Key result
Novel non-invasive CT-FFR framework matches invasive FFR with ~4% average relative error.
Why the study?
Invasive FFR measurement entails substantial costs and clinical challenges, making non-invasive alternatives based on CT imaging computational modeling highly desirable.
Does a non-invasive computational fluid dynamics framework accurately predict invasive fractional flow reserve in patients with left coronary artery stenosis?
Observational (n=12)
No
Does a non-invasive computational fluid dynamics framework accurately predict invasive fractional flow reserve in patients with left coronary artery stenosis?
A novel non-invasive computational fluid dynamics framework accurately predicts fractional flow reserve in left coronary arteries, showing high correlation with invasive measurements and potential for on-site clinical use.
Answers are grounded in this paper and cite their sources.
CT-based computational FFR modeling may reduce need for invasive assessment; leaves open prospective validation before clinical adoption.
Introduction Coronary artery disease (CAD) remains the leading global cause of death. Hemodynamic assessment is typically performed using fractional flow reserve (FFR); however, its invasive nature entails substantial costs and clinical challenges. Non-invasive alternatives are therefore highly desirable. Advances in cardiac imaging, particularly computed tomography (CT), now provide detailed coronary data that can serve as the foundation for computational modeling. This research proposes and validates a tool to numerically predict FFR in patient-specific LCA 3D models segmented from CT scans. Methods Using CFD in ANSYS ® Fluent, the developed tool employs physiologically informed boundary conditions, a Womersley velocity profile at the inlet and a three-element Windkessel model at the outlets, alongside a simplified Phan–Thien Tanner (sPTT) viscoelastic rheology model for blood. Simulations were conducted under hyperemic conditions to align with how the FFR is currently measured. The non-invasive FFR predictions on 12 patients were compared against both invasive gold standards and commercial HeartFlow ® data (Mountain View, CA, USA). Results and discussion The numerical results showed a remarkable correlation with invasive measurements (R 2 = 0.978) and a low average relative error of 3.86% ± 2.01%. Additionally, Bland-Altman analysis indicated high diagnostic precision with a negligible mean bias of −0.015. These metrics suggest improved stability compared to HeartFlow ® , which showed higher variability (20.84% ± 34.79%) in the cohort of this study. These results are promising; however, given the limited cohort size (12 patients), they should be interpreted as a preliminary proof-of-concept validation rather than a definitive clinical benchmark. The findings of this study suggest that the developed numerical tool has the potential to approximate hyperemic coronary dynamics in a way that is close to the real physiology. Moreover, the developed framework has the potential to be used on-site in medical facilities without costs to assess the functional severity of stenoses and aid the diagnosis of CAD. Larger multi-center clinical trials are required to fully establish the accuracy and generalizability of this tool.
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Fernandes et al. (2026) conducted an observational in Coronary artery disease (n=12). Non-invasive FFR prediction framework vs. Invasive FFR was evaluated on Correlation with invasive FFR measurements (R2 = 0.978). The non-invasive FFR prediction framework demonstrated a remarkable correlation with invasive measurements (R2 = 0.978) and a low average relative error of 3.86%.
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