Real-Time Sensitivity Analysis of Blood Flow Simulations to Lumen Segmentation Uncertainty

Patient-specific modeling of blood flow combining CT image data and computational fluid dynamics has significant potential for assessing the functional significance of coronary artery disease. An accurate segmentation of the coronary arteries, an essential ingredient for blood flow modeling methods, is currently attained by a combination of automated algorithms with human review and editing. However, not all portions of the coronary artery tree affect blood flow and pressure equally, and it is of significant importance to direct human review and editing towards regions that will most affect the subsequent simulations. We present a data-driven approach for real-time estimation of sensitivity of blood-flow simulations to uncertainty in lumen segmentation. A machine learning method is used to map patient-specific features to a sensitivity value, using a large database of patients with precomputed sensitivities. We validate the results of the machine learning algorithm using direct 3D blood flow simulations and demonstrate that the algorithm can predict sensitivities in real time with only a small reduction in accuracy as compared to the 3D solutions. This approach can also be applied to other medical applications where physiologic simulations are performed using patient-specific models created from image data.