Search Results (100)

Breast cancer detection through non-invasive and accurate techniques remains a critical challenge in medical diagnostics. This study introduces a deep learning-based framework that leverages a microwave radar system equipped with an arc-shaped array of six antennas to estimate key tumor parameters, including position, size, and depth. This research begins with the evolutionary design of an ultra-wideband octagram ring patch antenna optimized for enhanced tumor detection sensitivity in directional near-field coupling scenarios. The antenna is fabricated and experimentally evaluated, with its performance validated through S-parameter measurements, far-field radiation characterization, and efficiency analysis to ensure effective signal propagation and interaction with breast tissue. Specific Absorption Rate (SAR) distributions within breast tissues are comprehensively assessed, and power adjustment strategies are implemented to comply with electromagnetic exposure safety limits. The dataset for the deep learning model comprises simulated self and mutual S-parameters capturing tumor-induced variations over a broad frequency spectrum. A core innovation of this work is the development of the Attention-Based Feature Separation (ABFS) model, which dynamically identifies optimal frequency sub-bands and disentangles discriminative features tailored to each tumor parameter. A multi-branch neural network processes these features to achieve precise tumor localization and size estimation. Compared to conventional attention mechanisms, the proposed ABFS architecture demonstrates superior prediction accuracy and interpretability. The proposed approach achieves high estimation accuracy and computational efficiency in simulation studies, underscoring the promise of integrating deep learning with conformal microwave imaging for safe, effective, and non-invasive breast cancer detection. Full article

Here, we propose an analytical approach to simulating MammoWave, a novel apparatus for breast cancer detection using microwave imaging. The approach is built upon the theory of cylindrical waves emitted by line sources. The sample is modelled as a cylinder with an inclusion. Our results indicate that when compared with phantom measurements, our approach gives an average relative error (between the image generated through measurement with phantoms and the image generated through the analytical simulation approach) of less than 6% when considering the full frequency band of 1–9 GHz. The procedure permits the simulation of the MammoWave imaging system loaded with multilayered eccentric cylinders; thus, it can be used to obtain an insight into MammoWave’s detection capability, without having to perform either time-consuming full-wave simulations or phantom measurements. Full article

In this study, a novel improved ultra-wideband (UWB) antipodal Vivaldi antenna suitable for breast cancer detection via microwave imaging was designed. The antenna was made more directional by adding three pairs of nestings to the antenna fins by adding elliptical patches. The frequency operating range of the proposed antenna is UWB 3.6–13 GHz, its directivity is 11 dB, and its gain is 9.27 dB. The antenna is designed with FR4 dielectric material and dimensions of 34.6 mm × 33 mm × 1.6 mm. It was demonstrated that the bandwidth, gain, and directivity of the proposed antenna meet the requirements for UWB radar applications. The Vivaldi antenna was tested on an imaging system developed using the CST Microwave Studio (CST MWS) program. In CST MWS, a hemispherical heterogeneous breast model with a radius of 50 mm was created and a spherical tumor with a diameter of 0.9 mm was placed inside. A Gaussian pulse was sent through Vivaldi antennas and the scattered signals were collected. Then, adaptive Wiener filter and image formation algorithm delay-multiply-sum (DMAS) steps were applied to the reflected signals. Using these steps, the tumor in the breast model was scanned at high resolution. In the simulation application, the tumor in the heterogeneous phantom was detected and imaged in the correct position. A monostatic radar-based system was implemented for scanning a breast phantom in the prone position in an experimental setting. For experimental measurements, homogeneous (fat and tumor) and heterogeneous (skin, fat, glandular, and tumor) breast phantoms were produced according to the electrical properties of the tissues. The phantoms were designed as hemispherical with a diameter of 100 mm. A spherical tumor tissue with a diameter of 16 mm was placed in the phantoms produced in the experimental environment. The dynamic range of the VNA device used allowed us to image a 16 mm diameter tumor in the experimental setting. The developed microwave imaging system shows that it is suitable for the early-stage detection of breast cancer by scanning the tumor in the correct location in breast phantoms. Full article

This study focuses on breast imaging. A spectral approach based on the Fourier diffraction theorem is combined with a pair of U-NETs to perform real-time quantitative human breast imaging. The U-NET pair is trained based on the input of an induced current spectrum and the output of a contrast dielectric spectrum. A spectral database is constructed using combinations of anthropomorphic cavities. The weighted mean absolute percentage error (WMAPE) loss is associated with the Adam optimizer to perform optimization. Numerical results are presented to validate the proposed concept to demonstrate the transformation brought about by the U-NETs. Full article

