Needle deflection prediction using adaptive slope model

É. Dorilêo, N. Zemiti, P. Poignet
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引用次数: 6

Abstract

Thin and long (semi-rigid) needles are well known to bend during percutaneous insertions because of needle-tissue interactions. Robotized needle insertions have been proposed to improve the efficacy of Interventional Radiology (IR) procedures such as radiofrequency ablation (RFA) of kidney tumors. However, the success of treatments and diagnosis depends on accurate prediction of needle deflection. This work aims to demonstrate the feasibility of merging needle-tissue properties, tip asymmetry and needle tip position updates to assist needle placement. In this paper we proposed a needle-tissue interaction model that matches the observations of transversal and axial resultant forces acting in the system. Analysis of a slope parameter between needle and tissue provides online and offline needle deflections predictions. Online updates of the needle tip position allow adaptive corrections of the slope parameter. Moreover, promising results were observed while evaluating the model's performance under uncertainties conditions such as tissue deformation, tissue inhomogeneity, needle-tissue friction, topological changes of the tissue and other modeling approximations. The system is evaluated by experiments in soft (homogeneous) PVC and multilayer tissue phantoms. Experiment results of needle placement into soft tissues presented average error of 1.04 mm. Meanwhile, online corrections decreased the error of offline predictions of 25%. The system shows an encouraging ability to predict semi-rigid needle deflection during interactions with elastic medium.
基于自适应斜率模型的针挠度预测
众所周知,由于针与组织的相互作用,细而长的(半刚性)针在经皮插入过程中会弯曲。机器人针头插入已被提出用于提高介入放射学(IR)手术的疗效,如肾肿瘤的射频消融(RFA)。然而,治疗和诊断的成功取决于准确预测针头偏转。这项工作旨在证明合并针组织特性,针尖不对称和针尖位置更新以辅助针头放置的可行性。在本文中,我们提出了一个针-组织相互作用模型,该模型与在系统中作用的横向和轴向合力的观察结果相匹配。分析针和组织之间的斜率参数提供在线和离线针挠度预测。针尖位置的在线更新允许对斜率参数进行自适应修正。此外,在不确定条件下,如组织变形、组织不均匀性、针与组织摩擦、组织拓扑变化和其他建模近似情况下,对模型的性能进行了评估,结果令人满意。通过软(均质)PVC和多层组织模型的实验对该系统进行了评价。针刺入软组织实验结果平均误差为1.04 mm。同时,在线修正将离线预测的误差降低了25%。该系统显示出令人鼓舞的预测半刚性针挠度与弹性介质相互作用的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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