Microwaves can safely and non-destructively illuminate and penetrate dielectric materials, making them an attractive solution for various medical tasks, including detection, diagnosis, classification, and monitoring. Their inherent electromagnetic properties, portability, cost-effectiveness, and the growth in computing capabilities have encouraged the development of numerous microwave sensing and imaging systems in the medical field, with the potential to complement or even replace current gold-standard methods. This review aims to provide a comprehensive update on the latest advances in medical applications of microwaves, particularly focusing on the near-field ones working within the 1–15 GHz frequency range. It specifically examines significant strides in the development of clinical devices for brain stroke diagnosis and classification, breast cancer screening, and continuous blood glucose monitoring. The technical implementation and algorithmic aspects of prototypes and devices are discussed in detail, including the transceiver systems, radiating elements (such as antennas and sensors), and the imaging algorithms. Additionally, it provides an overview of other promising cutting-edge microwave medical applications, such as knee injuries and colon polyps detection, torso scanning and image-based monitoring of thermal therapy intervention. Finally, the review discusses the challenges of achieving clinical engagement with microwave-based technologies and explores future perspectives.

This paper presents the experimental validation of a microwave imaging system for real-time monitoring of brain stroke in the post-acute stage. The system exploits a low-complexity sensing apparatus and a multi-frequency microwave imaging algorithm with a novel artifact removal feature. Phantoms of a homogeneous anthropomorphic head and an ellipsoidal non-static stroke mimicking target, varying gradually from 0 cm ³ to 60 cm ³ , are employed for the experiments. The phantom and the evolving target are filled with appropriate alcohol-based mixtures to mimic the different dielectric properties of the relevant tissue. The microwave imaging scanner operates using a 22-antennas architecture formed by printed flexible antennas with a custom-made matching medium. The system provides 3-D images of the entire brain region, exploiting differential multi-view scattering measures and the distorted Born approximation to build a pre-computed imaging kernel. The results show the system’s capability to follow up the continuous progression of hemorrhage and ischemia zones with centimetric spatial resolution and to provide information on whether the stroke is growing or shrinking.

This paper examines the effects of the switching matrix on a multi-view and low-complexity portable microwave imaging system for brain stroke monitoring. It considers two switching solutions: an ad-hoc one relying on RF electromechanical switches and a compact off-the-shelf one using solid-state switches. The performed analysis deems path attenuation and inter-channel isolation. It studies the impact of the different components of the scanning time, such as switching, communication, acquisition times, and the system dynamics on imaging performance and monitoring capabilities, optimizing the system setting while identifying system bottlenecks. The system uses an upgraded antenna-matching module and is experimentally validated using a mimicked hemorrhagic stroke-evolving scenario, demonstrating the effectiveness of both switching solutions in tracking and localizing the stroke progression. Tests of repeatability and sensitivity to false positive cases are also reported.

This work gathers the pathway from the design to the experimental testing of a microwave imaging prototype to monitor brain stroke in real-time conditions, approaching thus the electromagnetic inverse problem of retrieving a dielectric temporal variation within the head. To this end, it presents a low-complexity device consisting of twenty-two custom-made radiating elements working with a linear imaging algorithm based on distorted Born approximation and a truncated singular value decomposition, able to localize, identify and track the stroke evolution. The system is prototyped using a compact two-ports vector analyzer and electromechanical switching matrix. It is assessed experimentally via a mimicked hemorrhagic condition, demonstrating the system’s capabilities to follow up centimetric confined variations, retrieving 3-D maps of the studied cases in real-time.