Raquel C. Conceição

University of Lisbon

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Martin O’Halloran

Ollscoil na Gaillimhe – University of Galway

Daniela M. Godinho

University of Lisbon

Edward Jones

Ollscoil na Gaillimhe – University of Galway

Martin Glavin

Ollscoil na Gaillimhe – University of Galway

Carlos A. Fernandes

Instituto de Telecomunicações

João M. Felício

Instituto de Telecomunicações

Emily Porter

McGill University

Tiago Castela

Hospital da Luz

Matteo Savazzi

Instituto de Telecomunicações

Ana Catarina Pelicano

University of Lisbon

Cooperative Institutions

University of Lisbon

Ollscoil na Gaillimhe – University of Galway

National University of Ireland

University of Aveiro

Universidade Nova de Lisboa

Instituto Politécnico de Lisboa

Sapienza University of Rome

King's College London

University of Malta

University Hospital Galway

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The Complex Permittivity of Biological Tissues: A Practical Measurement Guideline

IEEE Access (2024)

Lourdes Farrugia Emily Porter Raquel C. Conceição Simona Di Meo Daniela M. Godinho

Despite the significant number of studies published on the measurements of complex permittivity of biological tissues in the last thirty years, implementing a successful measurement program for dielectric measurements can still present a challenge for researchers. Most problems are not theoretical but of methodological or practical nature. In this article, lessons learned from experiences with goal-oriented measurements are presented by structuring them into practical guidelines for efficient and useful measurements of dielectric properties of biological tissues, aimed at addressing gaps in knowledge. Issues related to calibration, validation of the measurement system and data collection procedures are addressed from a practical perspective. This will help support reproducibility of measurements. In addition, guidelines for data analysis and data reporting are provided. The latter is also supported by a data analysis tool developed in MATLAB, made available as open source. This facilitates the harmonisation and merging of different datasets, ease of interpreting and re-using of data and comparison of data across studies. Additionally, a data repository is presented for uploading of dielectric data of biological tissues, along with the corresponding meta-data describing the experiments. These guidelines are the result of the work carried out by a dedicated working group in the project COST Action MyWAVE.

Guideline

10.1109/access.2024.3352728

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Citations (4)

Other applications of medical microwaves – Breast tumour classification

European Journal of Molecular & Clinical Medicine (2015)

Raquel C. Conceição

Abstract This talk addresses the development of imaging techniques for the early detection of breast cancer, based on Ultra Wideband (UWB) radar, a promising emerging technology that exploits the dielectric contrast between normal and tumour tissues at microwave frequencies. Of particular interest in this work are issues related to techniques for classification of potential breast tumours into benign and malignant. This is particularly important given the results from recent studies of the dielectric properties of breast and tumour tissue, which have found that strong similarities exist between the dielectric properties of malignant, benign and normal fibroglandular breast tissue. This creates a more challenging imaging scenario and motivates the development of enhanced signal processing techniques for UWB imaging systems. Tumour growth and development patterns are modelled using Gaussian Random Spheres, using four discrete sizes and four different shapes. Feature extraction methods including Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Discrete Wavelet Transform (DWT), are used to extract the most relevant features from the detailed Radar Target Signatures of the tumours, which are then classified with a number of different classification techniques: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machines (SVM). In addition to these techniques, a number of different multi-stage classification architectures are considered. The feature extraction and classification algorithms are evaluated for both homogeneous and heterogeneous breast tissue models, for a range of different tumour sizes and shapes. Also, the first experimental results using a pre-clinical UWB prototype imaging system for tumour classification based on the shape of tumours. A database of benign and malignant tumour phantoms was created using dielectrically–representative tissue-mimicking material. Classification of benign and malignant tumour models of the experimental data was completed with Linear Discriminant Analysis, Quadratic Discriminant Analysis and Support Vector Machines classifiers.

10.1016/j.nhtm.2014.11.028

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Extracting Dielectric Properties for MRI-based Phantoms for Axillary Microwave Imaging Device

2022 16th European Conference on Antennas and Propagation (EuCAP) (2020)

Daniela M. Godinho João M. Felício Tiago Castela Nuno André da Silva M. Lurdes Orvalho

Microwave Imaging (MWI) is an emerging medical imaging technique, which has been studied to aid breast cancer diagnosis in the frequency range from 0.5 to 30 GHz. The information about the dielectric properties of each tissue is essential to assess the viability of this type of systems. However, accurate measurements of heterogeneous tissues can be very challenging, and the current available information is still very limited. In this paper, we present a methodology for extracting dielectric properties to create anatomical models of the axillary region. These models will be used in a MWI device to aid breast cancer diagnosis through the detection of metastasised axillary lymph nodes. We apply segmentation tools to Magnetic Resonance Images (MRI) of the breast and assign dielectric properties to each tissue, extracting preliminary information about the properties of axillary lymph nodes. This study may open a way to more quickly extract dielectric properties of tissues and/or validate measurements, accelerating the development of microwave-based medical devices.

Microwave Imaging

Axillary lymph nodes

10.23919/eucap48036.2020.9135980

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Diagnosing Breast Cancer with Microwave Technology: Remaining Challenges and Potential Solutions with Machine Learning

Diagnostics (2018)

Bárbara Luz Oliveira Daniela M. Godinho Martin O’Halloran Martin Glavin Edward Jones

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.

