Search Results (73)

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

A non-uniform antenna array is proposed to enhance the accuracy of medical microwave imaging systems by increasing the amount of useful information captured about the imaged domain without increasing the number of antennas. These systems have so far been using uniform antenna arrays, which lead to highly correlated signals, limiting the amount of imaging information and adversely affecting diagnostic accuracy. In the proposed non-uniform antenna array method, the optimal number and positions of antennas are calculated with the aim of enhancing spatial diversity and reducing information redundancy. The mutual information coefficient is used as a metric to evaluate and minimize redundancy between received signals. A microwave head imaging system is used to verify the proposed approach. The results of the investigated scenarios show that using a non-uniform antenna configuration outperforms a uniform setup in imaging accuracy and clarity, when using the same number of antennas. Moreover, the reconstructed images demonstrate that using an optimized non-uniform antenna array with fewer elements can outperform a uniform array with more elements in terms of localization accuracy and image quality. The proposed approach improves imaging performance and reduces system complexity, cost, and power consumption, making it a practical solution for real-world biomedical imaging applications. Full article

Complex-valued neural networks have emerged as an effective instrument in image reconstruction, exhibiting significant advancements compared to conventional techniques. This study introduces an innovative methodology to tackle the difficulties related to image reconstruction within medical microwave imaging. Initially, in the estimation phase, the proposed methodology integrates the Born iterative method with quadratic programming. Subsequently, in the refinement stage, the study explores the application of complex-valued neural networks to enhance the quality of reconstructions. The research emphasizes distinct complex-valued neural network architectures, namely, CV-UNET, CV-CNN, CV-MLP, and their corresponding performances. CV-UNET stands out as the best architecture, surpassing conventional methods and the other complex-valued neural networks variants. The complex-valued neural network improves the fidelity of reconstructions and simplifies the procedure by obviating the need for multiple training steps, a common prerequisite in real-valued neural networks. Full article

Machine learning (ML) techniques have attracted many microwave researchers and engineers for their potential to improve performance in microwave- and millimeter-wave-based medical applications. This paper reviews ML algorithms, data acquisition, training techniques, and applications that have emerged in recent years. It also reviews state-of-the-art ML techniques applied for the detection of various organ diseases with microwave signals, achieving more successful results than using traditional methods alone, such as a higher diagnosis accuracy or spatial resolution and significantly improved efficiency. Challenges and the outlook of using ML in future microwave medical applications are also discussed. Full article

Predicting meat quality, especially dark, firm and dry meat, as well as muscle fat prior to slaughter, presents a challenge in practice. Medical as well as high-frequency ultrasound applications can be utilized to predict body composition and meat quality aspects. Ultrasounds are non-invasive, rapid-to-operate in vivo and show high correlations to the animal production traits being estimated. Farm animal ultrasounds are used to predict intramuscular fat content in the beef cattle industry. Challenges are identified in applying ultrasound technology to detect glycogen content in farm animals due to a wide range of fat, muscle and water composition. Other technologies and methods are reported in this literature review to overcome issues in the practicability and accuracy of ultrasound technology when estimating muscle glycogen levels in cattle. The discussion of other tools such as hyperspectral imaging, microwave sensor technology and digital infrared thermal imaging were addressed because of their superior accuracy in estimating moisture and fat components. Full article

Objectives: Anthropomorphic phantoms offer a promising solution to minimize animal testing, enable medical training, and support the efficient development of medical devices. The adjustable mechanical, biochemical, and imaging properties of the polyvinyl alcohol cryogel (PVA-C) make it an appropriate phantom material for mimicking soft tissues. Conventional manufacturing (CM) of aqueous solutions requires constant stirring, using a heated water bath, and monitoring. Methods: To explore potential improvements in the dissolution of PVA crystals in water, a microwave-based manufacturing method (MWM) was employed. Samples created using CM and MWM (n = 14 each) were compared. Because PVA-C is a multifunctional phantom material (e.g., in magnetic resonance imaging (MRI)), its MRI properties (T1/T2 relaxation times) and elasticity were determined. Results: T1 relaxation times did not significantly differ between the two methods (p = 0.3577), whereas T2 and elasticity for the MWM were significantly higher than those for the CM (p < 0.001). The MWM reduced the production time by 11% and decreased active user involvement by 93%. Conclusions: The MWM offers a promising, easily implementable, and time-efficient method for manufacturing PVA-C-based phantoms. Nevertheless, manufacturing-related microstructural properties and sample molding require further study. 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

The standard of care for early-stage NSCLC has historically been surgical resection. Given the association of lung cancer with smoking, a large number of early-stage patients also have active smoking-related medical comorbidities such as COPD precluding surgery. The current approach for treating such inoperable patients is frequently considered to be stereotactic body radiation therapy (SBRT). SBRT (also known as stereotactic ablative radiation therapy or SABR) is a curative modality that precisely delivers very high dose radiation in few (typically <5) sessions. That said, because of their minimal invasiveness and repeatable nature, image-guided thermal ablation therapies such as radiofrequency ablation (RFA), microwave ablation (MWA), and cryoablation (CA) have also been used to treat early-stage lung tumors. For those patients deemed to have “high operative risk” (i.e., those who cannot tolerate lobectomy, but are candidates for sublobar resection), the appropriateness of potential alternatives [e.g., SBRT; ablation] to surgery is an active area of investigation. In the absence of completed randomized phase III trials, the approach to comparing outcomes between surgery, SBRT, or ablative therapies by their efficacy or equivalence is complex. An overview of the role of SBRT and other non-surgical modalities in the management of early-stage lung cancer is the subject of the present review. Full article

