  Simulation-based imaging (SBI) is a blood flow imaging technique that
optimally fits a computational fluid dynamics (CFD) simulation to
low-resolution, noisy magnetic resonance (MR) flow data to produce a
high-resolution velocity field. In this work, we study the effectivity of SBI
in predicting wall shear stress (WSS) relative to standard magnetic resonance
imaging (MRI) postprocessing techniques using two synthetic numerical
experiments: flow through an idealized, two-dimensional stenotic vessel and a
model of an adult aorta. In particular, we study the sensitivity of these two
approaches with respect to the Reynolds number of the underlying flow, the
resolution of the MRI data, and the noise in the MRI data. We found that the
SBI WSS reconstruction: 1) is insensitive to Reynolds number over the range
considered (Re $\leq$ 1000), 2) improves as the amount of MRI data increases
and provides accurate reconstructions with as little as three MRI voxels per
diameter, and 3) degrades linearly as the noise in the data increases with a
slope determined by the resolution of the MRI data. We also consider the
sensitivity of SBI to the resolution of the CFD mesh and found there is
flexibility in the mesh used for SBI, although the WSS reconstruction becomes
more sensitive to other parameters, particularly the resolution of the MRI
data, for coarser meshes. This indicates a fundamental trade-off between scan
time (i.e., MRI data quality and resolution) and reconstruction time using SBI,
which is inherently different than the trade-off between scan time and
reconstruction quality observed in standard MRI postprocessing techniques.
