A wearable microwave antenna array for time-domain breast tumor screening

In this work, we present a clinical prototype with a wearable patient interface for microwave breast cancer detection. The long-term aim of the prototype is a breast health monitoring application. The system operates using multistatic time-domain pulsed radar, with 16 flexible antennas embedded into a bra. Unlike the previously reported, table-based prototype with a rigid cup-like holder, the wearable one requires no immersion medium and enables simple localization of breast surface. In comparison with the table-based prototype, the wearable one is also significantly more cost-effective and has a smaller footprint. To demonstrate the improved functionality of the wearable prototype, we here report the outcome of daily testing of the new, wearable prototype on a healthy volunteer over a 28-day period. The resulting data (both signals and reconstructed images) is compared to that obtained with our table-based prototype. We show that the use of the wearable prototype has improved the quality of collected volunteer data by every investigated measure. This work demonstrates the proof-of-concept for a wearable breast health monitoring array, which can be further optimized in the future for use with patients with various breast sizes and tissue densities

Remote diagnostics and monitoring using microwave technique – improving healthcare in rural areas and in exceptional situations

Interests towards wireless portable medical diagnostics and monitoring systems, which could be used outside hospital e.g. during pandemic or catastrophic situations, have increased recently. Additionally, portable monitoring solutions could partially address widely recognized challenges related to healthcare equality in rural areas. Microwave based sensing has recently been recognized as emerging technology for portable medical monitoring and diagnostics devices since they may enable development of safe, reliable, and low-cost solutions for future’s telemedicine. The aim of this paper is to present the basic idea of microwave -based medical monitoring and discuss its possibilities, advantages, and challenges. In particular, we show that microwaves could be exploited in three pre-diagnostics applications: 1) Detection of abnormalities in the brain with a helmet type of monitoring device, 2) Detection of breast cancer with a self-monitoring vest, 3) Detection of blood clots in leg with an antenna band. The technique is based on detecting differences in radio channel responses caused by the abnormalities having different dielectric properties than the surrounding tissues. Our results of realistic simulations and experimental measurements show that even small-sized abnormalities, e.g. tumors, can change channel characteristics in detectable level

Microwave Imaging for Diagnostic Application

Imaging of the human body makes a significant contribution to the diagnosis and succeeding treatment of diseases. Among the numerous medical imaging methods, microwave imaging (MWI) is an attractive approach for medical applications due to its high potential to produce images of the human body safely with cost-efficiency. A wide range of studies and research has been done with the aim of using the microwave approach for medical applications. The focus of this research is developing MWI algorithms, which is the Huygens Principle (HP) based and to validate the capability of the proposed MWI algorithm to detect skin cancer and bone lesion through phantom measurements. The probability of the HP procedure for skin cancer detection has been investigated through design, and fabrication of a heterogeneous phantom simulating the human forearm having an inclusion mimicking a skin cancer. Ultrawideband (UWB) MWI methods are then applied to the phantom. The S21 parameter measurements are collected in an anechoic chamber environment and processed via HP technique. The tumour is successfully detected after applying appropriate artefact removal procedure. The ability to successfully apply HP to detect and locate a skin cancer type inclusion in a multilayer cylindrical phantom has been verified. The feasibility study of HP-based MWI procedure for bone lesion detection has also been investigated using a dedicated phantom. Validation has been completed through measurements inside the anechoic chamber in the frequency range of 1–3 GHz using one receiving and one transmitting antennas in free space. The identification of the lesion’s presence in different bone layers has been performed on images. The quantification of the obtained images has been performed by introducing parameters such as the resolution and signal-to-clutter ratio (S/C). The impact of different frequencies and bandwidths (in the 1–3 GHz range) in lesion detection has been investigated. The findings showed that the frequency range of 1.5–2.5 GHz offered the best resolution (1.1 cm) and S/C (2.22 on a linear scale). Subtraction between S21 obtained using two slightly displaced transmitting positions has been employed to remove the artefacts; the best artefact removal has been obtained when the spatial displacement was approximately of the same magnitude as the dimension of the lesion. Subsequently, a phantom validation of a low complexity MWI device (based on HP) operating in free space in the 1-6.5 GHz frequency band using two antennas in free space has been applied. Detection has been achieved in both bone fracture lesion and bone marrow lesion scenarios using superimposition of five doublet transmitting positions after applying the rotation subtraction method to remove artefact. A resolution of 5 mm and the S/C (3.35 in linear scale) are achieved which is clearly confirming the advantage of employing multiple transmitting positions on increased detection capability. The finding of this research verifies the dedicated MWI device as a simple, safe and without any X-ray radiation, portable, and low complexity method, which is capable of been successfully used for bone lesion detection. The outcomes of this thesis may pave the way for the construction of a dedicated bone imaging system that in future could be used as a safe diagnostic device even in emergency sites