The impact of breast cancer on public health is serious, and due to risk/benefit assessment, screening programs are usually restricted to women older than 49 years. Microwave imaging devices offer advantages such as non-ionizing radiation, low cost, and the ability to distinguish between cancerous and healthy tissues due to their electrical properties. Ensuring the safety of this technology is vital for its potential clinical application. To estimate the temperature increase in breast tissues from a microwave imaging scanner, cases of healthy, benign, and malignant breast tissues were analyzed using three digital models and adding two healthy breast models with varying densities. Virtual experiments were conducted using the Sim4Life software (version 7.2) with a system consisting of a horn antenna in transmission and a Vivaldi antenna in reception. Temperature increases were estimated based on the Specific Absorption Rate distributions computed for different configurations and frequencies. The highest temperature increase obtained in this analysis is lower than 60 μK in fibroglandular tissue or skin, depending on the frequency and breast density. The presence of a receiving antenna acting as a scatterer modifies the temperature increase, which is almost negligible. Microwave examination can be performed without harmful thermal effects due to electromagnetic field exposure. Full article

Microwave tomography serves as a promising non-invasive technique for breast imaging, yet accurate reconstruction in noisy environments remains challenging. We propose an adaptive subspace-based two-step iterative shrinkage/thresholding (S-TwIST) algorithm that enhances reconstruction accuracy through two key innovations: a singular value decomposition (SVD) approach for extracting deterministic contrast sources, and an adaptive strategy for optimal singular value selection. Unlike conventional DBIM methods that rely solely on secondary incident fields, S-TwIST incorporates deterministic induced currents to achieve more accurate total field approximation. The algorithm’s performance is validated using both synthetic “Austria” profiles and 45 digital breast phantoms derived from the UWCEM repository. The results demonstrate robust reconstruction capabilities across varying noise levels (0–20 dB SNR), achieving average relative errors of 0.4847% in breast tissue reconstruction without requiring prior noise level knowledge. The algorithm successfully recovers complex tissue structures and density distributions, showing potential for clinical breast imaging applications. Full article

Background: Early and accurate detection of breast cancer is crucial for improving treatment outcomes and survival rates. To achieve this, innovative imaging technologies such as microwave radiometry (MWR)—which measures internal tissue temperature—combined with advanced diagnostic methods like deep learning are essential. Methods: To address this need, we propose a hierarchical self-contrastive model for analyzing MWR data, called Joint-MWR (J-MWR). J-MWR focuses on comparing temperature variations within an individual by analyzing corresponding sub-regions of the two breasts, rather than across different samples. This approach enables the detection of subtle thermal abnormalities that may indicate potential issues. Results: We evaluated J-MWR on a dataset of 4932 patients, demonstrating improvements over existing MWR-based neural networks and conventional contrastive learning methods. The model achieved a Matthews correlation coefficient of 0.74 ± 0.02, reflecting its robust performance. Conclusions: These results emphasize the potential of intra-subject temperature comparison and the use of deep learning to replicate traditional feature extraction techniques, thereby improving accuracy while maintaining high generalizability. Full article

Conventional circularly polarized antennas have been employed to deliver microwave illumination in microwave-induced thermoacoustic tomography (TAT). However, these antennas exhibit several limitations in TAT systems, including low efficiency, poor axial ratio (AR) roundness, and narrow axial ratio beamwidth (ARBW). These issues lead to uniform radiation only within a relatively confined area, thereby restricting their effectiveness in clinical applications such as breast imaging. To address these issues, we propose a novel planar slot array antenna that offers a wide ARBW and improved axial ratio (AR) roundness, enabling homogeneous illumination over a larger field. We validated this approach both theoretically and experimentally. Tissue-mimicking phantoms were imaged, demonstrating that the antenna generated a circularly polarized electric field as well as a uniformly illuminated area. These advantages make the antenna proposed in this paper more suitable for clinical imaging compared to traditional microwave radiating antennas. Full article

Background/Objectives: Breast cancer is a significant global health concern, and early detection is crucial for improving patient outcomes. Mammography is widely used but has limitations, particularly for younger women with denser breasts. These include reduced sensitivity, false positives, and radiation risks. This highlights the need for alternative screening methods. In this study, we assess the performance of SAFE (Scan and Find Early), a novel microwave imaging device, in detecting breast cancer in a larger patient cohort. Unlike previous studies that predominantly relied on cross-validation, this study employs a more reliable, independent evaluation methodology to enhance generalizability. Methods: We developed an XGBoost model to classify breast cancer cases into positive (malignant) and negative (benign or healthy) groups. The model was analyzed with respect to key factors such as breast size, density, age, tumor size, and histopathological findings. This approach provides a better understanding of how these factors influence the model’s performance, using an independent evaluation methodology for increased reliability. Results: Our results demonstrate that SAFE exhibits high sensitivity, particularly in dense breasts (91%) and younger patients (83%), suggesting its potential as a supplemental screening tool. Additionally, the system shows high detection accuracy for both small (<2 cm) and larger lesions, proving effective in early cancer detection. Conclusions: This study reinforces the potential of SAFE to complement existing screening methods, particularly for patients with dense breasts, where mammography’s sensitivity is reduced. The promising results warrant further research to solidify SAFE’s clinical application as an alternative screening tool for breast cancer detection. Full article

