Virtual flow diverter deployment and embedding for hemodynamic simulations.

Flow-diverter stents offer clinicians an effective solution for treating intracranial aneurysms, especially in cases where other devices may be unsuitable. However, strongly deviating success rates among different centres, manufacturers, and aneurysm phenotypes highlight the need for better in-situ studies of these devices. To support research in this area, virtual stenting algorithms have been proposed that, combined with computational fluid dynamics, provide insights into the hemodynamic alterations induced by the device. Yet, many existing algorithms rely on uncertain parameters, such as the forces applied during operation, fail to predict the length of the device after deployment, or lack robust validation steps, raising concerns about their reliability. Therefore, we developed a robust deployment technique that builds upon the geometrical features of the vessel and includes advancements from previous works. The algorithm is detailed and validated against literature examples, in-vitro experiments, and patient data, achieving a mean angular error below 5° in the latter. Furthermore, we describe and demonstrate how the deployed device can be embedded in a computational mesh using open-source tools and anisotropic meshing routines.