Advances in three-dimensional coronary imaging and computational fluid dynamics: is virtual fractional flow reserve more than just a pretty picture?

Fast Computational Approaches to Derive Fractional Pressure Ratio in Patients with Extracranial or Intracranial Symptomatic Stenosis

OBJECTIVE

We aimed to evaluate the performance of fast and straightforward Murray law-based quantitative flow ratio (μQFR) computation in cerebrovascular stenosis.

METHODS

A total of 30 patients with symptomatic stenosis of 50%-70% luminal stenosis and underwent fractional pressure ratio (FPR) assessment at our hospital were included in the present study. μQFR was applied to the interrogated vessel. An artificial intelligence algorithm was proposed for automatic delineation of lumen contours of cerebrovascular stenosis. We used invasive FPRs as a reference standard. Pearson's correlation coefficient (r) was used to assess the correlation strength between the μQFR and FPR, and Bland-Altman plots were used to evaluate the agreement between the μQFR and FPR. An analysis of the receiver operating characteristic was used to evaluate the performance of μQFR.

RESULTS

Our results displayed a strong positive correlations (r = 0.92; P < 0.001) between the μQFR and pressure wire FPR. Excellent agreement was observed between the μQFR and FPR with a mean difference of 0.01 ± 0.08 (range, -0.16 to 0.14; P = 0.263). The overall accuracy for identifying an FPR of ≤0.7 was 92% (95% confidence interval [CI], 85%-100%). The area under the receiver operating characteristic curve was higher for the μQFR (0.92; 95% CI, 0.81-0.98) than for diameter stenosis (0.88; 95% CI, 0.75-0.95). The positive likelihood ratio was 3.9 for the μQFR with a negative likelihood ratio of 0.

CONCLUSIONS

The μQFR computation has a strong correlation and agrees with the FPR calculated from the pressure wire. Therefore, the μQFR might provide an essential therapeutic aid for patients with symptomatic stenosis.

Digital Subtraction Angiography Contrast Material Transport as a Direct Assessment for Blood Perfusion of Middle Cerebral Artery Stenosis

Digital subtraction angiography (DSA) is a fluoroscopic technique used extensively in interventional radiology for visualizing blood vessels. It has also been used to evaluate blood perfusion. However, the perfusion obtained in previous techniques was extracted from signal intensity rather than by the transport of contrast material (CM) through blood flow. The main aim of this study is to evaluate the morphological effects on the hemodynamics and the CM concentration in the middle cerebral artery (MCA) stenosis. We proposed a quantitative parameter, i.e., contrast material remaining time (CMRT), to describe the variation in the transport of CM over time. Computational fluid dynamics simulations were performed on both reconstructive synthetic and patient-derived models. In the synthetic models, we evaluated the variation of flow patterns and the transport of CM with different degrees of stenosis and the location of the lesion. It was found that an increase in the degree of stenosis (from 30 to 80%) resulted in a significant increase in CMRT at the anterior cerebral artery (ACA) outlet (p = 0.0238) and a significant decrease in CMRT at the MCA outlet (p = 0.012). The patient-derived models were reconstructed from the pre- and post-interventional DSA images of a patient with MCA stenosis. Both blood flow velocity and CMRT increased at the ACA outlet but decreased at the MCA outlet. The perfusion analysis demonstrated that the perfusion function was improved after interventional surgery. In conclusion, changes in stenotic degree at MCA may lead to apparent differences in the hemodynamic distribution and the transport of CM. CMRT could be a quantitative indicator to evaluate the changes in blood perfusion after the intervention for MCA stenosis.

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

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

Diagnostic Accuracy of Fast Computational Approaches to Derive Fractional Flow Reserve From Diagnostic Coronary Angiography: The International Multicenter FAVOR Pilot Study

OBJECTIVES

The aim of this prospective multicenter study was to identify the optimal approach for simple and fast fractional flow reserve (FFR) computation from radiographic coronary angiography, called quantitative flow ratio (QFR).

BACKGROUND

A novel, rapid computation of QFR pullbacks from 3-dimensional quantitative coronary angiography was developed recently.

METHODS

QFR was derived from 3 flow models with: 1) fixed empiric hyperemic flow velocity (fixed-flow QFR [fQFR]); 2) modeled hyperemic flow velocity derived from angiography without drug-induced hyperemia (contrast-flow QFR [cQFR]); and 3) measured hyperemic flow velocity derived from angiography during adenosine-induced hyperemia (adenosine-flow QFR [aQFR]). Pressure wire-derived FFR, measured during maximal hyperemia, served as the reference. Separate independent core laboratories analyzed angiographic images and pressure tracings from 8 centers in 7 countries.

RESULTS

The QFR and FFR from 84 vessels in 73 patients with intermediate coronary lesions were compared. Mean angiographic percent diameter stenosis (DS%) was 46.1 ± 8.9%; 27 vessels (32%) had FFR ≤ 0.80. Good agreement with FFR was observed for fQFR, cQFR, and aQFR, with mean differences of 0.003 ± 0.068 (p = 0.66), 0.001 ± 0.059 (p = 0.90), and -0.001 ± 0.065 (p = 0.90), respectively. The overall diagnostic accuracy for identifying an FFR of ≤0.80 was 80% (95% confidence interval [CI]: 71% to 89%), 86% (95% CI: 78% to 93%), and 87% (95% CI: 80% to 94%). The area under the receiver-operating characteristic curve was higher for cQFR than fQFR (difference: 0.04; 95% CI: 0.01 to 0.08; p < 0.01), but did not differ significantly between cQFR and aQFR (difference: 0.01; 95% CI: -0.04 to 0.06; p = 0.65). Compared with DS%, both cQFR and aQFR increased the area under the receiver-operating characteristic curve by 0.20 (p < 0.01) and 0.19 (p < 0.01). The positive likelihood ratio was 4.8, 8.4, and 8.9 for fQFR, cQFR, and aQFR, with negative likelihood ratio of 0.4, 0.3, and 0.2, respectively.

CONCLUSIONS

The QFR computation improved the diagnostic accuracy of 3-dimensional quantitative coronary angiography-based identification of stenosis significance. The favorable results of cQFR that does not require pharmacologic hyperemia induction bears the potential of a wider adoption of FFR-based lesion assessment through a reduction in procedure time, risk, and costs.

Numerical and experimental investigations of the flow-pressure relation in multiple sequential stenoses coronary artery

Virtual fractional flow reserve (vFFR) has been evaluated as an adjunct to invasive fractional flow reserve (FFR) in the light of its operational and economic benefits. The accuracy of vFFR and the complexity of hyperemic flow simulation are still not clearly understood. This study investigates the flow–pressure relation in an idealised multiple sequential stenoses coronary artery model via numerical and experimental approaches. Pressure drop is linearly correlated with flow rate irrespective of the number of stenosis. Computational fluid dynamics results are in good agreement with the experimental data, demonstrating reasonable accuracy of vFFR. It was also found that the difference between data obtained with steady and pulsatile flows is negligible, indicating the steady flow may be used instead of pulsatile flow conditions in vFFR computation. This study adds to the current understanding of vFFR and may improve its clinical applicability as an adjunct to invasively determined FFR.