Document Type

Article

Publication Date

2026

DOI

10.1007/s00366-026-02287-4

Publication Title

Engineering with Computers

Volume

42

Issue

2

Pages

45

Abstract

This paper presents two performance optimization techniques for a mesh adaptation method that is designed to help streamline the discretization of complex vascular geometries within the numerical modeling process. This method is integrated into a pipeline with an image-to-mesh conversion tool to 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. 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. While utilizing a new user-defined sizing function, we also show an adaptive isotropic method that generates meshes with good quality and fidelity 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 min.

Rights

© 2026 The Authors.

This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. 

Data Availability

Article states: "No datasets were generated or analysed during the current study."

Original Publication Citation

Garner, K., Sadasivan, C., & Chrisochoides, N. (2026). Near real-time adaptive isotropic and anisotropic image-to-mesh conversion for cerebral aneurysm simulations. Engineering with Computers, 42(2), Article 45. https://doi.org/10.1007/s00366-026-02287-4

ORCID

0000-0003-4138-1017 (Garner), 0000-0003-3088-0187 (Chrisochoides)

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