Interactive Visual Similarity Analysis of Measured and Simulated Multi-field Tubular Flow Ensembles

Abstract

Tubular flow analysis plays an important role in many fields, such as for blood flow analysis in medicine, e.g., for the diagnosis of cardiovascular diseases and treatment planning. Phase-contrast magnetic resonance imaging (PC-MRI) allows for noninvasive in vivo-measurements of such tubular flow, but may suffer from imaging artifacts. New acquisition techniques (or sequences) that are being developed to increase image quality and reduce measurement time have to be validated against the current clinical standard. Computational Fluid Dynamics (CFD), on the other hand, allows for simulating noise-free tubular flow, but optimization of the underlying model depends on multiple parameters and can be a tedious procedure that may run into local optima. Data assimilation is the process of optimally combining the data from both PC-MRI and CFD domains. We present an interactive visual analysis approach to support domain experts in the above-mentioned fields by addressing PC-MRI and CFD ensembles as well as their combination. We develop a multi-field similarity measure including both scalar and vector fields to explore common hemodynamic parameters, and visualize the evolution of the ensemble similarities in a low-dimensional embedding. Linked views to spatial visualizations of selected time steps support an in-detail analysis of the spatio-temporal distribution of differences. To evaluate our system, we reached out to experts from the PC-MRI and CFD domains and summarize their feedback.