Evaluating a breast tumor monitoring vest with flexible UWB antennas and realistic phantoms:a proof-of-concept study

Abstract. The introduction provides an overview of the global significance of breast cancer as a health concern and the limitations of existing breast cancer screening methods. It introduces the concept of microwave-based breast cancer monitoring and highlights the promising findings from a previous research paper. The objective of the master thesis is presented, which is to develop and evaluate a self-monitoring vest equipped with UWB antennas and channel analysis to overcome the limitations of current screening methods and enable regular breast cancer monitoring from home. The "Background and Literature Review," provides a comprehensive overview of the relevant topics related to microwave techniques for breast cancer detection. It starts by discussing the anatomy of the female breast, highlighting the importance of understanding its structure for effective tumor detection. The section then delves into the microwave properties of the human breast, elucidating the interactions between microwaves and breast tissue. The basic principle of microwave channel analysis is explained, emphasizing its significance in detecting breast tumors. Furthermore, the advantages of microwave-based tumor detection methods are explored, showcasing their potential for improved breast cancer screening. Various microwave techniques used in breast cancer detection, including microwave tomography and radar-based UWB microwave imaging, are discussed, along with different self-monitoring vests integrated with UWB antennas. This section serves as a foundation for the subsequent chapters of the thesis, providing a comprehensive background and literature review to support the research and development of the practical self-monitoring vest for early detection of small-sized breast tumors. The "Preparation of Tissue Phantoms" section in the master’s thesis explores the comprehensive methodology for creating tissue phantoms that replicate the dielectric properties of various human tissues. While the section primarily focuses on fat tissue, it also acknowledges the existence of other phantom types. The outlined approach involves careful ingredient selection, formulation development, fabrication techniques, and stability evaluation for the creation of skin, muscle, fat, tumor, and gland tissue phantoms. By following these procedures, researchers can successfully produce tissue phantoms that closely mimic the properties of real human tissues. These phantoms serve as essential tools for investigating microwave-based applications in medical diagnostics and provide a reliable and versatile platform for further research in the field. The third section discusses the assembly of heterogeneous breast phantoms used for evaluating the performance of the tumor detection vest. The phantoms consisted of outer and inner molds, with the outer molds resembling the shape of a prone human breast. Two breast density types, representing very dense and less dense breasts, were used. For the dense breast phantoms, liquid fat material was solidified in the outer molds, and a glandular liquid was poured into the inner mold, with tumors inserted and covered with additional glandular liquid. For the less dense breast phantoms, fat liquid was solidified in the outer molds, and cylindrical glandular molds were inserted. A skin layer and muscle layer were added to complete the assembly, accurately simulating the composition and structure of a breast. This realistic breast phantom assembly allowed for accurate measurements and evaluation of the vest’s performance under different breast density conditions, contributing to breast imaging research and development. The "Monitoring Vest" section discusses the antennas used in the tumor detection vest and the design of two different vest versions. Antenna1 is a UWB monopole antenna with a flexible laminate substrate, while Antenna2 is a textile-based version of Antenna1. Antenna3 has a Kapton-based substrate and larger dimensions. The combination of these antennas ensures accurate tumor detection in various breast conditions. The section also highlights the measurement and comparison of the S11 parameter for the PCB antenna in free space and when placed on the skin, emphasizing the impact of the skin on antenna performance. The section concludes by describing the design of the vests, including the arrangement of pockets and the use of RF cables for connection. The careful design and implementation of the vests and antenna placement ensure accurate measurements and reliable performance evaluation. The results section of the study shows that the presence of tumors in breast tissue leads to a noticeable decrease in channel attenuation. The higher dielectric properties of tumors cause additional reflections and diffraction, affecting signal propagation within the breast. These changes in channel characteristics are influenced by factors such as tumor size, breast density, and antenna configuration. The study demonstrates the detectability of tumors and provides valuable insights for developing effective tumor detection systems in different breast tissue scenarios. In this master thesis, a prototype of a breast tumor monitoring vest utilizing UWB flexible antennas was developed and evaluated. The research demonstrated the effectiveness of the vest in detecting breast tumors, even as small as 1cm, by leveraging the distinct characteristics of radio channels among multiple on-body antennas embedded in the vest. Higher frequencies in the 7–8 GHz range showed improved resolution and contrast in relative permittivity, enhancing the accuracy of tumor detection. The development of tissue phantoms played a crucial role, enabling reliable experiments to mimic human tissues. Integration of advanced AI algorithms and 6G technology holds promise for enhancing diagnostic capabilities and revolutionizing healthcare. Overall, the breast tumor monitoring vest shows potential for widespread implementation in breast health checks, home monitoring, and wireless healthcare systems

