基于中心线的脑动脉网络建模与六面体网格划分
Modeling and hexahedral meshing of cerebral arterial networks from centerlines
AI总结：
本研究提出一种直接从中心线生成适用于CFD的结构化六面体脑动脉网格的自动方法，通过惩罚样条血管模型、拓展的解剖参数化分叉模型提升鲁棒性与网格质量，在60例脑动脉数据集上验证了有效性且代码已开源。
摘要：
计算流体动力学（CFD）仿真可从血管几何结构中提供有价值的血流信息，但该过程需要从低分辨率医学图像中提取精确的动脉模型，这一工作目前仍具挑战性。基于中心线的表示方法因同时编码几何与拓扑信息且便于人工编辑，被广泛用于含细小血管的大型血管网络建模。本研究提出一种自动方法，可直接从中心线生成适用于CFD的结构化六面体网格，同时解决建模与网格划分两项任务。我们提出基于惩罚样条的血管模型，以克服中心线表示固有的噪声、稀疏性等局限；分叉结构则采用基于解剖学的参数化模型重建，并将该模型拓展至平面n分叉场景。最后，我们开发了一种方法，可基于所提出的血管网络模型生成具有结构化、六面体且沿血流方向单元的体网格。与现有最优方法相比，该方法对中心线常见缺陷的鲁棒性更强，且提升了网格质量。由于仅依赖中心线，该方法可轻松编辑血管模型，用于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划分，验证了方法的有效性：尽管输入数据存在挑战性，92%的血管与83%的分叉结构划分后无需要人工干预的缺陷。本研究的源代码已公开发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
