Effects of Image Uncertainty on Blood Flow Simulation

Abstract Image-based Computational Fluid Dynamics (ICFD) is widely applied for simulating and predicting patient-specific hemodynamics in human blood vessels. This study explores the impact of image input uncertainty from segmentation on ICFD simulation outputs for the iliac stenosis application for individual patients, using patients’ Computed Tomography Angiography imaging data. This investigation includes key components: first, we assess uncertainty in the patient’s three-dimensional (3D) vessel geometry extracted by three image segmentation platforms and multiple different operators, highlighting segmentation-induced variability. Secondly, ICFD simulations are performed to predict flow pressure fields based on random 3D shapes generated from segmentation, illustrating the impact of image uncertainty. To enable statistical image analysis, we develop an alignment method that computes the average 3D shape from a set of sample shapes from segmentation. By transforming complex 3D vessel shapes into coordinate vectors with automatic detection of correspondence points, this method enables the alignment and averaging of multiple 3D shapes. The resulting average 3D shape serves as input for ICFD simulation, generating a flow pressure field that can approximate the average prediction. The findings of this study demonstrate the effects of image uncertainty and the need for new methodologies to quantify these effects. The alignment method proves valuable for statistically analyzing random image data and 3D geometries, providing a useful tool for image statistical analysis.