including thin-cap fibroatheroma (TCFA), lipidic plaque volume (LPV), fibrous plaque volume (FPV), and calcific plaque volume (CPV). The interrogated vessels were stratified into three subgroups: the overestimation group with the numerical difference between the optical flow ratio (OFR) and FFR >0.05, the reference group with the difference ≥-0.05 and ≤0.05, and the underestimation group with the difference <-0.05. In total 230 vessels with intermediate coronary artery stenosis from 193 patients were analyzed. The mean FFR was 0.82±0.10. Among them, 21, 179, and 30 vessels were in the overestimation, the reference, and the underestimation group, respectively. TCFA was higher in the underestimation group (60%) compared with reference (36.3%) and overestimation group (19%). Besides, it was not associated with numerical difference between OFR and FFR (NDOF) after multilevel linear regression. LPV was associated with NDOF as OFR underestimated FFR with -0.028 [95% confidence interval (CI): -0.047, -0.009] for every 100 mm3 increase in LPV. High lipid burden underestimates FFR when OFR is used to assess the hemodynamic importance of intermediate coronary artery stenosis. TCFA, FPV, and CPV were not independent predictors of NDOF.

Diagnostic performance of fractional flow reserve derived from coronary angiography, intravascular ultrasound, and optical coherence tomography; a meta-analysis

BackgroundLittle is known about the overall diagnostic performance of computational fractional flow reserve (FFR) derived from angiography (Angio-FFR), intravascular ultrasound (IVUS-FFR), and optical coherence tomography (OCT-FFR) to detect hemodynamically significant coronary artery disease. The present study aimed to evaluate the diagnostic performance of those novel physiologic indices using conventional FFR as the gold standard. MethodsPubMed and Embase were searched in September 2021 for a systematic review and meta-analysis of studies assessing the diagnostic performance of invasive imaging-derived FFR. The primary outcomes were the summary sensitivity, specificity, correlation coefficients of each index. ResultsA total of 6572 records were initially identified and 49 studies were included in the final analysis (7010 lesions from 36 studies for Angio-FFR, 305 lesions from 5 studies for IVUS-FFR, and 667 lesions from 8 studies for OCT-FFR). Invasive imaging-derived FFR had a high diagnostic performance to detect functionally significant coronary lesions using conventional FFR as the gold standard [Angio-FFR, sensitivity 0.87 (95% CI 0.84–0.89), specificity 0.93 (95% CI 0.910.95); IVUS-FFR, sensitivity 0.90 (95% CI 0.84–0.94), specificity 0.95 (95% CI 0.90–0.98); OCT-FFR, sensitivity 0.85 (95% CI 0.78–0.91), specificity 0.93 (95% CI 0.89–0.95)]. The summary correlation coefficients of Angio-, IVUS-, and OCT-FFRs with wire-based FFR were 0.83 (95% CI 0.80–0.85), 0.85 (95% CI 0.79–0.91), and 0.80 (95% CI 0.74–0.86), respectively. ConclusionsThis meta-analysis demonstrated that computational FFR derived from invasive coronary imaging has clinically acceptable diagnostic performances irrespective of modalities, supporting their applicability to clinical practice.

Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: A novel method based on bifurcation fractal law.

We aimed to evaluate the diagnostic accuracy of computation of fractional flow reserve (FFR) from a single angiographic view in patients with intermediate coronary stenosis. Computation of quantitative flow ratio (QFR) from a single angiographic view might increase the feasibility of routine use of computational FFR. In addition, current QFR solutions assume a linear tapering of the reference vessel size, which might decrease the diagnostic accuracy in the presence of the physiologically significant bifurcation lesions. An artificial intelligence algorithm was proposed for automatic delineation of lumen contours of major epicardial coronary arteries including their side branches. A step-down reference diameter function was reconstructed based on the Murray bifurcation fractal law and used for QFR computation. Validation of this Murray law-based QFR (μQFR) was performed on the FAVOR II China study population. The μQFR was computed separately in two angiographic projections, starting with the one with optimal angiographic image quality. Hemodynamically significant coronary stenosis was defined by pressure wire-derived FFR ≤0.80. The μQFR was successfully computed in all 330 vessels of 306 patients. There was excellent correlation (r = 0.90, p < .001) and agreement (mean difference = 0.00 ± 0.05, p = .378) between μQFR and FFR. The vessel-level diagnostic accuracy for μQFR to identify hemodynamically significant stenosis was 93.0% (95% CI: 90.3 to 95.8%), with sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio of 87.5% (95% CI: 80.2 to 92.8%), 96.2% (95% CI: 92.6 to 98.3%), 92.9% (95% CI: 86.5 to 96.9%), 93.1% (95% CI: 88.9 to 96.1%), 23.0 (95% CI: 11.6 to 45.5), 0.13 (95% CI: 0.08 to 0.20), respectively. Use of suboptimal angiographic image view slightly decreased the diagnostic accuracy of μQFR (AUC = 0.97 versus 0.92, difference = 0.05, p < .001). Intra- and inter-observer variability for μQFR computation was 0.00 ± 0.03, and 0.00 ± 0.03, respectively. Average analysis time for μQFR was 67 ± 22 s. Computation of μQFR from a single angiographic view has high feasibility and excellent diagnostic accuracy in identifying hemodynamically significant coronary stenosis. The short analysis time and good reproducibility of μQFR bear potential of wider adoption of physiological assessment in the catheterization laboratory.

