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The Quantification of Injectability by Mechanical Testing
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Simulation and experimental studies in needle-tissue interactions.

Bardia Konh1,2, Mohammad Honarvar1, Kurosh Darvish1

  • 1Department of Mechanical Engineering, Temple University, 1947 N. 12th Street, Philadelphia, PA, 19122, USA.

Journal of Clinical Monitoring and Computing
|July 20, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel needle insertion simulation for predicting needle deflection in soft tissue, enhancing percutaneous procedures through virtual path planning and training for improved patient safety.

Keywords:
Needle insertion testsNeedle-tissue interactionsSurgical needle insertion simulation

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Area of Science:

  • Medical Simulation
  • Biomechanical Engineering
  • Computational Mechanics

Background:

  • Percutaneous needle-based procedures like brachytherapy and thermal ablation require precise needle navigation.
  • Current methods face challenges in predicting needle-tissue interactions and steering behavior.
  • Minimizing complications necessitates advanced simulation tools for virtual path planning and operator training.

Purpose of the Study:

  • To develop and validate a new simulation model for predicting bevel-tip needle deflection in soft tissue.
  • To investigate the influence of needle geometry (diameter, bevel angle) on steering behavior.
  • To provide a foundational model for virtual path planning and training in medical procedures.

Main Methods:

  • Utilized the Arbitrary-Lagrangian-Eulerian (ALE) formulation in LS-DYNA software.
  • Modeled solid-fluid interactions between the needle and soft tissue, accounting for large deformation and fracture.
  • Validated the simulation against three experimental needle steering cases in a soft phantom.

Main Results:

  • The simulation accurately predicted needle deflection with less than 10% error compared to experimental data.
  • Investigated the impact of needle diameter and bevel tip angle on the final needle shape.
  • Demonstrated that thinner needles facilitate a smaller radius of curvature for maneuvering.

Conclusions:

  • The developed needle insertion simulation offers a validated and accurate approach for predicting needle-tissue interactions.
  • The model provides insights into optimizing needle design and insertion strategies for complex anatomical navigation.
  • This simulation serves as a valuable tool for virtual path planning and training, potentially reducing clinical complications.