Cargando…

Antenna Excitation Optimization with Deep Learning for Microwave Breast Cancer Hyperthermia

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Yildiz, Gulsah, Yasar, Halimcan, Uslu, Ibrahim Enes, Demirel, Yusuf, Akinci, Mehmet Nuri, Yilmaz, Tuba, Akduman, Ibrahim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460623/
https://www.ncbi.nlm.nih.gov/pubmed/36080800
http://dx.doi.org/10.3390/s22176343
_version_ 1784786792596111360
author Yildiz, Gulsah
Yasar, Halimcan
Uslu, Ibrahim Enes
Demirel, Yusuf
Akinci, Mehmet Nuri
Yilmaz, Tuba
Akduman, Ibrahim
author_facet Yildiz, Gulsah
Yasar, Halimcan
Uslu, Ibrahim Enes
Demirel, Yusuf
Akinci, Mehmet Nuri
Yilmaz, Tuba
Akduman, Ibrahim
author_sort Yildiz, Gulsah
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9460623
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-94606232022-09-10 Antenna Excitation Optimization with Deep Learning for Microwave Breast Cancer Hyperthermia Yildiz, Gulsah Yasar, Halimcan Uslu, Ibrahim Enes Demirel, Yusuf Akinci, Mehmet Nuri Yilmaz, Tuba Akduman, Ibrahim Sensors (Basel) Article 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. MDPI 2022-08-23 /pmc/articles/PMC9460623/ /pubmed/36080800 http://dx.doi.org/10.3390/s22176343 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yildiz, Gulsah
Yasar, Halimcan
Uslu, Ibrahim Enes
Demirel, Yusuf
Akinci, Mehmet Nuri
Yilmaz, Tuba
Akduman, Ibrahim
Antenna Excitation Optimization with Deep Learning for Microwave Breast Cancer Hyperthermia
title Antenna Excitation Optimization with Deep Learning for Microwave Breast Cancer Hyperthermia
title_full Antenna Excitation Optimization with Deep Learning for Microwave Breast Cancer Hyperthermia
title_fullStr Antenna Excitation Optimization with Deep Learning for Microwave Breast Cancer Hyperthermia
title_full_unstemmed Antenna Excitation Optimization with Deep Learning for Microwave Breast Cancer Hyperthermia
title_short Antenna Excitation Optimization with Deep Learning for Microwave Breast Cancer Hyperthermia
title_sort antenna excitation optimization with deep learning for microwave breast cancer hyperthermia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460623/
https://www.ncbi.nlm.nih.gov/pubmed/36080800
http://dx.doi.org/10.3390/s22176343
work_keys_str_mv AT yildizgulsah antennaexcitationoptimizationwithdeeplearningformicrowavebreastcancerhyperthermia
AT yasarhalimcan antennaexcitationoptimizationwithdeeplearningformicrowavebreastcancerhyperthermia
AT usluibrahimenes antennaexcitationoptimizationwithdeeplearningformicrowavebreastcancerhyperthermia
AT demirelyusuf antennaexcitationoptimizationwithdeeplearningformicrowavebreastcancerhyperthermia
AT akincimehmetnuri antennaexcitationoptimizationwithdeeplearningformicrowavebreastcancerhyperthermia
AT yilmaztuba antennaexcitationoptimizationwithdeeplearningformicrowavebreastcancerhyperthermia
AT akdumanibrahim antennaexcitationoptimizationwithdeeplearningformicrowavebreastcancerhyperthermia