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An unsupervised learning-based guidewire shape registration for vascular intervention surgery robot.

Yueling Liu1, Zhi Hu2

  • 1School of Electronics and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China.

Computer Methods in Biomechanics and Biomedical Engineering
|July 22, 2024
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Summary

This study introduces an unsupervised learning method for guidewire shape registration in vascular interventional surgery robots. The technique enhances master-slave system transparency and operational reliability by accurately tracking guidewire deformations.

Keywords:
VISRguidewire deformationkirchhoff modelmaster-slave systemunsupervised learning

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Area of Science:

  • Medical Robotics
  • Surgical Navigation
  • Computational Mechanics

Background:

  • Vascular interventional surgery robots (VISR) require high transparency for precise guidewire manipulation.
  • Guidewire deformation during procedures can reduce master-slave system accuracy and transparency.
  • Accurate registration of the guidewire's shape is crucial for effective robotic surgery.

Purpose of the Study:

  • To develop an unsupervised learning-based guidewire shape registration (UL-GSR) method.
  • To accurately estimate geometric transformations of the guidewire by learning displacement field functions.
  • To improve the transparency and operational reliability of VISR systems.

Main Methods:

  • Analysis of guidewire deformation using the Kirchhoff model.
  • Implementation of an unsupervised learning approach for guidewire shape registration.
  • Estimation of geometric transformations through learning displacement field functions.

Main Results:

  • The UL-GSR method achieves high registration accuracy for flexible bodies.
  • The method demonstrates robustness across various guidewire shape complexities.
  • Significant improvement in shape point set registration accuracy between master and slave sides was observed.

Conclusions:

  • The proposed UL-GSR method effectively addresses guidewire deformation challenges in VISR.
  • Enhanced registration accuracy leads to improved master-slave system transparency.
  • The UL-GSR method contributes to increased operational reliability in robotic vascular interventions.