The study on the inverse problem of applied current thermoacoustic imaging based on generative adversarial network

Abstract Applied Current Thermoacoustic Imaging (ACTAI) is a new imaging method which combines electromagnetic excitation with ultrasound imaging, and takes ultrasonic signal as medium and biological tissue conductivity as detection target. Taking the high contrast advantage of Electrical Impedance...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Liang Guo, Su Li, Xiangye Wang, Caihong Zeng, Chunyu Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4b5d85ae4b594660b66f811da08d4622
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4b5d85ae4b594660b66f811da08d4622
record_format	dspace
spelling	oai:doaj.org-article:4b5d85ae4b594660b66f811da08d46222021-11-28T12:17:06ZThe study on the inverse problem of applied current thermoacoustic imaging based on generative adversarial network10.1038/s41598-021-02291-22045-2322https://doaj.org/article/4b5d85ae4b594660b66f811da08d46222021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02291-2https://doaj.org/toc/2045-2322Abstract Applied Current Thermoacoustic Imaging (ACTAI) is a new imaging method which combines electromagnetic excitation with ultrasound imaging, and takes ultrasonic signal as medium and biological tissue conductivity as detection target. Taking the high contrast advantage of Electrical Impedance Tomography (EIT) and high resolution advantage of ultrasound imaging, ACTAI has broad application prospects in the field of biomedical imaging. Although ACTAI has high excitation efficiency and strong detectable Signal-to-Noise Ratio, yet while under low frequency electromagnetic excitation, it is still a big challenge to reconstruct a high-resolution image of target conductivity. This paper proposes a new method for reconstructing conductivity based on Generative Adversarial Network, and it consists of three main steps: firstly, use Wiener filtering deconvolution to restore the electrical signal output by the ultrasonic probe to a real acoustic signal. Then obtain the initial acoustic source image with filtered backprojection technology. Finally, match the conductivity image with the initial sound source image, which are used as training samples for generating the adversarial network to establish a deep learning model for conductivity reconstruction. After theoretical analysis and simulation research, it is found that by introducing machine learning, the new method can dig out the inverse problem solving model contained in the data, which further reconstruct a high-resolution conductivity image and has strong anti-interference characteristics. The new method provides a new way to solve the problem of conductivity reconstruction in Applied Current Thermoacoustic Imaging.Liang GuoSu LiXiangye WangCaihong ZengChunyu LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Liang Guo Su Li Xiangye Wang Caihong Zeng Chunyu Liu The study on the inverse problem of applied current thermoacoustic imaging based on generative adversarial network
description	Abstract Applied Current Thermoacoustic Imaging (ACTAI) is a new imaging method which combines electromagnetic excitation with ultrasound imaging, and takes ultrasonic signal as medium and biological tissue conductivity as detection target. Taking the high contrast advantage of Electrical Impedance Tomography (EIT) and high resolution advantage of ultrasound imaging, ACTAI has broad application prospects in the field of biomedical imaging. Although ACTAI has high excitation efficiency and strong detectable Signal-to-Noise Ratio, yet while under low frequency electromagnetic excitation, it is still a big challenge to reconstruct a high-resolution image of target conductivity. This paper proposes a new method for reconstructing conductivity based on Generative Adversarial Network, and it consists of three main steps: firstly, use Wiener filtering deconvolution to restore the electrical signal output by the ultrasonic probe to a real acoustic signal. Then obtain the initial acoustic source image with filtered backprojection technology. Finally, match the conductivity image with the initial sound source image, which are used as training samples for generating the adversarial network to establish a deep learning model for conductivity reconstruction. After theoretical analysis and simulation research, it is found that by introducing machine learning, the new method can dig out the inverse problem solving model contained in the data, which further reconstruct a high-resolution conductivity image and has strong anti-interference characteristics. The new method provides a new way to solve the problem of conductivity reconstruction in Applied Current Thermoacoustic Imaging.
format	article
author	Liang Guo Su Li Xiangye Wang Caihong Zeng Chunyu Liu
author_facet	Liang Guo Su Li Xiangye Wang Caihong Zeng Chunyu Liu
author_sort	Liang Guo
title	The study on the inverse problem of applied current thermoacoustic imaging based on generative adversarial network
title_short	The study on the inverse problem of applied current thermoacoustic imaging based on generative adversarial network
title_full	The study on the inverse problem of applied current thermoacoustic imaging based on generative adversarial network
title_fullStr	The study on the inverse problem of applied current thermoacoustic imaging based on generative adversarial network
title_full_unstemmed	The study on the inverse problem of applied current thermoacoustic imaging based on generative adversarial network
title_sort	study on the inverse problem of applied current thermoacoustic imaging based on generative adversarial network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4b5d85ae4b594660b66f811da08d4622
work_keys_str_mv	AT liangguo thestudyontheinverseproblemofappliedcurrentthermoacousticimagingbasedongenerativeadversarialnetwork AT suli thestudyontheinverseproblemofappliedcurrentthermoacousticimagingbasedongenerativeadversarialnetwork AT xiangyewang thestudyontheinverseproblemofappliedcurrentthermoacousticimagingbasedongenerativeadversarialnetwork AT caihongzeng thestudyontheinverseproblemofappliedcurrentthermoacousticimagingbasedongenerativeadversarialnetwork AT chunyuliu thestudyontheinverseproblemofappliedcurrentthermoacousticimagingbasedongenerativeadversarialnetwork AT liangguo studyontheinverseproblemofappliedcurrentthermoacousticimagingbasedongenerativeadversarialnetwork AT suli studyontheinverseproblemofappliedcurrentthermoacousticimagingbasedongenerativeadversarialnetwork AT xiangyewang studyontheinverseproblemofappliedcurrentthermoacousticimagingbasedongenerativeadversarialnetwork AT caihongzeng studyontheinverseproblemofappliedcurrentthermoacousticimagingbasedongenerativeadversarialnetwork AT chunyuliu studyontheinverseproblemofappliedcurrentthermoacousticimagingbasedongenerativeadversarialnetwork
_version_	1718408074745610240

The study on the inverse problem of applied current thermoacoustic imaging based on generative adversarial network

Ejemplares similares