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. Full article

In the medical field, microwave imaging technology has experienced rapid development due to its non-invasive and non-radioactive nature. The confocal algorithm is a method commonly used for microwave breast cancer imaging, with the key objective of removing clutter in images to achieve high-quality results. However, the current methods are facing challenges in removing clutter. In order to reduce the clutter in images, a multiplicative improved coherence factor delay multiply and sum algorithm based on the maximum interclass differencing method is proposed. The algorithm compares the starting and ending moments of tumor signals in different channels to determine whether the tumor-scattered signals in different channels overlap in time. An improved coherence coefficient is obtained by summing the non-overlapping signals and multiplying the time window. The multiplicative improved coherence factor, which is obtained by multiplying the coherence coefficients of the improved multi-pair signals, is then multiplied by the focal point intensity obtained using the delay multiply and sum algorithm to reduce clutter in an image. To evaluate the performance of the proposed algorithm, several low-cost uniform and non-uniform models of human breast and tumor tissue with dielectric properties were prepared for testing. The experimental results show that, compared to the existing algorithm, the proposed algorithm can greatly reduce the clutter in images, with a signal-to-clutter ratio of at least 4 dB higher as well as contrast at least six-fold higher. Full article

Focused microwave breast hyperthermia (FMBH) employs a phased antenna array to perform beamforming that can focus microwave energy at targeted breast tumors. Selective heating of the tumor endows the hyperthermia treatment with high accuracy and low side effects. The effect of FMBH is highly dependent on the applied phased antenna array. This work investigates the effect of polarizations of antenna elements on the microwave-focusing results by simulations. We explore two kinds of antenna arrays with the same number of elements using different digital realistic human breast phantoms. The first array has all the elements’ polarization in the vertical plane of the breast, while the second array has half of the elements’ polarization in the vertical plane and the other half in the transverse plane, i.e., cross polarization. In total, 96 sets of different simulations are performed, and the results show that the second array leads to a better focusing effect in dense breasts than the first array. This work is very meaningful for the potential improvement of the antenna array for FMBH, which is of great significance for the future clinical applications of FMBH. The antenna array with cross polarization can also be applied in microwave imaging and sensing for biomedical applications. Full article

Microwave medical imaging (MMI) is experiencing a surge in research interest, with antenna performance emerging as a key area for improvement. This work addresses this need by enhancing the directivity of a compact UWB antenna using a Yagi-Uda-inspired reflector antenna. The proposed reflector-loaded antenna (RLA) exhibited significant gain and directivity improvements compared to a non-directional reference antenna. When analyzed for MMI applications, the RLA showed a maximum increase of 4 dBi in the realized gain and of 14.26 dB in the transmitted field strength within a human breast model. Moreover, it preserved the shape of time-domain input signals with a high correlation factor of 94.86%. To further validate our approach, another non-directional antenna with proven head imaging capabilities was modified with a reflector, achieving similar directivity enhancements. The combined results demonstrate the feasibility of RLAs for improved performance in MMI systems. Full article

Microwave radar imaging is a diagnostic technique that is receiving significant attention in the research community for the striking advantages it may potentially offer. Nonetheless, diagnosis via microwave radar imaging is extremely difficult due to theoretical as well as practical reasons. In this contribution, in particular, we focus on the need to take frequency dispersion effects and the antenna’s frequency response into account. In more detail, we propose an imaging algorithm that works by completely ignoring the tissue frequency behaviour as well as the antenna’s response. The numerical results obtained via a full-wave electromagnetic solver for a simplified breast layout confirm the potential of the proposed approach. Full article

Breast cancer stands out as the most frequently identified malignancy, ranking as the fifth leading cause of global cancer-related deaths. The American College of Radiology (ACR) introduced the Breast Imaging Reporting and Data System (BI-RADS) as a standard terminology facilitating communication between radiologists and clinicians; however, an update is now imperative to encompass the latest imaging modalities developed subsequent to the 5th edition of BI-RADS. Within this review article, we provide a concise history of BI-RADS, delve into advanced mammography techniques, ultrasonography (US), magnetic resonance imaging (MRI), PET/CT images, and microwave breast imaging, and subsequently furnish comprehensive, updated insights into Molecular Breast Imaging (MBI), diagnostic imaging biomarkers, and the assessment of treatment responses. This endeavor aims to enhance radiologists’ proficiency in catering to the personalized needs of breast cancer patients. Lastly, we explore the augmented benefits of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications in segmenting, detecting, and diagnosing breast cancer, as well as the early prediction of the response of tumors to neoadjuvant chemotherapy (NAC). By assimilating state-of-the-art computer algorithms capable of deciphering intricate imaging data and aiding radiologists in rendering precise and effective diagnoses, AI has profoundly revolutionized the landscape of breast cancer radiology. Its vast potential holds the promise of bolstering radiologists’ capabilities and ameliorating patient outcomes in the realm of breast cancer management. Full article