ArticleComputer methods and programs in biomedicine2022
Real time reduced order model for angiography fractional flow reserve.
Article in Computer methods and programs in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Optimizing Non-invasive Fractional Flow Reserve Estimation with Machine Learning-Enhanced 1D Hemodynamic Modeling.Cardiovascular engineering and technology · 2026Article
- Developing a non-invasive diagnostic framework for the fractional flow reserve quantification in left coronary arteries: validation with patient cases.Frontiers in physiology · 2026Article
- Diagnostic Performance of Coronary Angiography Derived Computational Fractional Flow Reserve.Journal of the American Heart Association · 2024Article
- Impact of hypertension on coronary artery plaques and FFR-CT in type 2 diabetes mellitus patients: evaluation utilizing artificial intelligence processed coronary computed tomography angiography.Frontiers in artificial intelligence · 2024Article
- Current status and future perspectives of fractional flow reserve derived from invasive coronary angiography.Frontiers in cardiovascular medicine · 2023Review
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Authors and funding
4 authors.
Funding
Abstract
BACKGROUND AND
objectivesFractional flow reserve (FFR) is the gold standard for quantification of coronary stenosis and pressure wire is the gold standard for measuring FFR. Recently, computational fluid dynamics (CFD) methods have been used to compute FFR less invasively using images obtained from coronary angiography. This approach is, however, computationally intensive and solutions to reduce computation time are clearly required.
methodsWe hypothesized that FFR can be calculated instantly using a reduced order model (ROM) derived using response surface method (RSM) for simulation modeling in lieu of the computationally intensive CFD. Specifically, eleven physiological and anatomical factors known to affect FFR were selected as input variables, and Plackett-Burman analysis was performed in conjunction with CFD on model arteries to identify set of variables affecting FFR the most. Based on the Box-Behnken design, a mathematical model was developed to compute FFR using the retained set of variables.
resultsThe model fidelity was tested on a cohort of 90 patients (100 coronary arteries) with known pressure-wire FFR. FFR derived from this ROM had a strong correlation with pressure-wire FFR with sensitivity of 89.4%, specificity of 100% and area under curve of 0.947 (p < 0.05).
conclusionsThe ROM method can be used to reliably calculate FFR in patients with coronary stenosis and able to replace time-consuming CFD-based FFR estimation and provide instead a real-time calculation method.
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