Presented are two techniques that are designed to help streamline the discretization of complex vascular geometries within the numerical modeling process. The first method integrates multiple software tools into a single pipeline which can generate adaptive anisotropic meshes from segmented medical images. The pipeline is shown to satisfy quality, fidelity, smoothness, and robustness requirements while providing near real-time performance for medical image-to-mesh conversion. The second method approximates a user-defined sizing function to generate adaptive isotropic meshes of good quality and fidelity in real-time. Tested with two brain aneurysm cases and utilizing up to 96 CPU cores within a single, multicore node on Purdue University's Anvil supercomputer, the parallel adaptive anisotropic meshing method utilizes a hierarchical load balancing model (designed for large, cc-NUMA shared memory architectures) and contains an optimized local reconnection operation that performs three times faster than its original implementation from previous studies. The adaptive isotropic method is shown to generate a mesh of up to approximately 50 million elements in less than a minute while the adaptive anisotropic method is shown to generate approximately the same number of elements in about 5 minutes.

翻译：本文提出了两种旨在简化复杂血管几何结构在数值建模过程中离散化的技术。第一种方法将多个软件工具集成至单一流程中，能够从分割后的医学图像生成自适应各向异性网格。该流程在满足质量、保真度、平滑性和鲁棒性要求的同时，实现了医学图像到网格转换的近实时性能。第二种方法通过逼近用户定义的尺寸函数，实时生成具有良好质量和保真度的自适应各向同性网格。在普渡大学Anvil超级计算机的单多核节点上使用最多96个CPU核心对两个脑动脉瘤案例进行测试，并行自适应各向异性网格生成方法采用了分层负载均衡模型（专为大规模cc-NUMA共享内存架构设计），并包含优化的局部重连接操作，其执行速度较先前研究中的原始实现提升三倍。自适应各向同性方法可在不到一分钟内生成约5000万个单元的网格，而自适应各向异性方法生成相近数量单元网格约需5分钟。