Design, Analysis, and Optimization of a Novel Stent Retriever for Acute Ischemic Stroke

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Advances in Mechanism and Machine Science (IFToMM WC 2023)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 147))

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Abstract

Acute ischemic stroke (AIS) is a leading global cause of disability and death. Stent retrievers, which enable quick reperfusion of blocked arteries, have transformed the way that AIS is treated. The limitations of current stent retrievers, which have trouble expanding in tortuous arteries, prevent them from being used in patients with complex vascular anatomy. The design, mechanical analysis, and optimization of a novel self-expanding stent-retriever for the treatment of AIS are presented in this paper. The mechanical capabilities of the stent retriever are investigated using finite element analysis. In the course of the contraction process, strain and radial reaction forces are analyzed, with the impacts of different geometrical parameters being explored. In the bent condition of the stent, the flexibility and ability to maintain shape are assessed. The findings show that the geometric parameters had an impact on the stent's strain and that the new stent retriever had better flexibility and shape-preservation capabilities than a commercial device. Additionally, stent optimization is carried out, which reduced the level of local stress. These results show the potential of the innovative stent retriever to enhance the management of AIS and have significant implications for the design of stent retrievers.

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Acknowledgement

This research has been supported by the National Natural Science Foundation of China under Grant 52075016, U20A20281, 52222501. The authors gratefully acknowledge the supporting agencies.

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Correspondence to Shengnan Lyu .

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Wu, S., Wang, B., Zhang, T., Lyu, S. (2023). Design, Analysis, and Optimization of a Novel Stent Retriever for Acute Ischemic Stroke. In: Okada, M. (eds) Advances in Mechanism and Machine Science. IFToMM WC 2023. Mechanisms and Machine Science, vol 147. Springer, Cham. https://doi.org/10.1007/978-3-031-45705-0_14

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