CFD-based analysis of calcium channel blocker–induced vessel dilation and coiling in middle cerebral artery aneurysm management
This study presents a computational fluid dynamics (CFD) investigation of the hemodynamic effects of calcium channel blocker (CCB)–induced vessel dilation and endovascular coiling on a patient-specific middle cerebral artery (MCA) aneurysm. A high-quality unstructured mesh with boundary layer refinement was generated and grid convergence index (GCI) analysis confirmed mesh independence with errors below 5%. Simulations were conducted for six scenarios: without/with coiling treatment, combined with initial vessel size, 10% CCB-induced vessel dilation and 20% dilation. Hemodynamic parameters, including wall shear stress (WSS), average WSS (AWSS), oscillatory shear index (OSI), pressure distribution, pressure drop, iso-velocity surfaces and flow streamlines, were evaluated over the cardiac cycle. Results showed that CCB-induced vessel dilation reduces high WSS regions, peak pressures and vortex intensity, with the most significant improvements at 20% dilation. Coiling treatment further diminished WSS, AWSS and OSI values while substantially suppressing intra-aneurysmal flow recirculation and pressure peaks. The combination of CCB treatment and coiling produced the most favorable hemodynamic environment, characterized by uniformly low WSS, reduced oscillatory loading and minimal high-pressure zones. These findings indicate that integrating pharmacological vasodilation with mechanical flow diversion may synergistically reduce rupture risk and improve clinical outcomes in MCA aneurysm management.
Authors
- Qiao Wang (ORCID: https://orcid.org/0000-0003-0648-9399)
- Fanghui Li (ORCID: https://orcid.org/0000-0001-6580-3726)
- Khalil Hajlaoui (ORCID: https://orcid.org/0000-0002-9077-0820)
Institutions
- Universiti Sains Malaysia (MY)
- Imam Mohammad ibn Saud Islamic University (SA)
- Pingxiang University (CN)
Publication Details
- Journal
- International Journal of Modern Physics C
- Published
- 2025-10-16
- DOI
- https://doi.org/10.1142/s0129183126500531
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00