Microwave Imaging System Research Articles

Directivity enhancement of microstrip antennas for high-resolution brain tumor imaging using characteristic modes theory and the confocal microwave image reconstruction algorithm

The increasing prevalence of brain tumors necessitates the development of advanced diagnostic techniques to enhance detection and characterization. This paper presents innovative methodologies for designing and optimizing antenna characteristics using characteristic modes theory (CMT), specifically adapted for high-resolution imaging in medical applications. Our research focuses on the critical goal of improving the accuracy and precision of brain tumor detection through a confocal microwave image reconstruction algorithm. The study begins with an in-depth modeling of essential antenna elements, examining their behavior to understand their interactions within the overall structure. This comprehensive analysis enhances our understanding of antenna performance and characteristics. The introduction of CMT is pivotal, as it facilitates the identification of resonance frequencies that exhibit exceptional radiation efficiency. Moreover, the antenna’s directivity is significantly enhanced through a thorough investigation of the effects of various substrate materials and patch shapes on the performance of the radiated antenna modes. This study prioritizes the optimization of the dominant directive mode to improve tumor imaging resolution, ultimately leading to superior quality imaging results. To compare and analyze the impact of different antenna directivity modes on the imaging resolution of brain tumors, two optimized antennas with distinct patch shapes and radiation patterns are integrated into a microwave imaging system. This advanced system is carefully designed to accurately locate and characterize brain tumors, enhancing diagnostic precision. The confocal imaging algorithm demonstrates that the dominant mode with high directivity radiation produces high-resolution images that significantly improve tumor detection and diagnosis.

Machine learning‐driven microwave imaging for soil moisture estimation near leaky pipe

AbstractCharacterizing soil moisture around drip irrigation pipes is crucial for precise and optimized farming. Machine learning (ML) approaches are particularly suitable for this task as they can reduce uncertainties caused by soil conditions and the drip pipe positions, using features extracted from relevant datasets. This letter addresses local moisture detection in the vicinity of dripping pipes using a portable microwave imaging system. The employed ML approach is fed with two dimensional images generated using back projection as a radar‐based algorithm and the Born approximation as an inverse scattering method, based on spatio‐temporal (collected data at various positions over the soil surface and at different time points.) measurements at various frequencies. The study investigates the performance of K‐nearest neighbour (KNN) and convolutional neural networks (CNN) algorithms for moisture classification based on these imaging techniques. We also explore the potential of KNN and CNN for moisture estimation around the plant roots and in the presence of pebbles. In general, CNN outperforms KNN in moisture content detection from microwave data, especially after applying imaging algorithms. A combination of CNN as the ML approach and the back projection algorithm to provide training data, yielded accuracy more than other models for moisture content estimation. Also, the practical results demonstrate that our method can detect soil moisture with an estimation error of less than 10%.

Deep Learning-Based Electric Field Enhancement Imaging Method for Brain Stroke

In clinical settings, computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) are commonly employed in brain imaging to assist clinicians in determining the type of stroke in patients. However, these modalities are associated with potential hazards or limitations. In contrast, microwave imaging emerges as a promising technique, offering advantages such as non-ionizing radiation, low cost, lightweight, and portability. The primary challenges faced by microwave tomography include the severe ill-posedness of the electromagnetic inverse scattering problem and the time-consuming nature and unsatisfactory resolution of iterative quantitative algorithms. This paper proposes a learning electric field enhancement imaging method (LEFEIM) to achieve quantitative brain imaging based on a microwave tomography system. LEFEIM comprises two cascaded networks. The first, based on a convolutional neural network, utilizes the electric field from the receiving antenna to predict the electric field distribution within the imaging domain. The second network employs the electric field distribution as input to learn the dielectric constant distribution, thereby realizing quantitative brain imaging. Compared to the Born Iterative Method (BIM), LEFEIM significantly improves imaging time, while enhancing imaging quality and goodness-of-fit to a certain extent. Simultaneously, LEFEIM exhibits anti-noise capabilities.