Robustness

Microwave Imaging

Modalities

10.3390/diagnostics8020036

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Citations (27)

Development of Clinically Informed 3-D Tumor Models for Microwave Imaging Applications

IEEE Antennas and Wireless Propagation Letters (2015)

Bárbara Luz Oliveira Martin O’Halloran Raquel C. Conceição Martin Glavin Edward Jones

In this letter, a novel method for the generation of numerical 3-D clinically informed breast tumor models for microwave imaging applications is proposed, which greatly enhances flexibility in creating clinically realistic models. The proposed method was clinically validated through collaboration with breast cancer clinicians and conforms to the BI-RADS labeling standards. Moreover, the issue of clinically accurate tumor positioning within existing breast models is also addressed.

Microwave Imaging

Breast tumor

Breast imaging

10.1109/lawp.2015.2456051

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Citations (12)

Best Practices for Accurate Results Using Numerical Solvers for Microwave Body Screening

2022 16th European Conference on Antennas and Propagation (EuCAP) (2024)

Raquel A. Martins Daniela M. Godinho João M. Felício Matteo Savazzi Jorge R. Costa

10.23919/eucap60739.2024.10501344

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Current Status and Emerging Techniques for Measuring the Dielectric Properties of Biological Tissues

ASME Open Journal of Engineering (2024)

Emily Porter Lourdes Farrugia Punit Prakash Raquel C. Conceição Devashish Shrivastava

Abstract The dielectric properties of biological tissues are key parameters that support the design and usability of a wide range of electromagnetic-based medical applications, including for diagnostics and therapeutics, and allow the determination of safety and health effects due to exposure to electromagnetic fields. While an extensive body of literature exists that reports on values of these properties for different tissue types under different measurement conditions, it is now evident that there are large uncertainties and inconsistencies between measurement reports. Due to varying measurement techniques, limited measurement validation strategies, and lack of metadata reporting and confounder control, reported dielectric properties suffer from a lack of repeatability and questionable accuracy. Recently, the American Society of Mechanical Engineers (ASME) Thermal Medicine Standards Committee was formed, which included a Tissue Properties working group. This effort aims to support the translation and commercialization of medical technologies, through the development of a standard lexicon and standard measurement protocols. In this work, we present initial results from the Electromagnetic Tissue Properties subgroup. Specifically, this paper reports a critical gap analysis facing the standardization pathway for the dielectric measurement of biological tissues. All established measurement techniques are examined and compared, and emerging ones are assessed. Perspectives on the importance and challenges in measurement validation, accuracy calculation, metadata collection, and reporting are also discussed.

Repeatability

Aether

10.1115/1.4064746

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Development of a 3D Anthropomorphic Phantom Generator for Microwave Imaging Applications of the Head and Neck Region

Sensors (2020)

Ana Catarina Pelicano Raquel C. Conceição

The development of 3D anthropomorphic head and neck phantoms is of crucial and timely importance to explore novel imaging techniques, such as radar-based MicroWave Imaging (MWI), which have the potential to accurately diagnose Cervical Lymph Nodes (CLNs) in a neoadjuvant and non-invasive manner. We are motivated by a significant diagnostic blind-spot regarding mass screening of LNs in the case of head and neck cancer. The timely detection and selective removal of metastatic CLNs will prevent tumor cells from entering the lymphatic and blood systems and metastasizing to other body regions. The present paper describes the developed phantom generator which allows the anthropomorphic modelling of the main biological tissues of the cervical region, including CLNs, as well as their dielectric properties, for a frequency range from 1 to 10 GHz, based on Magnetic Resonance images. The resulting phantoms of varying complexity are well-suited to contribute to all stages of the development of a radar-based MWI device capable of detecting CLNs. Simpler models are essential since complexity could hinder the initial development stages of MWI devices. Besides, the diversity of anthropomorphic phantoms resulting from the developed phantom generator can be explored in other scientific contexts and may be useful to other medical imaging modalities.

Microwave Imaging

Cervical lymph nodes

10.3390/s20072029

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Citations (1)

Tumour Classification

Biological and medical physics series (2016)

Raquel C. Conceição M. Z. Jones Panagiotis Kosmas Yifan Chen

Microwave Imaging

Backscatter (email)

Distortion (music)

Breast Tumours

Parenchyma

10.1007/978-3-319-27866-7_5

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Citations (1)

Feasibility study of focal lens for multistatic microwave breast imaging

Daniela M. Godinho João M. Felício Carlos A. Fernandes Raquel C. Conceição

Microwave Imaging is an emerging technique to aid breast cancer diagnosis. Current multistatic setups involve complex and heavy signal processing techniques, such as to remove the energy coupling between adjacent sensors, which masks the response from inner tissues. We investigate a novel approach using a dielectric lens in order to reduce the coupling effects between antennas, thus reducing the signal processing burden, while preserving all the advantages of multistatic setups. In this paper, we show that we can successfully detect simulated breast targets on reconstructed images using a setup with a dielectric lens.

Microwave Imaging

SIGNAL (programming language)

10.1109/icecom48045.2019.9163628

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Citations (3)