Development of a patient-specific cerebral vasculature fluid–structure-interaction model. (March 2022)

• Record Type: Journal Article
• Authors: Moerman, Kevin M.; Konduri, Praneeta; Fereidoonnezhad, Behrooz; Marquering, Henk; van der Lugt, Aad; Luraghi, Giulia; Bridio, Sara; Migliavacca, Francesco; Rodriguez Matas, Jose F.; McGarry, Patrick
• Is Part Of: Journal of biomechanics. Volume 133(2022)
• Publication Date: 2022-03
• Subjects: Image-based modelling -- Cerebral vessels -- Fluid Structure Interactions -- Hyperelastic -- Non-Newtonian Flow; Animal mechanics -- Periodicals; Biomechanics -- Periodicals; Biomechanics -- Periodicals; Mécanique animale -- Périodiques; Biomécanique -- Périodiques; Electronic journals; 571.4305
• DOI: 10.1016/j.jbiomech.2021.110896
• Languages: English
• ISSNs: 0021-9290

Abstract

Abstract: Development of in-silico models of patient-specific cerebral artery networks presents several significant technical challenges: (i) The resolution and smoothness of medical CT images are much lower than the required element/cell length for FEA/CFD/FSI models; (ii) contact between vessels, and indeed self contact of high tortuosity vessel segments are not clearly identifiable from medical CT images. Commercial model construction software does not provide customised solutions for such technical challenges, with the result that accurate, efficient and automated development of patient-specific models of the cerebral vessels is not facilitated. This paper presents the development of a customised and highly automated platform for the generation of high resolution patient-specific FEA/CFD/FSI models from clinical images. This platform is used to perform the first fluid–structure-interaction patient-specific analysis of blood flow and artery deformation of an occluded cerebral vessel. Results demonstrate that in addition to flow disruption, clot occlusion significantly alters the geometry and strain distribution in the vessel network, with the blocked M2 segment undergoing axial elongation. The new computational approach presented in this study can be further developed as a clinical diagnostic tool and as a platform for thrombectomy device design.