Microwave Antenna Excitation Optimization with Deep Learning For Breast Cancer Hyperthermia
Microwave hyperthermia (MH) requires the effective calibration of the antenna excitations for selective focusing of the microwave energy to the target region with a nominal effect on the surrounding tissue. To this end, many different antenna calibration methods such as optimization techniques and lookup tables have been proposed in the literature. These optimization procedures, however, do not consider the whole nature of the electric field, which is a complex vector field, instead it is simplified to a real and scalar field component. Furthermore, most of the approaches in literature are system-specific, limiting the applicability of the proposed methods to specific configurations. In this paper, we propose an antenna excitation optimization scheme applicable to a variety of configurations and present the results of a Convolutional Neural Network (CNN) based approach for two different configurations. Data set for CNN training is collected by superposing the information obtained from individual antenna elements. The results of the CNN models outperform to look-up table results. The proposed approach is promising as the phase only optimization shows 27% and phase-power combined optimization shows 4% less hot spot-to-target energy ratio than look-up table results.