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A Journey into Microwave Imaging and Sensing for Biomedical Applications: Recent Trends and Technologies

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 637

Special Issue Editors


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Guest Editor
Laboratoire de Génie Electrique et Electronique de Paris, CNRS, Sorbonne Université, 75252 Paris, France
Interests: target detection; medical imaging using microwave cameras; remote sensing applied to the study of natural environments

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Guest Editor
Laboratory of Electronics, Antennas and Telecommunications (LEAT), CNRS, Université Côte d’Azur, Sophia Antipolis, 06903 Valbonne, France
Interests: microwave and millimeter-wave imaging; radar; reflector antennas

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Guest Editor
Laboratoire de Génie Electrique et Electronique de Paris, CNRS, Sorbonne Université, 75252 Paris, France
Interests: microwave imaging; anthropomorphic standardized biological phantom; stroke monitoring; tumor detection

Special Issue Information

Dear Colleagues,

In recent years, there has been renewed interest in the development of microwave imaging and radar-based systems for biomedical applications and vital sign detection and analysis. Intelligent medical imaging offers significant advantages, including high speed, super-resolution images, and cost-effectiveness. Machine learning (ML) and deep learning (DL) techniques, which integrate big data and high-performance computing, have significantly advanced the study and application of these systems. Numerous studies have demonstrated the superiority of ML/DL-based imaging systems over traditional methods. However, a number of challenges remain in relation to fundamental theory, database development, and clinical applications.

This Special Issue will be devoted to exploring recently developed medical microwave imaging devices, encompassing machine learning theory, new imaging methods and devices, and signal and image processing techniques. We will highlight applications of microwave imaging, vital sign detection, and multimodal imaging more generally.

Prof. Dr. Helene Roussel
Prof. Dr. Claire Migliaccio
Dr. Nadine Joachimowicz
Guest Editors

Manuscript Submission Information

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Keywords

  • machine learning
  • microwave imaging
  • deep learning
  • diagnostic imaging
  • detection
  • hyperthermia
  • radar
  • vital signs
  • inverse problem

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Published Papers (1 paper)

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Research

20 pages, 23575 KB  
Article
Microwave Imaging for Parkinson’s Disease Detection: A Phantom-Based Feasibility Study Using Temperature-Controlled Dielectric Variations
by Leonardo Cardinali, David O. Rodriguez-Duarte, Jorge A. Tobón Vasquez, Francesca Vipiana and Luis Jofre-Roca
Sensors 2025, 25(24), 7562; https://doi.org/10.3390/s25247562 - 12 Dec 2025
Viewed by 387
Abstract
Parkinson’s disease (PD) is characterized by pathological changes in the substantia nigra, which in its early stages may manifest as structural and functional asymmetries between the two hemispheres. Microwave imaging has recently emerged as a promising non-invasive tool to detect subtle dielectric variations. [...] Read more.
Parkinson’s disease (PD) is characterized by pathological changes in the substantia nigra, which in its early stages may manifest as structural and functional asymmetries between the two hemispheres. Microwave imaging has recently emerged as a promising non-invasive tool to detect subtle dielectric variations. In the context of Parkinson’s disease, such contrasts are expected to arise from the underlying physiological alterations in brain tissue, although their magnitude has not yet been fully characterized. In this work, we investigate the feasibility of differential microwave imaging, where detection is based on permittivity contrasts, through a controlled phantom study. A simple two-dimensional head phantom was constructed using a 3D-printed cylindrical container filled with water, incorporating a Teflon tube to represent the substantia nigra. The tube was filled with hot water, whose gradual cooling emulated small dielectric changes. Since the dielectric properties of water vary linearly with temperature over 0.5–3 GHz, we first validated this dependence through both numerical analysis and experimental measurements. Four antennas were then employed in a differential imaging configuration, with image reconstruction performed via the multi-frequency bi-focusing algorithm. The results show that the system can successfully detect a dielectric contrast corresponding to a temperature variation as small as 0.4 °C, equivalent to approximately 0.17% in relative permittivity. While the exact dielectric changes associated with PD remain to be determined, these results demonstrate that the proposed approach is sensitive to very small contrasts, supporting the potential of differential microwave imaging as a candidate tool for future investigations into Parkinson’s disease detection. Full article
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