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.

翻译：计算流体动力学（CFD）模拟可从血管几何结构中获取关于血流的宝贵信息。然而，这需要从低分辨率医学图像中提取精确的动脉模型，这仍然具有挑战性。基于中心线的表示方法广泛用于对包含小血管的大型血管网络进行建模，因为它同时编码了几何和拓扑信息，并便于手动编辑。在本工作中，我们提出了一种自动方法，可直接从中心线生成适用于CFD的结构化六面体网格。我们同时解决了建模和网格生成任务。我们提出了一种基于惩罚样条的血管模型，以克服中心线表示固有的局限性，如噪声和稀疏性。分叉点使用基于解剖学的参数化模型进行重构，该模型被扩展至平面多分叉结构。最后，我们开发了一种方法，从所提出的血管网络模型生成具有结构化、六面体且流向导向单元的体网格。与现有先进方法相比，所提出的方法对中心线的常见缺陷具有更好的鲁棒性，并提高了网格质量。由于该方法仅依赖中心线，可轻松编辑血管模型，以研究血管几何结构与拓扑对血流动力学的影响。我们通过对包含60个脑血管网络的数据集进行完整网格生成来证明方法的效率。尽管输入数据具有挑战性，但92%的血管和83%的分叉点生成的网格无需手动干预即可避免缺陷。源代码已公开发布。