  Patient-specific hemodynamics assessment could support diagnosis and
treatment of neurovascular diseases. Currently, conventional medical imaging
modalities are not able to accurately acquire high-resolution hemodynamic
information that would be required to assess complex neurovascular pathologies.
Therefore, computational fluid dynamics (CFD) simulations can be applied to
tomographic reconstructions to obtain clinically relevant information. However,
three-dimensional (3D) CFD simulations require enormous computational resources
and simulation-related expert knowledge that are usually not available in
clinical environments. Recently, deep-learning-based methods have been proposed
as CFD surrogates to improve computational efficiency. Nevertheless, the
prediction of high-resolution transient CFD simulations for complex vascular
geometries poses a challenge to conventional deep learning models. In this
work, we present an architecture that is tailored to predict high-resolution
(spatial and temporal) velocity fields for complex synthetic vascular
geometries. For this, an octree-based spatial discretization is combined with
an implicit neural function representation to efficiently handle the prediction
of the 3D velocity field for each time step. The presented method is evaluated
for the task of cerebral hemodynamics prediction before and during the
injection of contrast agent in the internal carotid artery (ICA). Compared to
CFD simulations, the velocity field can be estimated with a mean absolute error
of 0.024 m/s, whereas the run time reduces from several hours on a
high-performance cluster to a few seconds on a consumer graphical processing
unit.
