Cancer Detection Using Advanced UWB Microwave Technology

Medical diagnosis and subsequent treatment efficacy hinge on innovative imaging modalities. Among these, Microwave Imaging (MWI) has emerged as a compelling approach, offering safe and cost-efficient visualization of the human body. This comprehensive research explores the potential of the Huygens principle-based microwave imaging algorithm, specifically focusing on its prowess in cancer, lesion, and infection detection. Extensive experimentation employing meticulously crafted phantoms validates the algorithm’s robustness. In the context of lung infections, this study harnesses the power of Huygens-based microwave imaging to detect lung-COVID-19 infections. Employing Microstrip and horn antennas within a frequency range of 1 to 5 GHz and a multi-bistatic setup in an anechoic chamber, the research utilizes phantoms mimicking human torso dimensions and dielectric properties. Notably, the study achieves a remarkable detection capability, attaining a signal-to-clutter ratio of 7 dB during image reconstruction using S21 signals.A higher SCR ratio indicates better contrast and clarity of the detected inclusion, which is essential for reliable medical imaging. It is noteworthy that this achievement is realized in free space without necessitating coupling liquid, underscoring the algorithm’s practicality. Furthermore, the research delves into the validation of Huygens Principle (HP)-based microwave imaging in detecting intricate lung lesions. Utilizing a meticulously designed multi-layered phantom with characteristics closely mirroring human anatomy, the study spans frequency bands from 0.5 GHz to 3 GHz within an anechoic chamber. The outcomes are compelling, demonstrating consistent lesion detection within reconstructed images. Impressively, the signal-to-clutter ratio post-artifact removal surges to 13.4 dB, affirming the algorithm’s potential in elevating medical imaging precision. To propel the capabilities of MWI further, this research unveils a novel device: 3D microwave imaging rooted in Huygens principle. Leveraging MammoWave device’s capabilities, the study ventures into 3D image reconstruction. Dedicated phantoms housing 3D structured inclusions, each embodying distinct dielectric properties, serve as the experimental bedrock. Through an intricate interplay of data acquisition and processing, the study attains a laudable feat: seamless 3D visualization of inclusions across various z-axis planes, accompanied by minimal dimensional error not exceeding 7.5%. In a parallel exploration, spiral-like measurement configurations enter the spotlight. These configurations, meticulously tailored along the z-axis, yield promising results. The research unveils an innovative approach to reducing measurement time while safeguarding imaging fidelity. Notably, spiral-like measurements achieve a notable 50% reduction in measurement time, albeit with slight trade-offs. Signal-to-clutter ratios experience a modest reduction, and there is a minor increase in dimensional analysis error, which remains within the confines of 3.5%. The research findings serve as a testament to MWI’s efficacy across diverse medical domains. The success in lung infection and lesion detection underscores its potential impact on medical diagnostics. Moreover, the foray into 3D imaging and the strategic exploration of measurement configurations lay the foundation for future advancements in microwave imaging technologies. As a result, the outcomes of this research promise to reshape the landscape of accurate and efficient medical imaging modalities

Application-Specific Broadband Antennas for Microwave Medical Imaging

The goal of this work is the introduction of efficient antenna structures on the basis of the requirement of different microwave imaging methods; i.e. quantitative and qualitative microwave imaging techniques. Several criteria are proposed for the evaluation of single element antenna structures for application in microwave imaging systems. The performance of the proposed antennas are evaluated in simulation and measurement scenarios

Modeling human head tissues using fourth-order debye model in convolution-based three-dimensional finite-difference time-domain

A fourth order Debye model is derived using genetic algorithms to represent the dispersive properties of the 17 tissues that form the human head. The derived model gives accurate estimation of the electrical properties of those tissues across the frequency band from 0.1 GHz to 3 GHz that can be used in microwave systems for head imaging. A convolution-based three-dimensional finite-difference time-domain (3D-FDTD) formulation is implemented for modeling the electromagnetic wave propagation in the dispersive head tissues whose frequency dependent properties are represented by the derived fourth-order Debye model. The presented results show that the proposed 3D-FDTD and fourth-order Debye model can accurately show the electromagnetic interaction between a wide band radiation and head tissues with low computational overhead and more accurate results compared with using multi-pole Cole-Cole model

A Comparative Study of Automated Segmentation Methods for Use in a Microwave Tomography System for Imaging Intracerebral Hemorrhage in Stroke Patients

Microwave technology offers the possibility for pre-hospital stroke detection as we have pre- viously demonstrated using non-imaging diagnostics. The focus in this paper is on image-based diagnostics wherein the technical and computational complexities of image reconstruction are a challenge for clinical realization. Herein we investigate whether information about a patient’s brain anatomy obtained prior to a stroke event can be used to facilitate image-based stroke diag- nostics. A priori information can be obtained by segmenting the patient’s head tissues from mag- netic resonance images. Expert manual segmentation is presently the gold standard, but it is labo- rious and subjective. A fully automatic method is thus desirable. This paper presents an evaluation of several such methods using both synthetic magnetic resonance imaging (MRI) data and real da- ta from four healthy subjects. The segmentation was performed on the full 3D MRI data, whereas the electromagnetic evaluation was performed using a 2D slice. The methods were evaluated in terms of: i) tissue classification accuracy over all tissues with respect to ground truth, ii) the accu- racy of the simulated electromagnetic wave propagation through the head, and iii) the accuracy of the image reconstruction of the hemorrhage. The segmentation accuracy was measured in terms of the degree of overlap (Dice score) with the ground truth. The electromagnetic simulation accu- racy was measured in terms of signal deviation relative to the simulation based on the ground truth. Finally, the image reconstruction accuracy was measured in terms of the Dice score, relative error of dielectric properties, and visual comparison between the true and reconstructed intrace- rebral hemorrhage. The results show that accurate segmentation of tissues (Dice score = 0.97) from the MRI data can lead to accurate image reconstruction (relative error = 0.24) for the intra- cerebral hemorrhage in the subject’s brain. They also suggest that accurate automated segmenta- tion can be used as a surrogate for manual segmentation and can facilitate the rapid diagnosis of intracerebral hemorrhage in stroke patients using a microwave imaging system

Microwave Sensing and Imaging

In recent years, microwave sensing and imaging have acquired an ever-growing importance in several applicative fields, such as non-destructive evaluations in industry and civil engineering, subsurface prospection, security, and biomedical imaging. Indeed, microwave techniques allow, in principle, for information to be obtained directly regarding the physical parameters of the inspected targets (dielectric properties, shape, etc.) by using safe electromagnetic radiations and cost-effective systems. Consequently, a great deal of research activity has recently been devoted to the development of efficient/reliable measurement systems, which are effective data processing algorithms that can be used to solve the underlying electromagnetic inverse scattering problem, and efficient forward solvers to model electromagnetic interactions. Within this framework, this Special Issue aims to provide some insights into recent microwave sensing and imaging systems and techniques