A 3D Structured Light Robot-Assisted System for CT-Guided Percutaneous Lung Targeting.

Area of Science:

• Medical Robotics
• Image-Guided Therapy
• Thoracic Surgery

Background:

• CT-guided percutaneous lung interventions face challenges with respiratory motion and geometric errors, impacting accuracy, efficiency, and radiation exposure.
• Accurate targeting is crucial for effective pulmonary interventions but is often compromised by patient movement.
• Existing methods require continuous intraoperative radiation, increasing patient and staff risk.

Purpose of the Study:

• To develop and validate a robot-assisted framework for accurate, phase-locked lung targeting in CT-guided interventions.
• To achieve precise targeting without relying on continuous intraoperative radiation.
• To improve the efficiency and safety of pulmonary interventions.

Main Methods:

• Developed a robot-assisted system integrating a 3D structured light scanner and a hybrid respiratory motion monitoring framework.
• Employed single-acquisition 3D surface registration, high-frame-rate monocular Perspective-n-Point (PnP) tracking, and Kalman filtering for phase-locked instrument insertion.
• Evaluated the system using phantom, animal, and clinical cohorts, comparing it to conventional freehand CT-guided procedures.

Main Results:

• Demonstrated sub-2 mm median registration accuracy and millimeter-scale targeting precision.
• Clinical comparison showed a 59% reduction in procedure time compared to conventional freehand methods.
• Significantly decreased radiation exposure in the robot-assisted group relative to the freehand workflow.

Conclusions:

• The robot-assisted system streamlines calibration, registration, respiratory tracking, and robotic execution for accurate, phase-locked targeting.
• Achieved improved procedural efficiency and safety in CT-guided pulmonary interventions.
• Provides a clinically viable solution that mitigates limitations of conventional methods, supporting standardized and safer image-guided targeting.