Minimax optimization-based inverse treatment planning for interstitial thermal therapy

Area of Science:

• Biomedical Engineering
• Medical Physics
• Computational Biology

Background:

• Interstitial thermal therapy requires precise control of temperature distribution within tumors and surrounding tissues.
• Current treatment planning methods may not adequately account for uncertainties in tissue properties like blood perfusion.
• Establishing optimal applicator settings a priori is crucial for effective and safe thermal ablation.

Purpose of the Study:

• To develop and evaluate a minimax optimization-based inverse treatment planning approach for interstitial thermal therapy.
• To determine optimal applicator placements and power levels to maintain specific minimum tumor and maximum normal tissue temperatures.
• To assess the approach's viability and sensitivity to variations in tumor shape and blood perfusion.

Main Methods:

• Utilized a finite element method (FEM) to approximate bioheat transfer equation solutions.
• Formulated a constrained minimax optimization problem to minimize temperature errors.
• Investigated two applicator classes: with and without surface cooling.
• Modeled power deposition for interstitial ultrasound applicators in a 2D setting.

Main Results:

• The minimax optimization approach successfully determined optimal applicator settings for thermal therapy.
• The method demonstrated utility in meeting prescribed Tmin/Tmax clinical objectives.
• Results showed potential for generating robust treatment plans resilient to blood perfusion variations.

Conclusions:

• The developed minimax optimization strategy offers a promising approach for inverse treatment planning in interstitial thermal therapy.
• This method can generate treatment plans that adhere to critical temperature thresholds in both target and healthy tissues.
• The approach shows potential for improving treatment efficacy and safety by accounting for biological variability.