Path planning algorithm for percutaneous puncture lung mass biopsy procedure based on the multi-objective constraints...

Area of Science:

• Medical Imaging and Interventional Radiology
• Computational Optimization
• Surgical Planning

Background:

• Percutaneous puncture lung mass biopsy using CT images is standard but has drawbacks.
• Traditional procedures are time-consuming, risk complications, and involve significant radiation exposure.
• Surgeon experience heavily influences the success of traditional lung biopsy procedures.

Purpose of the Study:

• To develop an optimized pathway selection model for percutaneous puncture lung mass biopsy.
• To reduce procedure time, complications, and radiation exposure.
• To decrease reliance on surgeon expertise in surgical path planning.

Main Methods:

• A multi-constrained objective optimization model based on clinical criteria was proposed.
• A fuzzy optimization-based multidimensional spatial Pareto front algorithm was developed for path selection.
• Optimal paths were visualized on 3D images for surgical planning.

Main Results:

• The algorithm's performance was evaluated using 25 datasets.
• Prospective and retrospective experiments showed 92% of optimal paths met clinical needs.
• The proposed algorithm demonstrated superior performance compared to existing methods.

Conclusions:

• The algorithm innovates mass target point selection and integrates clinical constraints with multi-objective optimization.
• It offers a more clinically feasible pathway, reducing surgeon dependency on experience.
• This approach enhances the safety and efficiency of lung mass biopsies.