Uncertainty Analysis for Blood Flow Simulation with Image Segmentation and Numerical Inputs

Abstract Image-based computational hemodynamics (ICH) employs medical imaging data to model and simulate patient-specific blood flow. Uncertainties arising from image noise, segmentation inaccuracies, and boundary condition modeling can significantly affect simulation outcomes. This study uses a case study to demonstrate the impact of image segmentation uncertainty and outlet boundary condition variability in ICH of blood flow within a human iliac arterial system reconstructed from CT angiography. To address challenges from high dimensionality and limited image data, the Uncertainty Separation Method is applied to decompose the simulation model into image segmentation and numerical sub-models, enabling uncertainty estimations at the average 3D shape and mean numerical inputs. An alignment method is introduced to compute the average 3D anatomy from multiple segmented samples. Results show that this alignment method is essential for statistical analysis of image-based vascular shapes, and that image uncertainty, especially with limited samples, strongly influences the simulation outcomes.