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

Diagnostic Performance of Coronary Angiography Derived Computational Fractional Flow Reserve

BACKGROUND

Computational fluid dynamics can compute fractional flow reserve (FFR) accurately. However, existing models are limited by either the intravascular hemodynamic phenomarkers that can be captured or the fidelity of geometries that can be modeled.

METHODS AND RESULTS

This study aimed to validate a new coronary angiography-based FFR framework, FFRHARVEY, and examine intravascular hemodynamics to identify new biomarkers that could augment FFR in discerning unrevascularized patients requiring intervention. A 2-center cohort was used to examine diagnostic performance of FFRHARVEY compared with reference wire-based FFR (FFRINVASIVE). Additional biomarkers, longitudinal vorticity, velocity, and wall shear stress, were evaluated for their ability to augment FFR and indicate major adverse cardiac events. A total of 160 patients with 166 lesions were investigated. FFRHARVEY was compared with FFRINVASIVE by investigators blinded to the invasive FFR results with a per-stenosis area under the curve of 0.91, positive predictive value of 90.2%, negative predictive value of 89.6%, sensitivity of 79.3%, and specificity of 95.4%. The percentage ofdiscrepancy for continuous values of FFR was 6.63%. We identified a hemodynamic phenomarker, longitudinal vorticity, as a metric indicative of major adverse cardiac events in unrevascularized gray-zone cases.

CONCLUSIONS

FFRHARVEY had high performance (area under the curve: 0.91, positive predictive value: 90.2%, negative predictive value: 89.6%) compared with FFRINVASIVE. The proposed framework provides a robust and accurate way to compute a complete set of intravascular phenomarkers, in which longitudinal vorticity was specifically shown to differentiate vessels predisposed to major adverse cardiac events.

Prediction of Hemodynamic-Related Hemolysis in Carotid Stenosis and Aiding in Treatment Planning and Risk Stratification Using Computational Fluid Dynamics

Atherosclerosis affects human health in many ways, leading to disability or premature death due to ischemic heart disease, stroke, or limb ischemia. Poststenotic blood flow disruption may also play an essential role in artery wall impairment linked with hemolysis related to shear stress. The maximum shear stress in the atherosclerotic plaque area is the main parameter determining hemolysis risk. In our work, a 3D internal carotid artery model was built from CT scans performed on patients qualified for percutaneous angioplasty due to its symptomatic stenosis. The obtained stenosis geometries were used to conduct a series of computer simulations to identify critical parameters corresponding to the increase in shear stress in the arteries. Stenosis shape parameters responsible for the increase in shear stress were determined. The effect of changes in the carotid artery size, length, and degree of narrowing on the change in maximum shear stress was demonstrated. Then, a correlation for the quick initial diagnosis of atherosclerotic stenoses regarding the risk of hemolysis was developed. The developed relationship for rapid hemolysis risk assessment uses information from typical non-invasive tests for treated patients. Practical guidelines have been developed regarding which stenosis shape parameters pose a risk of hemolysis, which may be adapted in medical practice.

Fractional Flow Reserve-Based Patient Risk Classification

Cardiovascular diseases (CVDs) are a leading cause of death. If not treated in a timely manner, cardiovascular diseases can cause a plethora of major life complications that can include disability and a loss of the ability to work. Globally, acute myocardial infarction (AMI) is responsible for about 3 million deaths a year. The development of strategies for prevention, but also the early detection of cardiovascular risks, is of great importance. The fractional flow reserve (FFR) is a measurement used for an assessment of the severity of coronary artery stenosis. The goal of this research was to develop a technique that can be used for patient fractional flow reserve evaluation, as well as for the assessment of the risk of death via gathered demographic and clinical data. A classification ensemble model was built using the random forest machine learning algorithm for the purposes of risk prediction. Referent patient classes were identified by the observed fractional flow reserve value, where patients with an FFR higher than 0.8 were viewed as low risk, while those with an FFR lower than 0.8 were identified as high risk. The final classification ensemble achieved a 76.21% value of estimated prediction accuracy, thus achieving a mean prediction accuracy of 74.1%, 77.3%, 78.1% and 83.6% over the models tested with 5%, 10%, 15% and 20% of the test samples, respectively. Along with the machine learning approach, a numerical approach was implemented through a 3D reconstruction of the coronary arteries for the purposes of stenosis monitoring. Even with a small number of available data points, the proposed methodology achieved satisfying results. However, these results can be improved in the future through the introduction of additional data, which will, in turn, allow for the utilization of different machine learning algorithms.

