Fast Optimization of Temperature Focusing in Hyperthermia Treatment of Sub-Superficial Tumors

TL;DR

This paper introduces a low-complexity, temperature-focused optimization method for hyperthermia treatment planning that improves tumor heating accuracy by adjusting SAR-based antenna excitations considering cooling effects.

Contribution

The proposed method simplifies temperature-focused planning by transforming it into SAR peak position optimization, reducing computational costs compared to traditional approaches.

Findings

Better temperature coverage in 3D head and neck models

Improved tumor heating accuracy over SAR-only optimization

Reduced computational effort by avoiding full bioheat equation simulations

Abstract

Microwave hyperthermia aims at selectively heating cancer cells to a supra-physiological temperature. For non-superficial tumors, this can be achieved by means of an antenna array equipped with a proper cooling system (the water bolus) to avoid overheating of the skin. In patient-specific treatment planning, antenna feedings are optimized to maximize the specific absorption rate (SAR) inside the tumor, or to directly maximize the temperature there, involving a higher numerical cost. We present here a method to effect a low-complexity temperature-based planning. It arises from recognizing that SAR and temperature have shifted peaks due to thermal boundary conditions at the water bolus and for physiological effects like air flow in respiratory ducts. In our method, temperature focusing on the tumor is achieved via a SAR-based optimization of the antenna excitations, but optimizing its target to account for the cooling effects. The temperature optimization process is turned into finding a SAR peak position that maximizes the chosen temperature objective function. Application of this method to the 3D head and neck region provides a temperature coverage that is consistently better than that obtained with SAR-optimization alone, also considering uncertainties in thermal parameters. This improvement is obtained by solving the bioheat equation a reduced number of times, avoiding its inclusion in a global optimization process.