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A method for predicting needle insertion deflection in soft tissue based on cutting force identification.

Shan Jiang1, Yihan Gao1, Zhiyong Yang1

  • 1School of Mechanical Engineering, Tianjin University, Tianjin, China.

Computer Methods in Biomechanics and Biomedical Engineering
|August 5, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a new mechanical model for predicting flexible needle deflection in soft tissues during robotic surgery. The model simplifies needle-tissue interaction, avoiding complex material property measurements for improved accuracy.

Keywords:
Needle insertionbiopsy surgerydeflection estimationimage processingmechanical-based model

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Area of Science:

  • Robotics
  • Biomedical Engineering
  • Mechanical Engineering

Background:

  • Accurate modeling of flexible needle deflection is essential for robot-assisted percutaneous procedures like biopsies.
  • Existing models often require complex measurements of tissue properties (Young's modulus, Poisson's ratio).

Purpose of the Study:

  • To propose a novel mechanical model for predicting flexible needle deflection in soft tissues.
  • To develop a model that bypasses the need for invasive tissue property measurements.

Main Methods:

  • The model discretizes needle insertion into steps, treating the needle as a spring-supported cantilever beam.
  • Tissue stiffness is represented by virtual spring stiffness coefficients, identified through cutting force analysis.
  • The model was validated using puncture experiments in polyvinyl alcohol (PVA) gel.

Main Results:

  • The proposed model accurately predicts flexible needle deflection.
  • The average maximum error in deflection prediction ranged from 0.606 ± 0.167 mm to 1.005 ± 0.174 mm.
  • The model successfully predicted experimental results in PVA gel samples.

Conclusions:

  • The developed mechanical model offers a simplified yet accurate approach to predicting flexible needle deflection.
  • This method eliminates the need for complex tissue property measurements, facilitating practical application.
  • The findings support the development of advanced control strategies for robotic needle insertion systems.