| description abstract | Whether treated surgically or with endovascular techniques, large and giant cerebral aneurysms are particularly difficult to treat. Nevertheless, high porosity stents can be used to accomplish stentassisted coiling and even standalone stentbased treatments that have been shown to improve the occlusion of such aneurysms. Further, stent assisted coiling can reduce the incidence of complications that sometimes result from embolic coiling (e.g., neck remnants and thromboembolism). However, in treating cerebral aneurysms at bifurcation termini, it remains unclear which configuration of high porosity stents will result in the most advantageous hemodynamic environment. The goal of this study was to compare how three different stent configurations affected fluid dynamics in a large patientspecific aneurysm model. Three common stent configurations were deployed into the model: a halfY, a fullY, and a crossbar configuration. Particle image velocimetry was used to examine posttreatment flow patterns and quantify rootmeansquared velocity magnitude (VRMS) within the aneurysmal sac. While each configuration did reduce VRMS within the aneurysm, the fullY configuration resulted in the greatest reduction across all flow conditions (an average of 56% with respect to the untreated case). The experimental results agreed well with clinical follow up after treatment with the fullY configuration; there was evidence of thrombosis within the sac from the stents alone before coil embolization was performed. A computational simulation of the fullY configuration aligned well with the experimental and in vivo findings, indicating potential for clinically useful prediction of posttreatment hemodynamics. This study found that applying different stent configurations resulted in considerably different fluid dynamics in an anatomically accurate aneurysm model and that the fullY configuration performed best. The study indicates that knowledge of how stent configurations will affect posttreatment hemodynamics could be important in interventional planning and demonstrates the capability for such planning based on novel computational tools. | |
