Deep Learning Enhanced Contrast Source Inversion for Microwave Breast Cancer Imaging Modality

被引：6

作者：

Hirose, Umita ^{[1
]}

Zhu, Peixian ^{[1
]}

Kidera, Shouhei ^{[1
]}

机构：

[1] Univ Electrocommun, Grad Sch Informat & Engn, Chofu, Tokyo 1828585, Japan

来源：

IEEE JOURNAL OF ELECTROMAGNETICS RF AND MICROWAVES IN MEDICINE AND BIOLOGY | 2022年 / 6卷 / 03期

关键词：

Image reconstruction; Training; Microwave imaging; Permittivity; Transmitters; Receivers; Microwave theory and techniques; Convolutional auto-encoder (CAE); contrast source inversion (CSI); deep learning; inverse scattering analysis; microwave ultra wide-band (UWB) breast cancer detection; CONVOLUTIONAL NEURAL-NETWORK; SCATTERING;

D O I：

10.1109/JERM.2021.3127110

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This study presents a deep-learning (DL) based contrast source inversion (CSI) algorithm for quantitative microwave breast cancer imaging. Inverse scattering analysis for quantitative dielectric profile reconstruction is promising for a higher recognition rate for cancer detection, especially for malignant breast tumors. We focus on CSI as a low complexity approach, and implement a deep convolutional autoencorder (CAE) scheme using radar raw-data, which enhances the convergence speed and reconstruction accuracy. Numerical tests using MRI-derived realistic phantoms demonstrate that the proposed method significantly enhances the reconstruction performance of the CSI.

引用

页码：373 / 379

页数：7

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