Two-phase computerized planning of cryosurgery using bubble-packing and force-field analogy

Area of Science:

• Medical Engineering
• Computational Biology
• Surgical Planning

Background:

• Cryosurgery involves tissue destruction via freezing, commonly used in prostate cancer treatment.
• Current cryosurgery planning relies on surgeon experience, lacking standardized optimization for cryoprobe arrangement.
• Minimally invasive cryosurgery utilizes needle-shaped cryoprobes, where optimal placement is critical for procedure success.

Purpose of the Study:

• To develop an automated, computerized technique for optimizing cryosurgery planning.
• To improve the overall quality and efficiency of cryosurgery procedures.
• To reduce the reliance on subjective surgeon experience for cryoprobe placement.

Main Methods:

• A two-phase optimization approach was employed, integrating prior research.
• Phase I utilized a bubble-packing method, adapted from finite element meshing techniques.
• Phase II incorporated a force-field analogy method for robust optimization.

Main Results:

• Proof-of-concept demonstrated on a 2D prostate cross-section model.
• Validated that cryosurgery planning can be achieved without costly bioheat transfer simulations.
• Phase I of the method proved effective for planning.

Conclusions:

• The developed automated planning method drastically reduces runtime from hours to minutes.
• This efficiency makes automated cryosurgery planning feasible within clinical time constraints.
• The study establishes a practical, faster approach to surgical planning for cryosurgery.