Validation of the diagnostic performance of 'HeartMedi V.1.0', a novel CT-derived fractional flow reserve measurement, for patients with coronary artery disease: a study protocol.

IntroductionCoronary CT angiography (CCTA) is widely used for non-invasive coronary artery evaluation, but it is limited in identifying the nature of functional characteristics that cause ischaemia. Recent computational fluid dynamic (CFD) techniques applied to CCTA images permit non-invasive computation of fractional flow reserve (FFR), a measure of lesion-specific ischaemia. However, this technology has limitations, such as long computational time and the need for expensive equipment, which hinder widespread use.Methods and analysisThis study is a prospective, multicentre, comparative and confirmatory trial designed to evaluate the diagnostic performance of HeartMedi V.1.0, a novel CT-derived FFR measurement for the detection of haemodynamically significant coronary artery stenoses identified by CCTA, based on invasive FFR as a reference standard. The invasive FFR values ≤0.80 will be considered haemodynamically significant. The study will enrol 184 patients who underwent CCTA, invasive coronary angiography and invasive FFR. Computational FFR (c-FFR) will be analysed by CFD techniques using a lumped parameter model based on vessel length method. Blinded core laboratory interpretation will be performed for CCTA, invasive coronary angiography, invasive FFR and c-FFR. The primary objective of the study is to compare the area under the receiver–operator characteristic curve between c-FFR and CCTA to non-invasively detect the presence of haemodynamically significant coronary stenosis. The secondary endpoints include diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value and correlation of c-FFR with invasive FFR.Ethics and disseminationThe study has ethic approval from the ethics committee of Seoul National University Bundang Hospital (E-1709/420-001) and informed consent will be obtained for all enrolled patients. The result will be published in a peer-reviewed journal.Trial registration numberKCT0002725; Pre-results.

Virtual FFR Quantified with a Generalized Flow Model Using Windkessel Boundary Conditions

Fractional flow reserve (FFR) has proved its efficiency in improving patient diagnosis. In this paper, we consider a 2D reconstructed left coronary tree with two artificial lesions of different degrees. We use a generalized fluid model with a Carreau law and use a coupled multidomain method to implement Windkessel boundary conditions at the outlets. We introduce our methodology to quantify the virtual FFR and conduct several numerical experiments. We compare FFR results from the Navier–Stokes model versus generalized flow model and for Windkessel versus traction-free outlet boundary conditions or mixed outlet boundary conditions. We also investigate some sources of uncertainty that the FFR index might encounter during the invasive procedure, in particular, the arbitrary position of the distal sensor. The computational FFR results show that the degree of stenosis is not enough to classify a lesion, while there is a good agreement between the Navier–Stokes model and the non-Newtonian flow model adopted in classifying coronary lesions. Furthermore, we highlight that the lack of standardization while making FFR measurement might be misleading regarding the significance of stenosis.

On outflow boundary conditions for CT-based computation of FFR: Examination using PET images

CT-based computations of fractional flow reserve (FFR) have been widely utilized for evaluating functional severity of a coronary artery stenosis. Whilst this approach has been successful clinically, assumptions involved in the analysis still need to be investigated for further improvement in predictive accuracy. To better understand the sensitivity of computational FFRs on outflow boundary condition – typically reflecting patient's own physiology only through anatomical features – FFR computations for 10 patients with different degree of stenosis was conducted. The computations were based on 3D anatomical model reconstructed from CT images and patient-specific in/outflow boundary conditions (BC). Two outflow BCs were considered: (1) conventional morphology-based and (2) PET perfusion-based conditions. The results showed that the FFRs derived from the two boundary conditions agree in general. It was also found that the FFRs computed with the morphology-based BC tend to estimate higher functional severity, especially in patients with reduced vasodilatory response under hyperaemia – an essential physiological condition in FFR measurement. Further investigation was made by varying hyperaemic resistances (30%-90% of the baseline) in the morphology-based BC. The variation of FFR for the varied resistances was narrow for patients with mild stenosis and wider for those who have severe stenosis. This latter approach confirmed that variability of FFR due to outflow condition tends to come from overestimation of vasodilatory response, especially those who have abnormal myocardial perfusion. The results suggest that outflow conditions that are more representative of each patient could be an effective way to improve CT-based FFR computation.

500.05 Comparison Between Fractional Flow Reserve (FFR) vs. Computational Fractional Flow Reserve Derived from Three-dimensional Intravascular Ultrasound (IVUSFR) and Quantitative Flow Ratio (QFR).

