Antenna Excitation Optimization with Deep Learning for Microwave Breast Cancer Hyperthermia

Gulsah Yildiz, Halimcan Yasar, Ibrahim Enes Uslu, Yusuf Demirel, Mehmet Nuri Akinci, Tuba Yilmaz*, Ibrahim Akduman

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name???Makalebilirkişi

10 Atıf (Scopus)


Microwave hyperthermia (MH) requires the effective calibration of antenna excitations for the selective focusing of the microwave energy on the target region, with a nominal effect on the surrounding tissue. To this end, many different antenna calibration methods, such as optimization techniques and look-up 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 the 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. The data set for CNN training is collected by superposing the information obtained from individual antenna elements. The results of the CNN models outperform the look-up table results. The proposed approach is promising, as the phase-only optimization and phase–power-combined optimization show a 27% and 4% lower hotspot-to-target energy ratio, respectively, than the look-up table results for the linear MH applicator. The proposed deep-learning-based optimization technique can be utilized as a protocol to be applied on any MH applicator for the optimization of the antenna excitations, as well as for a comparison of MH applicators.

Orijinal dilİngilizce
Makale numarası6343
Basın numarası17
Yayın durumuYayınlandı - Eyl 2022

Bibliyografik not

Publisher Copyright:
© 2022 by the authors.


This work has received funding from Scientific and Technological Research Council of Turkey under grant agreement 118S074 and COST Action grant agreement CA17115.

FinansörlerFinansör numarası
European Cooperation in Science and TechnologyCA17115
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu118S074

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