89
Views
0
CrossRef citations to date
0
Altmetric
Research Article

An improved contrast source inversion approach for the reconstruction of single-pole Cole–Cole model parameters

ORCID Icon, &
Pages 2211-2226 | Received 09 Dec 2021, Accepted 20 Apr 2022, Published online: 29 Apr 2022
 

Abstract

Single-pole Cole–Cole empirical models are often used for the high-precision description of the dispersion characteristics of common media, such as biological tissues, and water. The existing contrast source inversion (CSI) methods are not appropriate to reconstruct their frequency-dependent electrical properties. Therefore, one of them, a multi-frequency (MF) CSI method with a multiplicative regularization (MR), is modified from the direct estimation of the contrast between dispersive scatterers and their background medium, into the alternative reconstruction of three kinds of one-pole Cole–Cole model parameters, namely, the optical relative permittivity, relative permittivity difference, and static conductivity. Through two two-dimensional (2D) numerical experiments, the modified CSI method has been applied to detect breast tumors. The reconstructed results demonstrate preliminarily that the improved approach is feasible for the quantitative inversion of the internal breast compositions, and thus promising for breast cancer screening.

Acknowledgment

The authors wish to thank the contributors of the University of Wisconsin–Madison to the numerical breast phantom database.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under grant number 51271059 and the Natural Science Foundation of Jiangsu Province of China under grant number BK20181077.

Notes on contributors

Guang-Dong Liu

Guang-Dong Liu was born in Jiangsu, China, in 1972. He received the Dr. Eng. Degree in electromagnetic field and microwave technology, from the College of Electronic Science and Engineering, Nanjing University of Posts and Telecommunications, in 2011. Currently, he is a professor of the School of Electrical and Control Engineering, Nanjing Polytechnic Institute, China. His research interests are in electromagnetic inverse scattering theory and its applications.

Yan-Ling Hu

Yan-Ling Hu was born in 1990, China. She received her BS degree in Applied Chemistry from Nanjing Normal University in 2011. After that, she moved to Nanjing University of Posts and Telecommunications where she is pursuing her PhD degree under the supervision of Professor Lianhui Wang. During 2015 to 2016, she studied and worked as an exchange student in Professor Hua Zhang's group in the School of Materials Science and Engineering, Nanyang Technological University, Singapore. Her research interests focus on the optical and electrochemical biosensors based on noble metallic nanomaterials and two-dimensional nanomaterials.

Jie Yin

Jie Yin was born in 1983, China. He received the Ph.D in acoustic engineering in 2016, from the Nanjing University, China. From 2016 to 2020, he was a Postdoctoral Fellow in the School of Physics of the Nanjing University. Since 2020, he is the Principle Investigator in the Laboratory of Biomedical Imaging Technology and Equipment of Nanjing Polytechnic Institute. His research interests include photoacoustic imaging technology, reconstruction algorithm in both electromagnetic and acoustic problems.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 561.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.