The aim of this work is to set a finite element model of a Ni-Ti self-knotting suture, designed for deep neurosurgical applications, and to provide a reliable tool for evaluating its functionality before entering clinical practice. A closed shape is memorized through specific heat treatments; once implanted in the open configuration the surgeon does not need to pack any knots since the suture closes by itself only using some drops of physiological solution. A material user-subroutine allows the modeling of the shape memory effect which governs its functionality. Experiments of increasing complexities, involving shape-recovery behavior, are designed to validate the model. Indeed, accordingly with the most recent Standards, validation is the fundamental step for allowing the use of numerical models for predicting device performance and taking a decision on the design that may affect patient safety or health. Herein, once validated, the numerical approach is used for investigating the effects of the device preparatory phase on the clinical performance.

这项工作的目的是为深度神经外科应用设计镍钛自打结缝合线的有限元模型，并为进入临床实践之前提供评估其功能的可靠工具。封闭的形状通过特定的热处理来记忆。一旦植入开放结构，外科医生就无需打结，因为缝合线仅需使用几滴生理溶液即可自行闭合。材质用户子例程允许对形状记忆效果进行建模，从而控制其功能。设计了不断增加的复杂性（涉及形状恢复行为）的实验，以验证模型。实际上，根据最新的标准，验证是允许使用数值模型来预测设备性能并就可能影响患者安全或健康的设计做出决定的基本步骤。在此，一旦通过验证，就可以使用数值方法来研究设备准备阶段对临床性能的影响。