Improving the Imaging Performance of Microwave Imaging Systems by Exploiting Virtual Antennas

Improving the Imaging Performance of Microwave Imaging Systems by Exploiting Virtual Antennas

Detection of malignant breast tissue using SAR observation with microwave imaging and convolutional neural network

The research paper introduces a deep learning approach for distinguishing between benign and malignant breast tissues using Ultra Wide Band (UWB) microwave imaging. A multi-resonant monopole microstrip patch antenna was crafted and placed on a female breast phantom model containing a tumor. The phantom’s dielectric properties were adjusted to mimic those of benign and malignant tissues. The Specific Absorption Rate (SAR) was analyzed by rotating the antenna at various angles throughout the phantom’s trajectory. Images capturing the SAR distribution over the breast phantom at different antenna resonances were obtained. The popular Convolutional Neural Network (CNN) based AlexNet model was employed for autonomous feature learning from SAR images. To address the challenge of overfitting with a limited dataset, the study utilized image augmentation and transfer learning methods. The proposed model achieved an impressive 98.59% accuracy in classifying benign and malignant breast phantom images, surpassing the performance of three other CNN models—VggNet16, GoogleNet, and ResNet. Notably, this microwave imaging system based on SAR images detected tumors as small as 1 mm with an accuracy of 97.08% outperforming existing malignancy screening techniques.

Breast tumor detection with TR‐DORT based on multilayered Green's function theory

AbstractUltrawideband (UWB) time‐reversal techniques are widely used in microwave imaging systems to detect and localize breast tumors. Measuring the scattering electromagnetic fields from a breast phantom can be time‐reversed and back‐propagated to refocus on a tumor. In this study, a Debye model was applied to the breast phantom. Using the decomposition of the time‐reversal operator (TR‐DORT), images were obtained in the UWB frequency range of 3.1–10.6 GHz. The conventional TR imaging method employs the free‐space Green's function to refocus on the tumor, which degrades refocusing in a lossy and dispersive breast phantom. We propose the use of the cylindrical dyadic Green's function in a three‐layer medium for TR imaging. The excitation system consists of a circular array of spiral antennas. The imaging results of the proposed Green's function in TR‐DORT were subsequently calculated, indicating successful tumor detection in different application scenarios of the lossy and dispersive breast phantom.

Design and experimental validation of a metamaterial-based sensor for microwave imaging in breast, lung, and brain cancer detection

This study proposes an innovative geometry of a microstrip sensor for high-resolution microwave imaging (MWI). The main intended application of the sensor is early detection of breast, lung, and brain cancer. The proposed design consists of a microstrip patch antenna fed by a coplanar waveguide with a metamaterial (MTM) layer-based lens implemented on the back side, and an artificial magnetic conductor (AMC) realized on as a separate layer. The analysis of the AMC’s permeability and permittivity demonstrate that the structure exhibits negative epsilon (ENG) qualities near the antenna resonance point. In addition, reflectivity, transmittance, and absorption are also studied. The sensor prototype has been manufactures using the FR4 laminate. Excellent electrical and field characteristics of the structure are confirmed through experimental validation. At the resonance frequency of 4.56 GHz, the realized gain reaches 8.5 dBi, with 3.8 dBi gain enhancement contributed by the AMC. The suitability of the presented sensor for detecting brain tumors, lung cancer, and breast cancer has been corroborated through extensive simulation-based experiments performed using the MWI system model, which employs four copies of the proposed sensor, as well as the breast, lung, and brain phantoms. As demonstrated, the directional radiation pattern and enhanced gain of the sensor enable precise tumor size discrimination. The proposed sensor offers competitive performance in comparison the state-of-the-art sensors described in the recent literature, especially with respect to as gain, pattern directivity, and impedance matching, all being critical for MWI.

A low-cost UWB microwave imaging system for early-stage breast cancer detection

A low-cost UWB microwave imaging system for early-stage breast cancer detection

Ultra-wideband Imaging of Breast Tumors Based on Global Back Projection Algorithm

Abstract Tumor imaging in medicine requires consideration of human safety, image quality clarity, and cost of imaging. Ultra-wideband (UWB) technology employs large bandwidth channels that enable high-speed data transmission. This determines that it can provide high-quality images quickly while still being a harmless imaging method. Therefore, it is essential to construct a UWB microwave detection tumor system. The creation of a vector network analyzer (VNA) based microwave imaging system in this work. To adapt to breast imaging, this paper improved the imaging algorithm of global back projection (GBP) based on the classical back projection algorithm. To verify the system’s feasibility, this paper performed UWB detection experiments using a breast tumor model. By comparing the simulation results, the final imaging results show that the UWB imaging system can detect tumors located at two different locations in the model and that high-quality images can be obtained in the near field using the GBP algorithm. This paper provides a reliable and low-cost method for medical diagnosis.