Reconstruction of Microwave Imaging using Machine Learning

Tese de mestrado, Engenharia Biomédica e Biofísica, 2022, Universidade de Lisboa, Faculdade de CiênciasBreast cancer is the most diagnosed cancer in women. The gold standard technique for mass screening is X-ray mammography, which requires the use of ionising radiation. Mammography has a high false positive rate for women under 50, since the technique is highly sensitive to breast density. Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and Ultrasound Imaging (US) have been suggested as complementary imaging tools to lessen the false positive results; however present some disadvantages. The potential of using microwave signals for breast cancer detection and monitoring has been studied for over 20 years. Microwave Breast Imaging (MBI) is a low-cost, non-invasive and non-ionising technique. The reflected microwave signals are transformed into an image via beamforming algorithms. These images have limited resolution, which may result in a considerable high rate of false positives and false negatives. In this dissertation, a complementary method of image reconstruction using Machine Learning (ML) models to predict the healthy or tumorous nature of breast is proposed. To study the potential of the proposed method, microwave signals were collected with a monostatic radar-based microwave system. The signal was acquired from three breast phantoms: one mimicking a homogeneous breast and two mimicking heterogeneous breasts. The phantoms had a cavity to introduce a plug, which included types of tumour models in terms of malignancies. From the signals, portions with and without tumour signature were extracted to train classification models. The most robust models were used to reconstruct a binary image of the breast with values of “hit” for tumorous focal points, and values of “miss” for healthy focal points. Eventually, the reconstructed images resulting from the proposed method were compared with the images obtained using the traditional beamforming method, DAS. Overall, the results obtained with the method ML-based were satisfactory, since for most phantoms the regions classified as tumour, indeed corresponded to the real position of the tumour

A discrete dipole approximation solver based on the COCG-FFT algorithm and its application to microwave breast imaging

We introduce the discrete dipole approximation (DDA) for efficiently calculating the two-dimensional electric field distribution for our microwave tomographic breast imaging system. For iterative inverse problems such as microwave tomography, the forward field computation is the time limiting step. In this paper, the two-dimensional algorithm is derived and formulated such that the iterative conjugate orthogonal conjugate gradient (COCG) method can be used for efficiently solving the forward problem. We have also optimized the matrix-vector multiplication step by formulating the problem such that the nondiagonal portion of the matrix used to compute the dipole moments is block-Toeplitz. The computation costs for multiplying the block matrices times a vector can be dramatically accelerated by expanding each Toeplitz matrix to a circulant matrix for which the convolution theorem is applied for fast computation utilizing the fast Fourier transform (FFT). The results demonstrate that this formulation is accurate and efficient. In this work, the computation times for the direct solvers, the iterative solver (COCG), and the iterative solver using the fast Fourier transform (COCG-FFT) are compared with the best performance achieved using the iterative solver (COCG-FFT) in C++. Utilizing this formulation provides a computationally efficient building block for developing a low cost and fast breast imaging system to serve under-resourced populations

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