A novel method for noninvasive quantification of fractional flow reserve based on the custom function

Boundary condition settings are key risk factors for the accuracy of noninvasive quantification of fractional flow reserve (FFR) based on computed tomography angiography (i.e., FFRCT). However, transient numerical simulation-based FFRCT often ignores the three-dimensional (3D) model of coronary artery and clinical statistics of hyperemia state set by boundary conditions, resulting in insufficient computational accuracy and high computational cost. Therefore, it is necessary to develop the custom function that combines the 3D model of the coronary artery and clinical statistics of hyperemia state for boundary condition setting, to accurately and quickly quantify FFRCT under steady-state numerical simulations. The 3D model of the coronary artery was reconstructed by patient computed tomography angiography (CTA), and coronary resting flow was determined from the volume and diameter of the 3D model. Then, we developed the custom function that took into account the interaction of stenotic resistance, microcirculation resistance, inlet aortic pressure, and clinical statistics of resting to hyperemia state due to the effect of adenosine on boundary condition settings, to accurately and rapidly identify coronary blood flow for quantification of FFRCT calculation (FFRU). We tested the diagnostic accuracy of FFRU calculation by comparing it with the existing methods (CTA, coronary angiography (QCA), and diameter-flow method for calculating FFR (FFRD)) based on invasive FFR of 86 vessels in 73 patients. The average computational time for FFRU calculation was greatly reduced from 1-4 h for transient numerical simulations to 5 min per simulation, which was 2-fold less than the FFRD method. According to the results of the Bland-Altman analysis, the consistency between FFRU and invasive FFR of 86 vessels was better than that of FFRD. The area under the receiver operating characteristic curve (AUC) for CTA, QCA, FFRD and FFRU at the lesion level were 0.62 (95% CI: 0.51-0.74), 0.67 (95% CI: 0.56-0.79), 0.85 (95% CI: 0.76-0.94), and 0.93 (95% CI: 0.87-0.98), respectively. At the patient level, the AUC was 0.61 (95% CI: 0.48-0.74) for CTA, 0.65 (95% CI: 0.53-0.77) for QCA, 0.83 (95% CI: 0.74-0.92) for FFRD, and 0.92 (95% CI: 0.89-0.96) for FFRU. The proposed novel method might accurately and rapidly identify coronary blood flow, significantly improve the accuracy of FFRCT calculation, and support its wide application as a diagnostic indicator in clinical practice.

Analysis identifying minimal governing parameters for clinically accurate in silico fractional flow reserve

Background

Personalized hemodynamic models can accurately compute fractional flow reserve (FFR) from coronary angiograms and clinical measurements (FFR baseline ), but obtaining patient-specific data could be challenging and sometimes not feasible. Understanding which measurements need to be patient-tuned vs. patient-generalized would inform models with minimal inputs that could expedite data collection and simulation pipelines.

Aims

To determine the minimum set of patient-specific inputs to compute FFR using invasive measurement of FFR (FFR invasive ) as gold standard.

Materials and Methods

Personalized coronary geometries ( N = 50 ) were derived from patient coronary angiograms. A computational fluid dynamics framework, FFR baseline , was parameterized with patient-specific inputs: coronary geometry, stenosis geometry, mean arterial pressure, cardiac output, heart rate, hematocrit, and distal pressure location. FFR baseline was validated against FFR invasive and used as the baseline to elucidate the impact of uncertainty on personalized inputs through global uncertainty analysis. FFR streamlined was created by only incorporating the most sensitive inputs and FFR semi-streamlined additionally included patient-specific distal location.

Results

FFR baseline was validated against FFR invasive via correlation ( r = 0.714 , p < 0.001 ), agreement (mean difference: 0.01 ± 0.09 ), and diagnostic performance (sensitivity: 89.5%, specificity: 93.6%, PPV: 89.5%, NPV: 93.6%, AUC: 0.95). FFR semi-streamlined provided identical diagnostic performance with FFR baseline . Compared to FFR baseline vs. FFR invasive , FFR streamlined vs. FFR invasive had decreased correlation ( r = 0.64 , p < 0.001 ), improved agreement (mean difference: 0.01 ± 0.08 ), and comparable diagnostic performance (sensitivity: 79.0%, specificity: 90.3%, PPV: 83.3%, NPV: 87.5%, AUC: 0.90).

