A mechanics-based model for predicting flexible needle bending with large curvature in soft tissue

作者：Yan-Jiang Zhao, Ye-Xin Jin, Chao Wen, Yongde Zhang, He Zhang · 发表于：Medical Engineering & Physics · 年份：2024 · DOI：10.1016/j.medengphy.2024.104156 · 被引用次数：10 · 研究领域：Soft Robotics and Applications、Robot Manipulation and Learning、Teleoperation and Haptic Systems

Percutaneous insertion is one of the most common minimally invasive procedures. Compared with traditional straight rigid needles, bevel-tipped flexible needle can generate curved trajectories to avoid obstacles and sensitive organs. However, the nonlinear large deflection problem challenges the bending prediction of the needle, which dramatically influences the surgical success rate. This paper analyzed the mechanism of needle-tissue interaction, and established a mechanics-based model of the needle bending during an insertion. And then, a discretization of the bending model was adopted to accurately predict the large bending of the needle in soft tissue. Insertion experiments were conducted to validate the bending prediction model. The results showed that the large needle bending was predicted with the mean/RMSE/maximumu error of 0.42 mm / 0.26 mm / 1.08 mm, which was clinically acceptable. This proved the rationality and accuracy of the proposed model.