Session: 07-08-04: Computational Modeling in Biomedical Applications

Paper Number: 191557

Curved Needle Insertion in a Tissue-Mimicking Phantom: a Simulation and Experimental Study 

Precise needle placement is essential in minimally invasive procedures such as biopsy, brachytherapy, and targeted drug delivery, where accurate targeting directly affects clinical outcomes. During insertion into soft tissue, needles often deviate from their intended trajectory due to complex interactions between the needle geometry, tissue deformation, and frictional forces at the needle-tissue interface. Curved needles have been investigated as a method to improve needle steering and enable navigation around anatomical obstacles. Despite these advances, predicting the trajectory of curved needles within deformable tissue remains challenging due to large tissue deformation and nonlinear contact interactions. Computational models capable of accurately predicting needle deflection and final tip location can provide valuable insight for needle design and trajectory planning. This study presents a finite element modeling framework to predict the tip location of curved needles during linear insertion into soft tissue. The numerical model is implemented using an explicit dynamic with a Coupled Eulerian-Lagrangian (CEL) approach to capture large deformation of the surrounding tissue and complex needle-tissue interaction. In this work, the needle is modeled as a deformable Lagrangian body, while the surrounding soft tissue is represented using a Eulerian domain, allowing the tissue material to flow around the needle shaft during insertion. This approach mitigates excessive mesh distortion commonly encountered in large deformation simulations using purely Lagrangian domains. The tissue is modeled as a linear elastic material with a Young’s modulus of 23.3 kPa, obtained from independent compression tests performed on the tissue-mimicking gel used in the experiments. Needle-tissue contact is defined using a general contact with tangential penalty friction. The friction coefficient is experimentally estimated from insertion tests using the 3D-printed needle and tissue-mimicking gel used in the validation experiments. Incorporating experimentally measured material properties and friction parameters improves the physical realism of the needle-tissue interaction representation within the simulation. The model is applied to curved needles with bent angles of 7.5°,15°, and 22.5°. Simulations are performed to predict needle deflection and resulting needle tip trajectories during insertion. The predicted trajectories are compared with experimentally measured needle paths obtain through video-based tracking of the needle tip during insertion experiments. The simulated trajectories closely match the experimentally observed trajectories for all needle geometries throughout most of the insertion process, demonstrating the ability of the model to capture the curvature and overall trend of the needle deflection during insertion. The results demonstrate that the proposed finite element modeling can capture key aspects of curved needle-tissue interaction and provide realistic predictions of needle trajectories under linear insertion. By combining experimentally measured material properties and friction parameters with a CEL formulation capable of handling large tissue deformation, the model provides a physically informed representation of needle insertion mechanics. The proposed computational approach offers a useful tool for understanding curved needle behavior in soft tissue and may contribute to the design and optimization of steerable surgical needles for minimally invasive procedures.

Presenting Author: Doyoung Kim Temple University

Presenting Author Biography: Doyoung Kim is a PhD candidate in the Department of Mechanical Engineering at Temple University. Her research focuses on the mechanics of medical devices for minimally invasive procedures, with an emphasis on needle-tissue interaction and curved needle insertion. Her work combines experimental studies and computational modeling to understand needle trajectory, insertion forces, and tissue deformation. She received her BS and MA degrees in Mechanical Engineering from Drexel University in 2020.

Authors:

Doyoung Kim Temple University
Tarun Podder SUNY Upstate Medical University
Parsaoran Hutapea Temple University