The search for new non-invasive methods of investigating the functioning of human internal organs is an urgent task. One of these methods for assessing the functioning of the human respiratory system is electromagnetic sensing, which is based on a significant difference in the dielectric permittivity of muscle tissue and air. During breathing, when the lungs are filled with air, the dielectric permittivity of the lungs decreases, which leads to a change in the level of the electromagnetic signal passing through the body. The results of experiments on recording changes in the level of electromagnetic radiation passing through the human body performed on an experimental device consisting of eight transmitting and receiving antennas located on opposite sides of the chest have been presented in the article. The possibility of visualizing the measured “pulmonograms” in the form of dynamic two-dimensional images showing the process of filling various parts of the lungs with air has been demonstrated. Full article

In the field of medical imaging, microwave tomography (MWT) is based on the scattering and absorption characteristics of different tissues to microwaves and can reconstruct the electromagnetic property distribution of biological tissues non-invasively and without ionizing radiation. However, due to the inherently nonlinear and ill-posed characteristics of MWT calculations, actual imaging is prone to overfitting or artifacts. To address this, this paper proposes a two-step iterative imaging approach for rapid medical microwave tomography. This method establishes corresponding objective functions for microwave imaging across multiple frequencies and conducts iterative calculations on images at varying resolutions. This effectively enhances image clarity and accuracy while alleviating the issue of prolonged computational time associated with imaging complex structures at high resolution due to insufficient prior information during iterative processes. In the electromagnetic simulation section, we simulated a three-layer brain model and conducted imaging experiments. The results demonstrate that the algorithm significantly enhances imaging resolution, accurately pinpointing cerebral hemorrhages at different locations using an eight-antenna array and successfully reconstructs tomography images with a hemorrhage area radius of 1 cm. Lastly, experiments were conducted using a medical microwave tomography platform and four simplified human brain models, achieving millimeter-level accuracy in MWT. Full article

Synthetic microwave focusing methods have been widely adopted in qualitative medical imaging to detect and localize anomalies based on their electromagnetic scattering signatures. This paper discusses the principles, challenges, and limitations of synthetic microwave-focusing techniques in medical applications. It is shown that the various focusing techniques, including time reversal, confocal imaging, and delay-and-sum, are all based on the scalar solution of the electromagnetic scattering problem, assuming the imaged object, i.e., the tissue or object, is linear, reciprocal, and time-invariant. They all aim to generate a qualitative image, revealing any strong scatterer within the imaged domain. The differences among these techniques lie only in the assumptions made to derive the solution and create an image of the relevant tissue or object. To get a fast solution using limited computational resources, those methods assume the tissue is homogeneous and non-dispersive, and thus, a simplified far-field Green’s function is used. Some focusing methods compensate for dispersive effects and attenuation in lossy tissues. Other approaches replace the simplified Green’s function with more representative functions. While these focusing techniques offer benefits like speed and low computational requirements, they face significant ongoing challenges in real-life applications due to their oversimplified linear solutions to the complex problem of non-linear medical microwave imaging. This paper discusses these challenges and potential solutions. Full article

Near-infrared (NIR) persistent luminescence (PersL) materials have attracted extensive attention due to their great promise in medical diagnostics, bio-imaging, night vision surveillance, multi-level anticounterfeiting, and information encryption. To achieve NIR PersL (micro/nano-) materials with the desired properties, a variety of synthesis methods have been employed, including solid-phase reaction and liquid-phase synthesis. Different synthesis methods have different but important effects on the micro/nano-structure, luminescence, and PersL properties of the materials. Moreover, the influence of various synthesis methods on the properties of NIR PersL materials determines the selection of preparation approaches for other new material systems. Taking the representative NIR PersL ZnGa₂O₄:Cr³⁺ material as an example, four synthesis procedures are applied, namely, high-temperature solid-state reaction (SSR), high-temperature molten salt method (MSM), hydrothermal method (HM), and microwave-assisted solid-state (MASS) method. The structural and luminescent properties of samples made by SSR, MSM, HM, and MASS are compared. Notably, it is revealed that the MASS method can create additional trapping energy levels, which is of great significance for emerging applications. This work demonstrates the different effects of synthesis methods on PersL performance and provides a good guideline for the rapid and reasonable selection of preparation methods for diverse applications. 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

Quantum dots (QDs) are semiconductor materials, with a size range between 1–10 nm, showcasing unique size-dependent physical and chemical properties. Such properties have potentiated their use in areas like medical imaging and biosensing. Herein, we present an open-air approach for synthesis of QDs, reducing the need for controllable atmospheric conditions. Furthermore, we present a predictive mathematical model for maximum emission wavelength (λ_max) control. Through a straightforward microwave-based aqueous synthesis of TGA-CdTe QDs, we investigated the influence of time, temperature, and Te:Cd and TGA:Cd molar ratios on λ_max, using a chemometric experimental design approach. CdTe-QDs were characterized by UV-Vis and fluorescence spectroscopies. Additionally, Fourier-Transform Infrared spectroscopy, X-ray photoelectron spectroscopy, Transmission Electron Microscopy, and Energy Dispersive X-ray were conducted. Stable QDs with fluorescence ranging from green to red (527.6 nm to 629.2 nm) were obtained. A statistical analysis of the results revealed that time and temperature were the most significant factors influencing λ_max. After fine-tuning the variables, a mathematical model with 97.7% of prediction accurately forecasted experimental conditions for synthesizing TGA-CdTe QDs at predefined λ_max. Stability tests demonstrated that the QDs retained their optical characteristics for over a month at 4 °C, facilitating diverse applications. Full article