In vivo Flow Diverter Länge im Vergleich zu Simulation

Bei interventionellen Eingriffen besteht das Problem, dass sich die Länge intravaskulärer Implantate, insbesondere von Flow Divertern, oftmals nicht exakt voraussagen lässt. Dies kann dazu führen, dass sie Gefäße überdecken (und somit potentiell verschliessen) oder dass sie an ungünstigen Stellen wie in Gefäßkrümmungen enden. Um dieses relevante Problem auzugehen, wurde eine Software entwickelt, mit der sich anhand der Patienten-spezifischen Anatomie Implantatlagen simulieren lassen. Ziel des Projektes ist es, die Genauigkeit der Simulationen im klinischen Alltag zu prüfen.

Deployment of flow diverter devices: prediction of foreshortening and validation of the simulation in 18 clinical cases

Purpose: Flow diverter (FD) devices show severe shortening during deployment in dependency of the vessel geometry. Valid information regarding the geometry of the targeted vessel is therefore mandatory for correct device selection, and to avoid complications. But the geometry of diseased tortuous intracranial vessels cannot be measured accurately with standard methods.
The goal of this study is to prove the accuracy of a novel virtual stenting method in prediction of the behavior of a FD in an individual vessel geometry.
Methods: We applied a virtual stenting method on angiographic 3D imaging data of the specific vasculature of patients, who underwent FD treatment. The planning tool analyzes the local vessel morphology and deploys the FD virtually.We measured in 18 cases the difference between simulated FD length and real FD length after treatment in a landmark-based registration of pre-/ post-interventional 3D angiographic datasets.
Results: The mean value of length deviation of the virtual FD was 2.2 mm (SD ± 1.9 mm) equaling 9.5% (SD ± 8.2%). Underestimated cases present lower deviations compared with overestimated FDs. Flow diverter cases with a nominal device length of 20 mm had the highest prediction accuracy.
Conclusion: The results suggest that the virtual stenting method used in this study is capable of predicting FD length with a clinically sufficient accuracy in advance and could therefore be a helpful tool in intervention planning. Imaging data of high quality are mandatory, while processing and manipulation of the FD during the intervention may impact the accuracy.