Computational fluid dynamics (CFD) simulation provides valuable information
on blood flow from the vascular geometry. However, it requires extracting
precise models of arteries from low-resolution medical images, which remains
challenging. Centerline-based representation is widely used to model large
vascular networks with small vessels, as it encodes both the geometric and
topological information and facilitates manual editing. In this work, we
propose an automatic method to generate a structured hexahedral mesh suitable
for CFD directly from centerlines. We addressed both the modeling and meshing
tasks. We proposed a vessel model based on penalized splines to overcome the
limitations inherent to the centerline representation, such as noise and
sparsity. The bifurcations are reconstructed using a parametric model based on
the anatomy that we extended to planar n-furcations. Finally, we developed a
method to produce a volume mesh with structured, hexahedral, and flow-oriented
cells from the proposed vascular network model. The proposed method offers
better robustness to the common defects of centerlines and increases the mesh
quality compared to state-of-the-art methods. As it relies on centerlines
alone, it can be applied to edit the vascular model effortlessly to study the
impact of vascular geometry and topology on hemodynamics. We demonstrate the
efficiency of our method by entirely meshing a dataset of 60 cerebral vascular
networks. 92% of the vessels and 83% of the bifurcations were meshed without
defects needing manual intervention, despite the challenging aspect of the
input data. The source code is released publicly