A Feasibility Study of 2-D Microwave Thorax Imaging Based on the Supervised Descent Method

Haolin Zhang,Jianchong Shao,Hongyu Zhou,Shenheng Xu,Yan Yin,Fan Yang,Maokun Li,Yubo Yang,Sihang Zeng

doi:10.3390/electronics10030352

Haolin Zhang, Jianchong Shao + Show 7 more

Open Access

https://doi.org/10.3390/electronics10030352

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Journal: Electronics	Publication Date: Feb 2, 2021
Citations: 12	License type: CC BY 4.0

Affiliation: Tsinghua University

Abstract

In this paper, the application of the supervised descent method (SDM) for 2-D microwave thorax imaging is studied. The forward modeling problem is solved by the finite element-boundary integral (FE-BI) method. According to the prior information of human thorax, a 3-ellipse training set is generated offline. Then, the average descent direction between an initial background model and the training models is calculated. Finally, the reconstruction of the testing thorax model is achieved based on the average descent directions online. The feasibility using One-Step SDM for thorax imaging is studied. Numerical results indicate that the structural information of thorax can be reconstructed. It has potential for real-time imaging in future clinical diagnosis.

Highlights

Xu, S.; Yin, Y.; Zhou, H.; Yang, Y.; Microwave plays an important role in non-invasive detections, such as geophysics explorations and industrial monitoring
A training set consisting of three ellipses was introduced to train the average update direction
The results of numerical experiments verified its feasibility in thorax imaging

Summary

Introduction

S.; Yin, Y.; Zhou, H.; Yang, Y.; Microwave plays an important role in non-invasive detections, such as geophysics explorations and industrial monitoring. Microwave can be used for thorax imaging and monitoring the human respiratory system. Microwave biomedical imaging can be formulated as an inverse problem. Researchers applied the synthetic radar imaging method in detection of human torso fluid [19] and brain stroke [30]. To the authors’ limited knowledge, there is not much research being done on the application of machine learning techniques to microwave thorax imaging. The supervised descent method (SDM) is a learning-based technique. The application of SDM for 2-D microwave thorax imaging is discussed, which is the first time for SDM applied in this field. Numerical experiments based on thorax models are conducted and discussions are made according to numerical results of iterative SDM.

Formulations

Description of Thorax Model

Training Set

Iterative SDM

One-Step SDM

Discussions

Conclusions