[A method of lung puncture path planning based on multi-level constraint]

Area of Science:

• Medical Imaging
• Computational Geometry
• Oncology

Background:

• Percutaneous pulmonary puncture guided by computed tomography (CT) is crucial for lung tissue acquisition and cancer diagnosis.
• Effective path planning is essential to mitigate puncture complications, patient discomfort, and mortality risks.

Purpose of the Study:

• To propose a novel multi-level constraint-based path planning method for CT-guided percutaneous lung puncture.
• To enhance the safety and efficacy of lung biopsy procedures.

Main Methods:

• A digital chest model was created from patient CT images.
• Fibonacci lattice sampling generated candidate puncture paths around the tumor.
• An optimal path was selected using a multi-level constraint strategy, incorporating oriented bounding box trees (OBBTree) and Pareto optimization, adhering to clinical guidelines.

Main Results:

• Simulation experiments validated the proposed path planning method.
• The method demonstrated effectiveness in avoiding physical and physiological barriers.
• The approach showed good performance in identifying safe and optimal puncture trajectories.

Conclusions:

• The developed path planning method aids physicians in selecting optimal puncture paths for lung biopsies.
• This computational approach can improve the safety and precision of CT-guided percutaneous pulmonary procedures.
• The method offers a valuable tool for reducing complications in lung cancer diagnosis and treatment.