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Real-time 3D US-CT fusion-based semi-automatic puncture robot system: clinical evaluation.

Masayuki Nakayama1,2, Bo Zhang3, Ryoko Kuromatsu4

  • 1Graduate School of Creative Science and Engineering, Waseda University, Tokyo, 1698555, Japan. m_nakayama@akane.waseda.jp.

International Journal of Computer Assisted Radiology and Surgery
|August 5, 2025
PubMed
Summary

This study introduces a semi-automatic robotic system integrating ultrasound and CT images for safer percutaneous radiofrequency ablation (PRFA) puncture. The system demonstrated accurate navigation, with all centroid distances within the 5mm ablation margin in a pilot clinical trial.

Keywords:
Blood vessel segmentationFusion imagingRobotic puncture systemSurgical robot

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Area of Science:

  • Medical robotics
  • Image-guided therapy
  • Surgical navigation

Background:

  • Percutaneous radiofrequency ablation (PRFA) faces challenges in safe and accurate puncture due to image limitations and organ displacement.
  • Existing systems struggle with precision, impacting treatment efficacy and patient safety.

Purpose of the Study:

  • To evaluate a novel semi-automatic robotic puncture system integrating real-time ultrasound (US) and computed tomography (CT) images.
  • To assess the system's clinical usefulness and accuracy in guiding PRFA procedures through a pilot study.

Main Methods:

  • Development of an improved U-net model for image analysis, validated with a 0.87 Dice coefficient.
  • Integration of the AI model into a robotic system for semi-automatic puncture guidance.
  • Conducting a pilot clinical experiment with five participants using the integrated US-CT system.

Main Results:

  • The 3D US-CT fusion process evaluated centroid distances with an average of 1.97 mm (range: 0.38–4.81 mm).
  • All measured centroid distances were within the 5.00 mm ablation margin required for PRFA.

Conclusions:

  • The robotic system shows potential for accurate puncture navigation in PRFA, meeting critical safety margins.
  • The AI model demonstrated potential generalization performance, adaptable to variations in US images and patient anatomy.