  The emergence of computational fluid dynamics (CFD) enabled the simulation of
intricate transport processes, including flow in physiological structures, such
as blood vessels. While these so-called hemodynamic simulations offer
groundbreaking opportunities to solve problems at the clinical forefront, a
successful translation of CFD to clinical decision-making is challenging.
Hemodynamic simulations are intrinsically complex, time-consuming, and
resource-intensive, which conflicts with the time-sensitive nature of clinical
workflows and the fact that hospitals usually do not have the necessary
resources or infrastructure to support CFD simulations. To address these
transfer challenges, we propose a novel visualization system which enables
instant flow exploration without performing on-site simulation. To gain
insights into the viability of the approach, we focus on hemodynamic
simulations of the carotid bifurcation, which is a highly relevant arterial
subtree in stroke diagnostics and prevention. We created an initial database of
120 high-resolution carotid bifurcation flow models and developed a set of
similarity metrics used to place a new carotid surface model into a
neighborhood of simulated cases with the highest geometric similarity. The
neighborhood can be immediately explored and the flow fields analyzed. We found
that if the artery models are similar enough in the regions of interest, a new
simulation leads to coinciding results, allowing the user to circumvent
individual flow simulations. We conclude that similarity-based visual analysis
is a promising approach toward the usability of CFD in medical practice.
