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Purpose <p>Traditional surgical robot system relying on computed tomography (CT) navigation suffers from two drawbacks during abdominal organ puncture surgeries. Firstly, the puncture target is displaced under the influence of respiration, thereby reducing the puncture accuracy. Secondly, the puncture process lacks real-time visualization, which may potentially give rise to medical accidents. This paper presents a semi-automatic surgical robot system based on the fusion guidance of real-time ultrasound images and CT, along with the monitoring of the patient’s respiratory state, to address these issues.</p> Method <p>This system&#xa0;utilizes a six-axis force sensor in contact with the human body, and a respiratory model can be constructed through data got from force sensor to monitor the patient’s respiratory phase and recommend the optimal puncture phase. The issue of non-real-time puncture guidance is addressed through the real-time registration and fusion of ultrasound (US) images with preoperative CT images.</p> Results <p>Phantom experiments and animal experiments were carried out based on this design. The test results indicate that in these two experiments, the average fusion error between US and CT of the main tissues in the liver is within 3&#xa0;mm. For puncture accuracy, in the phantom experiment, the average puncture error was 1.0 mm, with a minimum of 0 mm and a maximum of 2.1 mm. In the animal experiment, the average puncture error was 2.5 mm, ranging from a minimum of 1.6 mm to a maximum of 3.0 mm.</p> Conclusion <p>The results of two experiments show both the image fusion accuracy and puncture accuracy of this system are within 3mm, which can meet the requirement of 5&#xa0;mm puncture accuracy in clinical practice. Approximately 70% of the operation are automatically accomplished by robot system, greatly reducing the reliance on the doctor’s experience.</p>

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Semi-automatic puncture robotic system based on real-time multi-modal image fusion: preclinical evaluation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月14日

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  • Bo Zhang,
  • Kui Chen,
  • Yuhang Yao,
  • Bo Wu,
  • Qiang Li,
  • Zheming Zhang,
  • Peihua Fan,
  • Wei Wang,
  • Manxia Lin,
  • Xiang Jing,
  • Shigeki Sugano,
  • Masakatsu G. Fujie,
  • Ming Kuang

摘要

Purpose

Traditional surgical robot system relying on computed tomography (CT) navigation suffers from two drawbacks during abdominal organ puncture surgeries. Firstly, the puncture target is displaced under the influence of respiration, thereby reducing the puncture accuracy. Secondly, the puncture process lacks real-time visualization, which may potentially give rise to medical accidents. This paper presents a semi-automatic surgical robot system based on the fusion guidance of real-time ultrasound images and CT, along with the monitoring of the patient’s respiratory state, to address these issues.

Method

This system utilizes a six-axis force sensor in contact with the human body, and a respiratory model can be constructed through data got from force sensor to monitor the patient’s respiratory phase and recommend the optimal puncture phase. The issue of non-real-time puncture guidance is addressed through the real-time registration and fusion of ultrasound (US) images with preoperative CT images.

Results

Phantom experiments and animal experiments were carried out based on this design. The test results indicate that in these two experiments, the average fusion error between US and CT of the main tissues in the liver is within 3 mm. For puncture accuracy, in the phantom experiment, the average puncture error was 1.0 mm, with a minimum of 0 mm and a maximum of 2.1 mm. In the animal experiment, the average puncture error was 2.5 mm, ranging from a minimum of 1.6 mm to a maximum of 3.0 mm.

Conclusion

The results of two experiments show both the image fusion accuracy and puncture accuracy of this system are within 3mm, which can meet the requirement of 5 mm puncture accuracy in clinical practice. Approximately 70% of the operation are automatically accomplished by robot system, greatly reducing the reliance on the doctor’s experience.