Conclusion

Streamlined models could match the diagnostic performance of the baseline with a full gamut of patient-specific measurements. Capturing coronary hemodynamics depended most on accurate geometry reconstruction and cardiac output measurement.

Effect of the ratio of vessel-specific volume to fractional myocardial mass on fractional flow reserve

This study aimed to examine whether the ratio of vessel-specific coronary arterial lumen volume to the fraction of myocardial mass (V_R/M_R) affects myocardial ischemia. We proposed a calculation method for V_R/M_R, and compared the ratio of total epicardial coronary arterial lumen volume to left ventricular myocardial mass (V/M) with V_R/M_R in predicting myocardial ischemia. V_R/M_R and V/M were computed using data from 205 patients with 241 stenosis vessel who underwent coronary computed tomography angiography (CTA), quantitative coronary angiography, and fractional flow reserve. The vessel-specific coronary arterial lumen volume (V_R) was obtained from CTA by segmenting the coronary arterial lumen volume, while the vessel-specific fraction of myocardial mass (M_R) was obtained by allometric scaling. The V_R/M_R was then calculated. The cut-off values of V/M (23.55 mm³/g) and V_R/M_R (12.98 mm³/g) were used to define equal groups of ischemic and non-ischemic patients, respectively. Using these cut-off values, the accuracy, specificity, sensitivity, positive predictive value, and negative predictive value of V/M were 60%, 76%, 45%, 57%, and 66%, and of V_R/M_R were 87%, 92%, 77%, 89%, and 83%, respectively. Patients have different V_R/M_R values in different stenotic coronary arteries. Clinically, V_R/M_R is a quantitative indicator of the risk of myocardial ischemia.

A modified method of noninvasive computed tomography derived fractional flow reserve based on the microvascular growth space

OBJECTIVE

To establish a modified method for optimizing outlet boundary conditions (BC) of computed tomography-derived fractional flow reserve (CT-FFR), considering the myocardium as a growth space for microcirculation. The feasibility and diagnostic performance of the modified method in stable coronary artery disease (CAD) were compared with invasive fractional flow reserve (FFR).

METHODS

Nineteen patients (19 lesions) underwent coronary computed tomography angiography (CCTA) and following invasive FFR were included. The microcirculation resistance model generated based on patient-specific anatomical structures and physiological principles was used as the outlet BC, considering the myocardium as a growth space. Brachial artery pressure (BAP) plus or minus 10 mmHg was used as the inlet pressure BC to investigate the effect of the circadian rhythm. After simulation, CT-FFR was compared with invasive FFR with a threshold of 0.80.

RESULTS

Compared with invasive FFR, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of CT-FFR with an optimal threshold of 0.80 were 100%, 100%, 100%, 100%, 100%, respectively. There were a good correlation and consistency between CT-FFR and invasive FFR. Little effect of the circadian fluctuation of BAP was found on the simulation.

CONCLUSIONS

A modified method for CT-FFR with high diagnostic accuracy compared with invasive FFR was established, considering the whole myocardial as the growth space for microcirculation. Circadian fluctuations in BAP could be ignored when it was used as the inlet BC.

Myocardial Perfusion Simulation for Coronary Artery Disease: A Coupled Patient-Specific Multiscale Model

Patient-specific models of blood flow are being used clinically to diagnose and plan treatment for coronary artery disease. A remaining challenge is bridging scales from flow in arteries to the micro-circulation supplying the myocardium. Previously proposed models are descriptive rather than predictive and have not been applied to human data. The goal here is to develop a multiscale patient-specific model enabling blood flow simulation from large coronary arteries to myocardial tissue. Patient vasculatures are segmented from coronary computed tomography angiography data and extended from the image-based model down to the arteriole level using a space-filling forest of synthetic trees. Blood flow is modeled by coupling a 1D model of the coronary arteries to a single-compartment Darcy myocardium model. Simulated results on five patients with non-obstructive coronary artery disease compare overall well to [\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{15}$$\end{document}15O]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {H}_{{2}}$$\end{document}H2O PET exam data for both resting and hyperemic conditions. Results on a patient with severe obstructive disease link coronary artery narrowing with impaired myocardial blood flow, demonstrating the model’s ability to predict myocardial regions with perfusion deficit. This is the first report of a computational model for simulating blood flow from the epicardial coronary arteries to the left ventricle myocardium applied to and validated on human data.