Near Real-time Adaptive Isotropic and Anisotropic Image-to-mesh Conversion for Numerical Simulations Involving Cerebral Aneurysms
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911300869685248 |
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| author | Garner, Kevin Drakopoulos, Fotis Sadasivan, Chander Chrisochoides, Nikos |
| author_facet | Garner, Kevin Drakopoulos, Fotis Sadasivan, Chander Chrisochoides, Nikos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13222 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Near Real-time Adaptive Isotropic and Anisotropic Image-to-mesh Conversion for Numerical Simulations Involving Cerebral Aneurysms Garner, Kevin Drakopoulos, Fotis Sadasivan, Chander Chrisochoides, Nikos Fluid Dynamics Distributed, Parallel, and Cluster Computing Graphics Mathematical Software Numerical Analysis 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. |
| title | Near Real-time Adaptive Isotropic and Anisotropic Image-to-mesh Conversion for Numerical Simulations Involving Cerebral Aneurysms |
| topic | Fluid Dynamics Distributed, Parallel, and Cluster Computing Graphics Mathematical Software Numerical Analysis |
| url | https://arxiv.org/abs/2412.13222 |