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Fractional Flow Reserve (FFR) is a widely used metric to quantify the functional significance of stenoses in coronary arteries. FFR is the ratio of pressure before and after a stenosis and is measured using a transducer during coronary catheterization. To avoid unnecessary catheterization, many analytical and data-driven models based on non-invasive imaging have been proposed for FFR estimation. In this study, we construct physics-informed analytical models and datadriven machine learning models for FFR estimation based on CT-derived information. All four models require simple information about suspect stenoses, offering rapid, practical approaches for functional assessment. The four models we study are: (1) a patient-specific blood flow informed pressure drop model based on Navier-Stokes equations, (2) a purely geometric model based on stenosis area reduction, (3) a Gaussian process regression model trained on patient specific stenosis geometry and blood flow data, and (4) a Gaussian process regression model trained only on patient specific stenosis geometry. The models were developed and tested using a simulation study with ground truth FFR values from computational fluid dynamics analysis of blood flow through a population of stenosed arteries. In total, 60 different stenosis conditions based on known prevalence were simulated with a range of measurement errors leading to 10000 data sets. The RMSE of the model estimates for approach 1 through 4 are, 0.19, 0.45, 0.06, 0.14. The flow informed machine learning model leads to ~1% lower bias and ~12% lower variance than the flow informed analytical model. Considering the improved variance performance, the machine learning models likely outperform analytic expressions because they learn optimal regression associations robust to noise. This work suggests that machine learned approaches may be superior to conventional analytic expressions for FFR estimation, particularly when inputs contain realistic measurement error.
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