Determining the electric field distribution using electromagnetic solvers requires significant computational effort, a high time consumption, and operator training. In this paper, a deep learning (DL)-based method is specifically developed to reconstruct the electric field distribution generated by antennas, based on the dielectric properties of the breast. ResNet and UNet were employed to predict the electric field within complex breast structures. Nine distinct breast dielectric models were utilized to create the initial dataset. A novel data augmentation technique was applied and the size was increased by three to address the challenge of limited dataset size. The electric field distribution data were generated using commercially available finite element method-based electromagnetic simulations. DL architectures were customized to fit the study and trained over 4374 breast slices. ResNet demonstrated an average Signal-to-Noise Ratio (SNR) of 23.7±6.7 dB and an average Normalized Mean Squared Error (NMSE) of 0.0029±0.0039 across 504 test breast slices, while UNet averaged 24.55±6.64 dB and 0.0024±0.0031 respectively. Additionally, a masked loss calculation was incorporated to enhance the learning accuracy of the implemented models. This approach yielded an increase of 0.9 dB in average SNR for ResNet and a 0.8 dB SNR increase for the UNet architecture on average. These results indicate that the proposed DL models effectively reconstruct electric field distributions with substantial accuracy, highlighting its potential for practical applications in real-time microwave medical imaging and treatment planning. Further refinement and validation with a more diverse set of breast models could enhance the model's applicability and performance.

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.