High-precision surgical navigation using speckle structured light-based thoracoabdominal puncture robot
<|reference_start|>High-precision surgical navigation using speckle structured light-based thoracoabdominal puncture robot: Abstract   Background During percutaneous puncture robotic surgical navigation, the needle insertion point is positioned on the patient's chest and abdomen body surface. By locating any point on the soft skin tissue, it is difficult to apply the traditional reflective ball tracking method. The patient's chest and abdomen body surface has fluctuations in breathing and appears irregular. The chest and abdomen are regular and smooth, lacking obvious features, and it is challenging to locate the needle insertion point on the body surface. Methods This paper designs and experiments a method that is different from previous reflective ball optical markers or magnetic positioning surgical navigation and tracking methods. It is based on a speckle structured light camera to identify the patient's body surface and fit it into a hollow ring with a diameter of 24mm. Results Experimental results show that this method of the system can be small, flexible, and high-precision positioning of any body surface point at multiple angles, achieving a positioning accuracy of 0.033-0.563mm and an image of 7-30 frames/s. Conclusions The positioning recognition ring material used in this method can be well imaged under CT, so the optical positioning of the body surface and the in vivo imaging positioning under CT can be combined to form a unified patient's internal and external positioning world coordinates to achieve internal and external registration. Positioning integration. The system senses motion with six degrees of freedom, up and down, front and back, left and right, and all rotations, with sub-millimeter accuracy, and has broad application prospects in future puncture surgeries.<|reference_end|>
@article{guo2024high-precision,
    title={High-precision surgical navigation using speckle structured light-based
  thoracoabdominal puncture robot},
    author={Zezhao Guo, Yanzhong Guo, Zhanfang Zhao},
    journal={arXiv preprint arXiv:2406.06478},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06478},
    primaryClass={cs.RO cs.AI}
}