Purpose Incomplete stent apposition after the treatment of brain aneurysm can increase the risk of thromboembolic complications and remains to be the major concern during the procedure. Utilizing the high resolution cone-beam CT (HR-CBCT) and metal artifact reduction software (MAR), the metal artifact produced by the coil mass is reduced and the visualization of the deployed stent is optimized. After combining with the 3D digital subtraction angiography (3D-DSA), the resulting image is used for the evaluation of the stent apposition in the artery. Initial clinical experience of this novel imaging method is reported.
Methods A total of 24 aneurysm patients who underwent the stent assisted coil embolization was selected for this study. All patients were treated using either Neuroform® stent or Enterprise® stent system. Artis PURE® Platform (Siemens) was used in this study. Acquisition protocols are follows. A HR-CBCT acquisition was performed to obtain the image of stent and coil mass. The dataset was then reconstructed using MAR. A 3 D DSA acquisition was performed for the visualization of the vasculature. The two datasets were combined using a dedicated software. A 3D volume rendering (VR) image was created and the stent apposition of each treated patient was evaluated.
Results All 24 patients underwent the image acquisition successfully. Relationship between the deployed stent and the wall of the parent artery was well visualized in every patient although partial image defect of the stent due to the metal artifact was observed in the relatively large aneurysms. The incomplete stent apposition was frequently seen near the carotid siphon, especially at the inner curve of the target vessel.
Conclusion Combination of high resolution cone-beam CT and 3D DSA for the evaluation of intracranial stents provided sufficient visualization of the deployed stent and parent artery. This imaging method can be used for the evaluation of stent apposition during/after the treatment of brain aneurysms.
Disclosures I. Yuki: 1; C; Siemens Grant. S. Hataoka: None. T. Ishibashi: 1; C; Siemens Grant. C. Dahmani: 5; C; Employee of Siemens Healthcare. A. Ikemura: None. Y. Kambayashi: None. I. Kan: None. Y. Abe: None. S. kaku: None. K. Nishimura: None. T. Kodama: None. Y. Sasaki: None. Y. Murayama: 1; C; Siemens Grant.
This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work noncommercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.