JOURNAL ARTICLE

Efficiency of endovascular coiling on the evolution of MCA cerebral aneurysm by hemodynamic analysis: Computational study.

  • Published In: International Journal of Modern Physics C: Computational Physics & Physical Computation, 2026, v. 37, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Ali, Rifaqat; Hassan, Hana Ihsan; Sharma, Aman; Dhawan, Aashim; Sharma, Prabhat; Taher, Waam Mohammed; Alwan, Mariem; Al-Hussainy, Ali Fawzi; Mushtaq, Hiba; Heaie, T. 3 of 3

Abstract

In this paper, the change in the hemodynamic factors associated with the rupture risk in the process of aneurysm growth is fully investigated. The main focus of this computational work is to reveal the changes in the blood flow parameters of wall shear stress (WSS), oscillatory shear index (OSI) and pressure to offer good insight into the selection of an efficient technique for the treatment. This work also examines the efficacy of coiling in the treatment of the middle cerebral artery (MCA) aneurysm in different stages of the growth process. The blood flow is modeled by solving the Navier–Stokes equation with the non-Newtonian Casson model. The results of the hemodynamic analyses show that the growth of the MCA aneurysm would reduce the WSS over the sac surface while the evolution of the aneurysm increases the OSI value. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics C: Computational Physics & Physical Computation. 2026/02, Vol. 37, Issue 2, p1
  • Document Type:Article
  • Subject Area:Anatomy and Physiology
  • Publication Date:2026
  • ISSN:0129-1831
  • DOI:10.1142/S0129183125500652
  • Accession Number:189477026
  • Copyright Statement:Copyright of International Journal of Modern Physics C: Computational Physics & Physical Computation is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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