Needle deflection prediction using adaptive slope model

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.