ArticleFrontiers in bioengineering and biotechnology2025
Enhancing cardiac assessments: accurate and efficient prediction of quantitative fractional flow reserve.
Article in Frontiers in bioengineering and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Clinical and Computational Analysis of Left Subclavian Artery Coverage on High-Risk Blunt Thoracic Aortic Injury.Journal of clinical medicine · 2026Article
- Fluid-Structure Interaction Analysis of Hyoid Bone-Induced Compression on Carotid Artery Hemodynamics.Biomedical engineering and computational biology · 2026Article
- The Impact of Stenosis Severity on Hemodynamic Parameters in the Iliac Artery: A Fluid-Structure Interaction Study.Bioengineering (Basel, Switzerland) · 2025Article
- Machine learning-based prediction of hemodynamic parameters in left coronary artery bifurcation: A CFD approach.Heliyon · 2025Article
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Authors and funding
9 authors.
Funding
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Abstract
Background: Obstruction within the left anterior descending coronary artery (LAD) is prevalent, serving as a prominent and independent predictor of mortality. Invasive Fractional flow reserve (FFR) is the gold standard for Coronary Artery Disease risk assessment. Despite advances in computational and imaging techniques, no definitive methodology currently assures clinicians of reliable, non-invasive strategies for future planning. Method: The present research encompassed a cohort of 150 participants who were admitted to the Rajaie Cardiovascular, Medical, and Research Center. The method includes a three-dimensional geometry reconstruction, computational fluid dynamics simulations, and methodology optimization for the computation time. Four patients are analyzed within this study to showcase the proposed methodology. The invasive FFR results reported by the clinic have validated the optimized model. Results: The computational FFR data derived from all methodologies are compared with those reported by the clinic for each case. The chosen methodology has yielded virtual FFR values that exhibit remarkable proximity to the clinically reported patient-specific FFR values, with the MSE of 6.186e-7 and R2 of 0.99 (p = 0.00434). Conclusion: This approach has shown reliable results for all 150 patients. The results are both computationally and clinically user-friendly, with the accumulative pre and post-processing time of 15 min on a desktop computer (Intel i7 processor, 16 GB RAM). The proposed methodology has the potential to significantly assist clinicians with diagnosis.
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