Abstract PO3-07-08: Breast density estimation with a microwave-frequency imaging system

Abstract Background: Microwave imaging has been proposed as an alternative method of breast imaging that is low-cost and comfortable for women as it avoids excessive compression. Microwave properties of tissues relate to water content and behavior (Gabriel et al, 1996); specifically, fatty tissues have lower properties and glandular tissues have greater properties (Lazebnik et al, 2007). These differences in microwave signatures of fatty and glandular tissues provide the opportunity to map the composition of the breast and create a density score without a mammogram. This density score may find utility in risk stratification or monitoring interventions aimed at decreasing breast density (Salazar et a, 2020). Purpose: We examine the feasibility of developing a density score based on microwave images that correlates to mammographic breast density (VOLPARA score) in a pilot study with healthy volunteers. Imaging System: We have developed a microwave imaging system that facilitates scanning of large groups of patients, as well as comparison to x-ray mammography (Mojabi et al, 2023). The system consists of two plates which are placed in contact with the breast. Microwave transmitters and receivers are embedded in the plates; signals transmitted through the breast are used to estimate microwave frequency properties of tissues, and maps of these estimates form a 2D image. Methods: 50 patients provided informed consent (study approved by Health Ethics Research Board of Alberta CC-21-0082). Both breasts of each volunteer were scanned. Previously performed mammograms were available for 21 of the volunteers. The number of volunteers with VOLPARA scores A, B, C, and D is 2, 8, 6, and 5, respectively. The percent density reported with the VOLPARA score is also available for these volunteers. Microwave images were formed for each scan and analyzed to predict density with three approaches: (1) average permittivity, (2) segmented regions, and (3) pixel-based intensities. The results demonstrate that the average permittivity of the breast typically increases from VOLPARA A to D, with some overlap in average values observed between the density categories. A correlation between average permittivity and percent breast density was observed. Regions representing glandular tissues are segmented from microwave images; the average values of these regions clearly differentiate between VOLPARA A and D, however do not show consistent ranges for VOLPARA scores B and C. Finally, the microwave breast density estimated using pixel-based intensities shows good correlation with the percentage density calculations from mammograms. Conclusions: Microwave images contain features related to the glandular tissues embedded in fat. By analyzing the composition of the breast in these images, density scores are created. While average permittivity appears to correlate to percent density calculated from mammograms, area or pixel-based approaches appear to have greater potential for categorizing into density classes. Expanding the number of participants, identifying biomarkers, and exploring deep learning techniques for density prediction are considered for future work Citation Format: Elise Fear, Jeremie Bourqui, Pedram Mojabi, Bobbie-Jo Docktor, Anita Garland, Danielle Deutscher, Zahra Lasemiimeni, Kathleen McMahon, Brendon Besler, Roger Tsang. Breast density estimation with a microwave-frequency imaging system [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-07-08.

Subcranial Encephalic Temnograph-Shaped Helmet for Brain Stroke Monitoring.

In this contribution, a wearable microwave imaging system for real-time monitoring of brain stroke in the post-acute stage is described and validated. The system exploits multistatic/multifrequency (only 50 frequency samples) data collected via a low-cost and low-complexity architecture. Data are collected by an array of only 16 antennas moved by pneumatic system. Phantoms, built from ABS material and filled with appropriate Triton X-100-based mixtures to mimic the different head human tissues, are employed for the experiments. The microwave system exploits the differential scattering measures and the Incoherent MUSIC algorithm to provide a 3D image of the region under investigation. The shown results, although preliminary, confirm the potential of the proposed microwave system in providing reliable results, including for targets whose evolution is as small as 16 mL in volume.

