This paper presents the computational modeling of a variety of flow-diverting stents, deployed in a number of patient-specific aneurysm geometries. We consider virtual device deployment and hemodynamics as well as thrombus formation, with the scope to assess pre-operatively the efficacy of specific devices in treating particular aneurysms. An algorithm based on a linear and torsional spring analogy is developed for the fast virtual deployment of stents and similar minimally invasive devices in patient-specific vessel geometries. The virtual deployment algorithm is used to accurately deploy a total of four stent designs in three aneurysm geometries. A variety of different flow-diverting stent designs, representing the commercially available and the entirely novel, are presented, varying in both mesh design and porosity. Transient computational hemodynamics simulations are performed on multiple patient-specific geometries to predict the reduction in aneurysm inflow after the deployment of each device. Further, a thrombosis initiation and growth model is implemented, coupled with the hemodynamic computations. Hemodynamic simulations show large variations in flow reduction between devices and across different aneurysm geometries. The industry standard of flow-diverters with 70% porosity, assumed to offer the best compromise in flexibility and flow reduction, is challenged in at least one aneurysm geometry.

This paper presents a methodology for modeling the deployment of implantable devices used in minimally invasive vascular interventions. Motivated by the clinical need to perform preinterventional rehearsals of a stent deployment, we have developed methods enabling virtual device placement inside arteries, under the constraint of real-time application. This requirement of rapid execution narrowed down the search for a suitable method to the concept of a dynamic mesh. Inspired by the idea of a mesh of springs, we have found a novel way to apply it to stent modeling. The experiments conducted in this paper investigate properties of the stent models based on three different spring types: lineal, semitorsional, and torsional springs. Furthermore, this paper compares the results of various deployment scenarios for two different classes of devices: a stent graft and a flow diverter. The presented results can be of a high-potential clinical value, enabling the predictive evaluation of the outcome of a stent deployment treatment.

In this paper, we perform a comparative analysis between two computational methods for virtual stent deployment: a novel fast virtual stenting method, which is based on a spring–mass model, is compared with detailed finite element analysis in a sequence of in silico experiments. Given the results of the initial comparison, we present a way to optimise the fast method by calibrating a set of parameters with the help of a genetic algorithm, which utilises the outcomes of the finite element analysis as a learning reference. As a result of the calibration phase, we were able to substantially reduce the force measure discrepancy between the two methods and validate the fast stenting method by assessing the differences in the final device configurations.

Bifurcation aneurysms account for a large fraction of cerebral aneurysms and often present morphologies that render traditional endovascular treatments, such as coiling, challenging and problematic. Flow-diverter stents offer a potentially elegant treatment option for such aneurysms, but clinical use of these devices remains controversial. Specifically, the deployment of a flow-diverter device in a bifurcation entails jailing one or more potentially vital vessels with a low-porosity mesh designed to restrict the flow. When multiple device placement configurations exist, the most appropriate clinical decision becomes increasingly opaque. In this study, three bifurcation aneurysm geometries were virtually treated by flow-diverter device. Each aneurysm was selected to offer two possible device deployment positions. Flow-diverters similar to commercially available designs were deployed with a fast-deployment algorithm before transient and steady state computational fluid dynamics simulations were performed. Reductions in aneurysm inflow, mean wall shear stress and maximum wall shear stress, all factors often linked with aneurysm treatment outcome, were compared for different device configurations in each aneurysm. In each of the three aneurysms modelled, a particular preferential device placement was shown to offer superior performance with the greatest reduction in the flow metrics considered. In all the three aneurysm geometries, substantial variations in inflow reduction (up to 25.3%), mean wall shear stress reduction (up to 14.6%) and maximum wall shear stress reduction (up to 12.1%) were seen, which were all attributed to device placement alone. Optimal device placement was found to be non-trivial and highly aneurysm specific; in only one-third of the simulated geometries, the best overall performance was achieved by deploying a device in the daughter vessel with the highest flow rate. Good correspondence was seen between transient results and steady state computations that offered a significant reduction in simulation run time. If accurate steady state computations are combined with the fast-deployment algorithm used, the modest run time and corresponding hardware make a virtual treatment pipeline in the clinical setting a meaningful possibility.