Closing the Gap between Reality and CFD Simulations of FFR with Techniques to Quantify and Reduce Uncertainty of Predictions
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Coronary artery disease has been reported the most common cause of death worldwide and is an ever-growing problem for global health. Recently, diagnosis of coronary artery disease from CT angiography (CTA) and computational fluid dynamics (CFD), CT-FFR, has emerged as a promising non-invasive alternative to the conventional clinical procedure. The aim of this thesis is to quantify and reduce the uncertainty of CT-FFR. In particular, we focus on uncertainties from the interaction between CFD and the coronary physiology. First, we use a lumped-element model to investigate the uncertainty and sensitivity of CT-FFR to physiological parameters. Second, we perform an in-depth investigation of the governing physiological model for flow distribution in CT-FFR simulations, Murray's law. Third, we propose a new model to reduce the inaccuracies from CTA-invisible coronary arteries. Our results show that uncertainty in physiological parameters has a significant effect on FFR estimates. Moreover, we find that CTA-invisible coronary arteries greatly increase the uncertainty of FFR, but that this effect can be reduced with improved mathematical modeling. Last, we find that CT-FFR is highly sensitive to post-stenotic flow outlets, which highlights the clinical importance of post-stenotic CTA image quality.