Special Issue "Electromagnetic Technologies for Medical Diagnostics: Fundamental Issues, Clinical Applications and Perspectives"

A special issue of Diagnostics (ISSN 2075-4418).

Deadline for manuscript submissions: 30 June 2018

Special Issue Editors

Guest Editor
Dr. Lorenzo Crocco

IREA - Institute for the Electromagnetic Sensing of the Environment;
CNR - National Research Council of Italy, Via Diocleziano 328, I-80124 Naples, Italy
Website | E-Mail
Interests: electromagnetics; imaging; radar; inverse problems
Guest Editor
Dr. Panos Kosmas

Department of Informatics, King’s College London, UK
Website | E-Mail
Interests: computational electrodynamics (FDTD); antennas and microwave engineering; physics-based signal processing; medical imaging

Special Issue Information

Dear Colleagues,

Electromagnetic (EM) technologies for medical imaging represent an emerging alternative diagnostic modality, which is attracting the attention of many researchers worldwide, thanks to unique features such as the non-ionizing nature and the intrinsic low cost of equipment. At the European level, this research area has gained significant momentum through two COST Actions (MiMed—TD1301; EMF-MED BM1309), which have brought together the efforts of a number of scholars and practitioners in the push towards translating this emerging technology into clinics.

This Special Issue aims at providing a comprehensive picture on this lively research area by gathering contributions covering all aspects related to this research, starting from fundamental questions (e.g., dielectric property measurements of tissue, development of imaging methodologies, modelling of EM scattering), to experimental validation in laboratory and in vivo, down to clinical trials and applications (e.g., breast cancer imaging, neuroimaging, biomedical sensing and monitoring of vital parameters). Contributions may be, therefore, related, but not limited, to microwave imaging, microwave radiometry, combined modalities, electrical property tomography, and low frequency imaging methods, such as electric impedance tomography, contrast enhanced imaging, and bioradar.

Dr. Lorenzo Crocco
Dr. Panos Kosmas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 550 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Microwave imaging
  • Inverse scattering
  • Cancer
  • Electromagnetics
  • Biomedical radar
  • Biomedical sensing

Published Papers (2 papers)

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Research

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Open AccessArticle Diagnosing Breast Cancer with Microwave Technology: Remaining Challenges and Potential Solutions with Machine Learning
Diagnostics 2018, 8(2), 36; https://doi.org/10.3390/diagnostics8020036
Received: 14 April 2018 / Revised: 15 May 2018 / Accepted: 16 May 2018 / Published: 19 May 2018
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Abstract
Currently, breast cancer often requires invasive biopsies for diagnosis, motivating researchers to design and develop non-invasive and automated diagnosis systems. Recent microwave breast imaging studies have shown how backscattered signals carry relevant information about the shape of a tumour, and tumour shape is
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Currently, breast cancer often requires invasive biopsies for diagnosis, motivating researchers to design and develop non-invasive and automated diagnosis systems. Recent microwave breast imaging studies have shown how backscattered signals carry relevant information about the shape of a tumour, and tumour shape is often used with current imaging modalities to assess malignancy. This paper presents a comprehensive analysis of microwave breast diagnosis systems which use machine learning to learn characteristics of benign and malignant tumours. The state-of-the-art, the main challenges still to overcome and potential solutions are outlined. Specifically, this work investigates the benefit of signal pre-processing on diagnostic performance, and proposes a new set of extracted features that capture the tumour shape information embedded in a signal. This work also investigates if a relationship exists between the antenna topology in a microwave system and diagnostic performance. Finally, a careful machine learning validation methodology is implemented to guarantee the robustness of the results and the accuracy of performance evaluation. Full article
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Review

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Open AccessReview Open-Ended Coaxial Probe Technique for Dielectric Measurement of Biological Tissues: Challenges and Common Practices
Diagnostics 2018, 8(2), 40; https://doi.org/10.3390/diagnostics8020040
Received: 30 April 2018 / Revised: 24 May 2018 / Accepted: 2 June 2018 / Published: 5 June 2018
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Abstract
Electromagnetic (EM) medical technologies are rapidly expanding worldwide for both diagnostics and therapeutics. As these technologies are low-cost and minimally invasive, they have been the focus of significant research efforts in recent years. Such technologies are often based on the assumption that there
[...] Read more.
Electromagnetic (EM) medical technologies are rapidly expanding worldwide for both diagnostics and therapeutics. As these technologies are low-cost and minimally invasive, they have been the focus of significant research efforts in recent years. Such technologies are often based on the assumption that there is a contrast in the dielectric properties of different tissue types or that the properties of particular tissues fall within a defined range. Thus, accurate knowledge of the dielectric properties of biological tissues is fundamental to EM medical technologies. Over the past decades, numerous studies were conducted to expand the dielectric repository of biological tissues. However, dielectric data is not yet available for every tissue type and at every temperature and frequency. For this reason, dielectric measurements may be performed by researchers who are not specialists in the acquisition of tissue dielectric properties. To this end, this paper reviews the tissue dielectric measurement process performed with an open-ended coaxial probe. Given the high number of factors, including equipment- and tissue-related confounders, that can increase the measurement uncertainty or introduce errors into the tissue dielectric data, this work discusses each step of the coaxial probe measurement procedure, highlighting common practices, challenges, and techniques for controlling and compensating for confounders. Full article
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