Clinical trial of Microwave Tomography imaging | IEEE Conference Publication | IEEE Xplore

Clinical trial of Microwave Tomography imaging


Abstract:

This paper describes clinical trial and results of a microwave tomography system. This system was designed as a clinical trial prototype operating at 3GHz to 6GHz to dete...Show More

Abstract:

This paper describes clinical trial and results of a microwave tomography system. This system was designed as a clinical trial prototype operating at 3GHz to 6GHz to detect woman-breast cancers. The subject numbers were 15 and it was non-blind test. Their images were reconstructed by the algorithm with a unique fast electromagnetic solver. Radiology doctors analyzed 30 images of 15 subjects and confirmed its working and feasibility.
Date of Conference: 21-25 August 2016
Date Added to IEEE Xplore: 23 March 2017
ISBN Information:
Conference Location: Seoul, Korea (South)
Citations are not available for this document.

I. Introduction

There are papers which introduce MT(Microwave Tomography) imaging [1] – [2]. They used conventional 2D FDTD method as a numerical forward solver to reconstruct their images except some case study of 3D method [3]. However, when we applied the method for our preliminary test system to reconstruct images, we found there were computing time and memory problems [4]. To solve these problems, we introduced a unique fast electromagnetic solver instead of FDTD method and they worked well. We are now calling this as FFS(Fast Forward Solver) [5]–[7].

Cites in Papers - |

Cites in Papers - Other Publishers (5)

1.
Moutusi Mondal, Palash Ghosal, Amish Kumar, Debashis Nandi, "A Modified Microwave Based System Design for Early-Stage Breast Cancer Detection", Advanced Computational and Communication Paradigms, vol.535, pp.251, 2023.
2.
Tyson Reimer, Stephen Pistorius, "Review and Analysis of Tumour Detection and Image Quality Analysis in Experimental Breast Microwave Sensing", Sensors, vol.23, no.11, pp.5123, 2023.
3.
Michele Ambrosanio, Stefano Franceschini, Vito Pascazio, Fabio Baselice, "An End-to-End Deep Learning Approach for Quantitative Microwave Breast Imaging in Real-Time Applications", Bioengineering, vol.9, no.11, pp.651, 2022.
4.
Douglas Kurrant, Muhammad Omer, Nasim Abdollahi, Pedram Mojabi, Elise Fear, Joe LoVetri, "Evaluating Performance of Microwave Image Reconstruction Algorithms: Extracting Tissue Types with Segmentation Using Machine Learning", Journal of Imaging, vol.7, no.1, pp.5, 2021.
5.
Maged A. Aldhaeebi, Khawla Alzoubi, Thamer S. Almoneef, Saeed M. Bamatraf, Hussein Attia, Omar M. Ramahi, "Review of Microwaves Techniques for Breast Cancer Detection", Sensors, vol.20, no.8, pp.2390, 2020.
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References

References is not available for this document.