Modeling and hexahedral meshing of cerebral arterial networks from centerlines

• URL: http://arxiv.org/abs/2201.08279v2
• Date: Tue, 13 Jun 2023 07:50:50 GMT
• Title: Modeling and hexahedral meshing of cerebral arterial networks from centerlines
• Authors: M\'eghane Decroocq, Carole Frindel, Pierre Roug\'e, Makoto Ohta and Guillaume Lavou\'e
• Abstract summary: Centerline-based representation is widely used to model large vascular networks with small vessels. We propose an automatic method to generate a structured hexahedral mesh suitable for CFD directly from centerlines. We demonstrate the efficiency of our method by entirely meshing a dataset of 60 cerebral vascular networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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