Filtenna Designed with Defected Ground Structure (DGS) for Ultra-wideband Applications

In microwave imaging applications, filter and antenna are the key components as front-end devices and function independently. Antenna radiates and receives signals to or from nearby scattered objects while filter is used to suppress unwanted signals noise before and after the required bandwidth. Current antennas suffer from high loss, bandwidth limitation and impedance mismatch and deteriorate their performance near the band-edges if connected as a stand-alone device. Due to the current trend towards simplicity and size reduction, researchers are focusing on integrating the filter and antenna into a single module called integrated filter-antenna (IFA) or filtering antenna (filtenna). These would improve the noise performance of the system and pre-filtering requirements such as complexity algorithm in inverse scattering techniques This paper introduces a novel contribution in the field of UWB antenna design by incorporating Defected Ground Structure (DGS) on both the antenna and filter. Thus, the main aim of this research is to conduct detail parametric studies of the proposed integrated filter-antenna with defected ground structure (DGS) to enhance the performance of imaging system. The proposed IFA will be analysed on Rogers RO4003C dielectric substrate by using EM tool, Computer Simulation Technology (CST) and measured using R&S Vector Network Analyzer (VNA). A compact ultra-wideband (UWB) filter and UWB elliptical antenna are designed and examined in detail before combining the devices. Different types of DGS are designed and act as the ground layer of the proposed filtering antenna. The bandwidth of each design is then compared with and without the existence of DGS. The results show that the proposed IFA with DGS implementation achieve the targeted objective, with compact size, enhanced bandwidth and better performance compared to the conventional design of filter and antenna. By integrating filter and antenna into one subsystem, it can help to reduce the loss and enhancing the bandwidth of the system thus letting the antenna to operate at more different frequencies that fall within the range. Both simulated and measured results prove that by integrating filter and antenna into one module, a low loss and larger bandwidth can be accomplished. High performance compact IFA can act as microwave transceiver to improve the overall performance of the microwave imaging system, MIS.

Microwave Imaging System Based on Signal Analysis in a Planar Environment for Detection of Abdominal Aortic Aneurysms.

A proof-of-concept of a microwave imaging system for the fast detection of abdominal aortic aneurysms is shown. This experimental technology seeks to overcome the factors hampering the fast screening for these aneurysms with the usual equipment, such as high cost, long-time operation or hazardous exposure to chemical substances. The hardware system is composed of 16 twin antennas mastered by a microcontroller through a switching network, which connects the antennas to the measurement instrument for sequential measurement. The software system is run by a computer, mastering the whole system, automatizing the measurement process and running the signal processing and medical image generation algorithms. Two image generation algorithms are tested: Delay-and-Sum (DAS) and Improved Delay-and-Sum (IDAS). Own-modified versions of these algorithms adapted to the requirements of our system are proposed. The system is carefully calibrated and fine-tuned with known objects placed at known distances. An experimental proof-of-concept is shown with a human torso phantom, including an aorta phantom and an aneurysm phantom placed in different positions. The results show good imaging capabilities with the potential for detecting and locating possible abdominal aortic aneurysms and reporting acceptable errors.

Ultra-thin and omnidirectional reflectionless absorbing surface working at dual-frequency

ABSTRACT Materials with characteristics of omnidirectional reflectionless absorption are of great importance in electromagnetic shielding and anechoic chamber. Recently emerged absorbing surfaces mostly work at a given angle, and absorbers working at a broad frequency band usually suffer from a 3-D structure. Thus, the fabrication of the ultra-thin and omnidirectional reflectionless absorber working at dual-frequency remains a technical challenge. In this work, we demonstrated the absorbing performance of an ultra-thin slab under different incident angles of the electric fields, and an approximate solution was derived to prove the realizability of the dual-frequency and omnidirectional reflectionless absorption. For illustration, a miniaturized dual-frequency anechoic chamber with a 343.8 mm diameter was constructed, and the full-wave simulations validated the results. The proposed design method is simple and robust, which provides a new clue for solving the practicability difficulty faced by existing anechoic chambers and microwave imaging systems.

Detection of Breast Tumour Depth using Felt Substrate Textile Antenna

Breast cancer is a frightening type of cancer for women globally, including in Indonesia. Early detection of cancer symptoms is crucial to reducing mortality rates. Microwave imaging systems using the ultra-wideband (UWB) can detect objects, particularly in medical diagnosis. The textile antenna is the latest development in antenna technology that can be used on the human body. This antenna can be bent and adjusted to the body's complex shape. This research proposed a textile antenna using the felt dielectric substrate material operating at 3.8 GHz. The simulation results showed a return loss value of -47.5138 dB and a bandwidth of 1.4 GHz. The antenna was also tested on a breast phantom with a tumour scenario, showing higher amplitude in the quadrant with the tumour. The fabrication was done manually through laser cutting and heat-assisted bonding. The antenna made of the felt substrate material could detect breast tumours’ multiple depths or layers using the graphical user interface (GUI) programming.

