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A mechanics-based model for predicting flexible needle bending with large curvature in soft tissue.
Zhao, Yan-Jiang; Jin, Ye-Xin; Wen, Chao; Zhang, Yong-De; Zhang, He.
  • Zhao YJ; Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, Heilongjiang, 150080, China. Electronic address: zhaoyj@hrbust.edu.cn.
  • Jin YX; Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, Heilongjiang, 150080, China.
  • Wen C; Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, Heilongjiang, 150080, China; College of Artificial Intelligence, Nankai University, Tianjin, 300350, China.
  • Zhang YD; Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, Heilongjiang, 150080, China.
  • Zhang H; Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, Heilongjiang, 150080, China.
Med Eng Phys ; 126: 104156, 2024 04.
Article en En | MEDLINE | ID: mdl-38621852
ABSTRACT
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.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Agujas Idioma: En Año: 2024 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Agujas Idioma: En Año: 2024 Tipo del documento: Article