The first virtual patient-specific thrombectomy procedure

Giulia Luraghi, Sara Bridio, Jose Felix Rodriguez Matas, Gabriele Dubini, Nikki Boodt, Frank J.H. Gijsen, Aad van der Lugt, Behrooz Fereidoonnezhad, Kevin M. Moerman, Patrick McGarry, Praneeta R. Konduri, Nerea Arrarte Terreros, Henk A. Marquering, Charles B.L.M. Majoie, Francesco Migliavacca*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

28 Citations (Scopus)
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Treatment of acute ischemic stroke has been recently improved with the introduction of endovascular mechanical thrombectomy, a minimally invasive procedure able to remove a clot using aspiration devices and/or stent-retrievers. Despite the promising and encouraging results, improvements to the procedure and to the stent design are the focus of the recent efforts. Computational studies can pave the road to these improvements, providing their ability to describe and accurately reproduce a real procedure. A patient with ischemic stroke due to intracranial large vessel occlusion was selected and after the creation of the cerebral vasculature from computed tomography images and a histologic analysis to determine the clot composition, the entire thrombectomy procedure was virtually replicated. As in the real situation, the computational replica showed that two attempts were necessary to remove the clot, as a result of the position of the stent retriever with respect to the clot. Furthermore, the results indicated that clot fragmentation did not occur as the deformations were mainly in a compressive state without the possibility for clot cracks to propagate. The accurate representation of the procedure can be used as an important step for operative optimization planning and future improvements of stent designs.

Original languageEnglish
Article number110622
JournalJournal of Biomechanics
Publication statusPublished - 20 Sept 2021

Bibliographical note

Funding Information:
This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 777072 and N. Arrarte Terreros also received funding from the AMC medical Research BV, Amsterdam UMC, location AMC, under project No 21937.

Funding Information:
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [HAM reports co-founder and shareholder of Nico.lab, a company that focuses on the use of artificial intelligence for medical image analysis. CBLMM received funds from the European Commission (related to this project, paid to institution); and from CVON/Dutch Heart Foundation, Stryker, TWIN Foundation, Health Evaluation Program Netherlands (unrelated; all paid to institution). CBLMM is shareholder of Nico.lab, a company that focuses on the use of artificial intelligence for medical imaging analysis]

Publisher Copyright:
© 2021 Elsevier Ltd


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