Visualising the Arc-shaped Rib Bone Embedded in Wood Shavings: Advancing Toward Microwave Imaging Systems for Preclinical Orthopaedic Applications

Visualising the Arc-shaped Rib Bone Embedded in Wood Shavings: Advancing Toward Microwave Imaging Systems for Preclinical Orthopaedic Applications

In silico, in vitro, and in vivo validation of a microwave imaging system using a low‐profile Ultra Wide Band Archimedean spiral antenna to detect skin cancer

AbstractMicrowave imaging (MI) is a noninvasive and nonionizing procedure for detection of cancerous cells in healthy body tissues using radiofrequency (RF) and microwaves. The procedure involves the use of Ultra Wide Band (UWB) antennas for sensing purposes. Therefore, this research article presents the design, development, and testing of a low‐profile UWB Archimedean spiral microstrip‐patch antenna (ASMA) for detection of skin cancer using monostatic radar‐based microwave imaging. The proposed ASMA consists of a spiral resonator with a defective ground structure and a slotted microstrip feed line with dimensions of 38 × 38 × 0.87 mm3. The proposed antenna shows an impedance bandwidth for the frequency range of 2.2–13.9 GHz, with a peak gain of 6.8 dB at 7.8 GHz. In silico analysis of the proposed ASMA for MI is carried out with Gaustav model using Computer Simulation Technology Microwave Studio. To validate the performance of the ASMA as a sensor for MI, a prototype of the same is fabricated and a four‐layered bio‐phantom of the human forearm is prepared for in vitro and in vivo testing of the proposed procedure. The validation of ASMA radiation properties is done using a Vector Network Analyser (E‐5063A) (VNA) and an anechoic chamber with the fabricated antenna at 10 mm away from the prepared bio‐phantom. The recorded S parameter data with bio‐phantom and the VNA is processed using different beamforming algorithms like Delay and Sum and Coherent Factor‐Delay Multiply and Sum (CF‐DMAS) to reconstruct the image of the scanned area. The reconstructed images are 97%–98% accurate. The proposed ASMA sensor is also safe for human exposure as it has a specific absorption rate of 0.0546 W/Kg at 5 GHz that complies with the safety guidelines of the Federal Communications Commission to minimize potential health risks associated with exposure to RF and microwave radiation.

Visualising the arc-shaped rib bone embedded in wood shavings: advancing toward microwave imaging systems for preclinical orthopaedic applications

Visualising the arc-shaped rib bone embedded in wood shavings: advancing toward microwave imaging systems for preclinical orthopaedic applications

A Novel Switch for Microwave Imaging Systems

A Novel Switch for Microwave Imaging Systems

Design of Antipodal Vivaldi Antenna for Medical Imaging Application

The microwave imaging (MWI) system in medical applications is commonly used to detect abnormalities in the human body. The purpose of this study was to design an Antipodal Vivaldi Antenna (AVA) for medical imaging applications using MWI. The research method used is based on the AVA design simulation of the CST Studio Suite 2019 application using time and frequency domain methods, which has dimensions of 60x40 mm2 with an antenna structure that works in the frequency range of 6.3-9.6 GHz, the impedance for bandwidth is -10 dB, using Flame Retardant-4 (FR-4) thickness 1.6 mm (= 4.3, tan δ = 0.025) as substrate material. A linear array of antennas was utilized in the simulation, either with or without a phantom. The phantom options include an absence of a phantom (only antennas), a cube-shaped water phantom, and a water phantom containing an anomaly. The result of the simulation on the AVA design produces a bandwidth of 41.61%, a gain of 5.16 dB, a return loss of -26.73 dB, a Specific Absorption Rate (SAR) value of 0.26 W/kg and a graph of S-parameters (S21). It can be concluded that the MWI system using the AVA design in this study has the potential to properly detect the presence of anomalies.