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Title: Predicting Needle Deflection in Soft Tissue: a Computational Modeling Approach
Abstract This study explores the mechanical interactions between surgical needles and soft tissues during procedures like biopsies and brachytherapy. A key challenge is needle tip deflection, which can cause deviation from the intended target. The study aims to develop an analytical model that predicts needle tip deflection during insertion by combining principles from interfacial mechanics and soft tissue deformation. A modified version of the dynamic Euler-Bernoulli beam theory is employed to model needle insertion and predict needle tip deflection. The model’s predictions are then compared to experimental data obtained from needle insertions in real tissues. The research aims to deepen our understanding of needle-tissue interactions and develop a reliable model for predicting needle deflection, ultimately enhancing surgical robots and navigation systems for safer and more precise percutaneous procedures. Pig organs are used as a material data source for a viscoelastic model, simulating needle insertion into kidney-like environments and analyzing organ deformation. The modified Euler-Bernoulli beam theory considers the viscoelastic properties of the tissue. Deflection is then calculated and compared to experimental data, with analytical deflection measurements exhibiting a 5–10% difference compared to experimental results.  more » « less
Award ID(s):
1917711
PAR ID:
10494842
Author(s) / Creator(s):
;
Publisher / Repository:
American Society of Mechanical Engineers
Date Published:
Journal Name:
Proceedings of the ASME 2023 International Mechanical Engineering Congress and Exposition
ISBN:
978-0-7918-8762-2
Format(s):
Medium: X
Location:
New Orleans, Louisiana, USA
Sponsoring Org:
National Science Foundation
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