A Path Planning Strategy for Robotic Bronchoscopic Multi-Sample Biopsy
Qiqi Pan, Jingjing Luo, Yongfei Feng, Wenke Duan, Shijie Guo, Wang Hongbo
AI summary
Problem
Conventional bronchoscopic biopsies for multiple pulmonary nodules are inefficient and heavily dependent on operator skill, while existing path planning methods largely ignore bronchial anatomy and fail to optimize multi-target trajectories.
Approach
The bronchial tree is modeled as an electrical circuit where lesions act as resistive bulbs and airways as morphological resistors, transforming multi-target navigation into a minimum-resistance circuit optimization problem.
Key results
- Over 60% reduction in manipulator movement distance
- 76% decrease in overall operation time
- Over 40% efficiency improvement in multi-sample TBB procedures
- Automated route generation for both single and multiple lesion inspections
Why it matters
Improves the safety and efficiency of robotic bronchoscopic biopsies for clinicians and medical robotics developers managing multiple pulmonary nodules.
Abstract
Lung cancer is the leading cause of cancer death globally, and early diagnosis via transbronchial biopsy (TBB) improves outcomes. However, conventional bronchoscopes for multiple pulmonary nodules face inefficiencies and operator skill dependency. This paper proposes a path planning strategy for robotic bronchoscopic multi-sample TBB. It abstracts the bronchial tree as a circuit, with lesions as constant-resistance bulbs and bronchial branches as resistors with equivalent resis- tance based on their morphology. Multi-target path planning is transformed into minimizing total circuit resistance, opti- mizing trajectories to reduce redundant movements of robotic manipulators. Comparing to traditional methods, evaluations show that over 60% reduced movement distance and 76% less operation time are achieved; experiments accomplish over 40% efficiency improvement, enhancing multi-sample TBB efficiency and safety.