Uncertainty quantification of computational coronary stenosis assessment and model based mitigation of image resolution limitations

Coronary artery disease is one of the leading causes of death globally. The hallmark of this disease is the occurrence of stenosed coronary arteries which reduce blood flow to the myocardium. Severely stenosed arteries can be treated if detected, but the diagnostic procedure to assess fractional flow reserve (FFR), a quantitative measure of stenosis severity, is invasive, burdensome to the patient, and costly. Recent computational approaches estimate the severity of stenoses from simulations of coronary blood flow based on CT imagery. These methods allow for diagnosis to be made noninvasively and using fewer hospital resources; however, the predictions depend on uncertain input data and model parameters due to technical limitations and patient variability. To assess the consequences of boundary condition and input uncertainty on predictions of FFR, we developed a model of coronary blood flow. We performed uncertainty quantification and sensitivity analysis of the predictions based on uncertainties in boundary conditions, parameters, and geometric measurements. Our results identified three influential sources of uncertainty: geometric data, cardiac output, and coronary resistance during hyperemia. Further, uncertainty about the geometry of the stenosed coronary branch influences estimates much more than other geometrical data. Limitations of medical imaging contribute uncertainty to predictions as vessels below a certain threshold remain unobserved. We assessed the effects of unobserved vessels by comparing predictions based on both high and low resolution data. Moreover, we introduced a novel method that estimates flow distribution while accounting for unobserved vessels. This method improved FFR predictions in the cases considered by 50% on average