The determination of the ischemic status of a coronary artery by wireless physiologic assessment derived from angiography has been validated and approved in the US. However, the use of plain angiography quantitative variables does not add much to the physiology data since it has low correlation with

Current Evidence in Cardiothoracic Imaging: Computed Tomography-derived Fractional Flow Reserve in Stable Chest Pain.

High-accuracy diagnostic imaging is needed to diagnose and manage coronary artery disease as well as to allow risk stratification for future events. Advancements in multidetector computed tomography and image postprocessing allow for routine computed tomography coronary angiography to provide anatomic luminal assessment similar to invasive coronary angiography, and, similarly, computational fractional flow reserve derived from computed tomography facilitates determination of hemodynamically relevant stenosis comparable to invasive fractional flow reserve. In this review article, we describe the diagnostic performance and the potential impact of fractional flow reserve derived from computed tomography in clinical practice.

First Presentation of Integration of Intravascular Optical Coherence Tomography and Computational Fractional Flow Reserve.

First Presentation of Integration of Intravascular Optical Coherence Tomography and Computational Fractional Flow Reserve.

Uncertainty Quantification and Sensitivity Analysis for Computational FFR Estimation in Stable Coronary Artery Disease

The main objectives of this study are to validate a reduced-order model for the estimation of the fractional flow reserve (FFR) index based on blood flow simulations that incorporate clinical imaging and patient-specific characteristics, and to assess the uncertainty of FFR predictions with respect to input data on a per patient basis. We consider 13 patients with symptoms of stable coronary artery disease for which 24 invasive FFR measurements are available. We perform an extensive sensitivity analysis on the parameters related to the construction of a reduced-order (hybrid 1D-0D) model for FFR predictions. Next we define an optimal setting by comparing reduced-order model predictions with solutions based on the 3D incompressible Navier-Stokes equations. Finally, we characterize prediction uncertainty with respect to input data and identify the most influential inputs by means of sensitivity analysis. Agreement between FFR computed by the reduced-order model and by the full 3D model was satisfactory, with a bias ([Formula: see text]) of [Formula: see text] at the 24 measured locations. Moreover, the uncertainty related to the factor by which peripheral resistance is reduced from baseline to hyperemic conditions proved to be the most influential parameter for FFR predictions, whereas uncertainty in stenosis geometry had greater effect in cases with low FFR. Model errors related to solving a simplified reduced-order model rather than a full 3D problem were small compared with uncertainty related to input data. Improved measurement of coronary blood flow has the potential to reduce uncertainty in computational FFR predictions significantly.

TCT-308 Comparison between fractional flow reserve (FFR) and Computational fractional flow reserve derived from three-dimensional intravascular ultrasound (FFR-IVUS), percentage of diameter stenosis by visual estimation and bi-dimensional quantitative coronary angiography.

Current guidelines recommend the supplementary assessment of intermediate coronary artery disease (CAD) lesions by fractional flow reserve or gray scale intravascular ultrasound (IVUS) in order to predict the actual significance of the stenosis. Recently, the advances of computational fluid dynamics

Local Flow Patterns After Implantation of Bioresorbable Vascular Scaffold in Coronary Bifurcations - Novel Findings by Computational Fluid Dynamics.

Development of methods for accurate reconstruction of bioresorbable scaffolds (BRS) and assessing local hemodynamics is crucial for investigation of vascular healing after BRS implantation.Methods and Results:Patients with BRS that crossed over in a coronary bifurcation were included for analysis. Reconstructions of the coronary lumen and BRS were performed by fusion of optical coherence tomography and coronary angiography generating a tree model (TM) and a hybrid model with BRS (TM-BRS). A virtual BRS model with thinner struts was created and all 3 models were analyzed using computational fluid dynamics to derive: (1) time-average shear stress (TASS), (2) TASS gradient (TASSG), which represents SS heterogeneity, and (3) fractional flow reserve (FFR). Reconstruction of the BRS was successful in all 10 patients. TASS and TASSG were both higher by TM-BRS than by TM in main vessels (difference 0.27±4.30 Pa and 10.18±27.28 Pa/mm, P<0.001), with a remarkable difference at side branch ostia (difference 13.51±17.40 Pa and 81.65±105.19 Pa/mm, P<0.001). With thinner struts, TASS was lower on the strut surface but higher at the inter-strut zones, whereas TASSG was lower in both regions (P<0.001 for all). Computational FFR was lower by TM-BRS than by TM for both main vessels and side branches (P<0.001). Neglecting BRS reconstruction leads to significantly lower SS and SS heterogeneity, which is most pronounced at side branch ostia. Thinner struts can marginally reduce SS heterogeneity.

TCT-72 Computational fractional flow reserve derived from three-dimensional intravascular ultrasound: a new algorithm of fusion between anatomy and physiology

Fractional flow reserve (FFR) and intravascular ultrasound (IVUS) imaging, respectively, are considered as the “gold standard” for functional and anatomical assessments of angiographic intermediate stenoses. Associating both in a single method could optimize diagnosis and treatment of coronary