尸体痉挛
医学
生物医学工程
曲折
冲程(发动机)
可视化
医学物理学
放射科
计算机科学
外科
人工智能
机械工程
多孔性
岩土工程
工程类
作者
Yang Liu,Mehdi Abbasi,Jorge Arturo Larco,Ramanathan Kadirvel,David F. Kallmes,Waleed Brinjikji,Luis Savastano
标识
DOI:10.1136/neurintsurg-2020-017133
摘要
Preclinical testing platforms have been instrumental in the research and development of thrombectomy devices. However, there is no single model which fully captures the complexity of cerebrovascular anatomy, physiology, and the dynamic artery-clot-device interaction. This article provides a critical review of phantoms, in-vivo animal, and human cadaveric models used for thrombectomy testing and provides insights into the strengths and limitations of each platform. Articles published in the past 10 years that reported thrombectomy testing platforms were identified. Characteristics of each test platform, such as intracranial anatomy, artery tortuosity, vessel friction, flow conditions, device-vessel interaction, and visualization, were captured and benchmarked against human cerebral vessels involved in large-vessel occlusion stroke. Thrombectomy phantoms have been constructed from silicone, direct 3D-printed polymers, and glass. These phantoms represent oversimplified patient-specific cerebrovascular geometry but enable adequate visualization of devices and clots under appropriate flow conditions. They do not realistically mimic the artery-clot interaction. For the animal models, arteries from swine, canines, and rabbits have been reported. These models can reasonably replicate the artery-clot-device interaction and have the unique value of evaluating the safety of thrombectomy devices. However, the vasculature geometries are substantially less complex and flow conditions are different from human cerebral arteries. Cadaveric models are the most accurate vascular representations but with limited access and challenges in reproducibility of testing conditions. Multiple test platforms should be likely used for comprehensive evaluation of thrombectomy devices. Interpretation of the testing results should take into consideration platform-specific limitations.
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