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Modeling, simulation, and optimal initiation planning for needle insertion into the liver.

R Sharifi Sedeh1, M T Ahmadian, F Janabi-Sharifi

  • 1Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. rsharifi@mit.edu

Journal of Biomechanical Engineering
|April 15, 2010
PubMed
Summary

This study introduces a faster, cost-effective simulation and planning system (SPS) for needle insertion into the liver. It uses interpolation models to significantly reduce computation time, enabling real-time surgical planning and improving safety.

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Area of Science:

  • Computational mechanics
  • Surgical simulation
  • Medical device development

Background:

  • Needle insertion simulation and planning systems (SPSs) are crucial for reducing complications from soft tissue procedures.
  • Current SPS development faces challenges due to the high computational demands of finite element (FE) models.
  • Improving computational speed is essential for clinical feasibility while maintaining realistic tissue deformation modeling.

Purpose of the Study:

  • To develop a cost-effective SPS platform for needle insertions into the liver.
  • To address computational limitations of FE models for real-time simulation.
  • To create a system capable of modeling large, velocity-dependent tissue deformations.

Main Methods:

  • A hyperviscoelastic model was developed for liver tissue FE modeling, accommodating large and velocity-dependent deformations.

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  • Material constants were determined using a genetic algorithm on bovine liver compression data.
  • B-spline interpolations of FE model data were used to create predictive models for puncture time and needle-tissue interaction points.

    • Main Results:

    • The proposed SPS framework achieved real-time performance, exceeding haptic rates (>500 Hz).
    • Computational time for needle tip node updates was drastically reduced from 177 seconds to 0.8069 milliseconds.
    • Planning accuracy was acceptable, with root-mean-square and maximum errors of 1 mm and 1.2 mm, respectively, at moderate resolution levels.

    Conclusions:

    • The interpolation-based models significantly enhance computational speed for needle insertion simulation and planning.
    • This approach provides a cost-effective platform for liver needle insertion planning.
    • The methodology is adaptable for other surgical simulation applications.