Application and analysis of MUSIC algorithm for anomaly detection in microwave imaging without a switching device

Microwave-induced thermoacoustic imaging, as an emerging biomedical imaging technique, combines the high contrast of microwave imaging with the high spatial resolution of ultrasound imaging. As an important branch of this technology, microwave-induced thermoacoustic microscopy retains these advantages while providing the capability to visualize finer tissue characteristics. However, conventional raster scanning mechanisms introduce interference in microwave field distribution due to mechanical motion, necessitating multiple signal averages to maintain signal-to-noise ratio. Additionally, the idle time during motor movement leads to prolonged single-scan duration, limiting its practical applications. To address these limitations, this paper proposes a rapid imaging system based on one-dimensional galvanometer scanning. The system employs a hybrid galvanometer-translation stage architecture and an optimized scanning strategy to minimize microwave field interference, reduce the number of signal averages, and decrease idle time, ultimately achieving more than a tenfold improvement in imaging speed. A specially designed timing control algorithm ensures precise synchronization of microwave excitation, galvanometer motion, and ultrasound detection, while a reconstruction algorithm adapted to the optimized scanning method effectively corrects distortions generated during the scanning process. System performance was evaluated through phantom and ex vivo tissue experiments. Resolution tests demonstrated hundred-micrometer resolution along all three axes (332 <i>μm</i> × 324 <i>μm</i> × 79 <i>μm</i>), while contrast and depth imaging experiments confirmed its capability to clearly distinguish targets with different conductivities, achieving an effective detection depth of at least 10 mm in tissue. Early tumor mimicking experiments further demonstrated the system's ability to identify lesion boundaries, preliminarily revealing its potential for rapid tumor margin assessment. This approach maintains the imaging quality of microwave-induced thermoacoustic microscopy while enhancing imaging efficiency and system stability, laying a crucial foundation for advancing the technology from laboratory research to clinical applications.

Well-designed and trained deep neural networks can solve inverse electromagnetic problems much faster than conventional solvers. However, they need a physics framework to ensure producing physically correct results. Since most physics-guided deep learning inverse solvers require substantial training with numerous epochs, each involving solving a forward problem, their accuracy and efficiency are largely defined by the utilized forward solver, which becomes a bottleneck for their practical training. Thus, a fast and accurate self-supervised deep learning forward solver is presented. The solver uses a physics-based framework that divides the domain into two regions: an interior region, which includes any scatterers, and an exterior region, which represents the background medium. A hybrid loss function, incorporating Maxwell's curl equation and integral equation with the well-defined scalar background's Green's function, is employed to guide the scattered field generated from the neural network, ensuring global and local accuracy. To verify the generality of the solver, it is trained on random objects and tested on realistic models, showing high global and local metrics accuracy. For example, more than 95% of testing cases using the proposed method achieve less than 0.15 root-mean-square error in the calculated scattered field and dielectric properties of the imaged domain compared to the ground truth. In contrast, two recent deep learning methods could only realize that level of accuracy for less than 50% of the tested cases. The reported method is 97% faster than conventional solvers, enabling the development of reliable deep-learning inverse solvers.

A novel 3D-printed microwave probe operating in the 25-45 GHz frequency range is designed and fabricated for early skin tumor detection using signal processing. Due to the highly lossy nature of the skin, electromagnetic wave penetration is difficult. To overcome this limitation, a multi-section probe design was developed to enhance wave penetration into the skin layer. This design effectively mitigates the effects of high-loss tangents in tissues and compensates for the small size of tumors, aiding in early detection. The probe's performance is validated through simulations and experimental measurements, showing excellent agreement. For imaging evaluation, a phantom model composed of pork skin, measuring 30 mm × 30 mm with a skin thickness of 4 mm, is utilized. A total of 215 scanning points were analyzed, and time-domain reflection waves were extracted, demonstrating the probe's ability to detect variations in tissue properties accurately. These signals were then processed using an entropy-based method. The reconstructed images across various scenarios highlight the effectiveness of the proposed probe in achieving high-resolution microwave imaging, indicating its strong potential for non-invasive, early-stage tumor detection.

Abstract In this paper, we present an ultra-fast technique for brain tumor detection in microwave brain imaging systems based on compressive sensing (CS). To achieve this, we designed an elliptical array-based microwave imaging system by simulating sixteen elements of modified bowtie antennas in the CST medium around a multi-layer head phantom. Additionally, we designed an appropriate matching medium to radiate in the desired band from 1 to 4 GHz. The algorithm section of our technique involves pre-processing steps for calibration, a processing step to create a two-dimensional image of the received signals, and a post-processing step for CS. In the processing section, we used a confocal image-reconstructing method based on delay and sum and delay, multiply, and sum beam-forming algorithms. Finally, we applied a new CS technique that includes an L1-norm convex optimization method to reconstruct low-dimension images from the original reconstructed images. We present simulated results to validate the effectiveness of our proposed method for precisely localizing the tumor target in a human full head phantom. The simulated results demonstrate that by using our proposed CS method, the image reconstruction processing time decreased to 63% and the compressed image size reduced to 25% of the original image.

Parkinson’s disease (PD) is characterized by pathological changes in the substantia nigra, which in its early stages may manifest as structural and functional asymmetries between the two hemispheres. Microwave imaging has recently emerged as a promising non-invasive tool to detect subtle dielectric variations. In the context of Parkinson’s disease, such contrasts are expected to arise from the underlying physiological alterations in brain tissue, although their magnitude has not yet been fully characterized. In this work, we investigate the feasibility of differential microwave imaging, where detection is based on permittivity contrasts, through a controlled phantom study. A simple two-dimensional head phantom was constructed using a 3D-printed cylindrical container filled with water, incorporating a Teflon tube to represent the substantia nigra. The tube was filled with hot water, whose gradual cooling emulated small dielectric changes. Since the dielectric properties of water vary linearly with temperature over 0.5–3 GHz, we first validated this dependence through both numerical analysis and experimental measurements. Four antennas were then employed in a differential imaging configuration, with image reconstruction performed via the multi-frequency bi-focusing algorithm. The results show that the system can successfully detect a dielectric contrast corresponding to a temperature variation as small as 0.4 °C, equivalent to approximately 0.17% in relative permittivity. While the exact dielectric changes associated with PD remain to be determined, these results demonstrate that the proposed approach is sensitive to very small contrasts, supporting the potential of differential microwave imaging as a candidate tool for future investigations into Parkinson’s disease detection.

Microwave imaging, used for breast tumor detection, requires low mutual coupling antennas in a circular array configuration, which can lead to signal degradation and distortion in the resulting images. This study proposes a Printed Monopole Antenna design with a Z-shaped slot Electromagnetic Band Gap (PMA-ZEBG) to minimize mutual coupling in a circular array configuration, resulting in a 13 dB decrease. The PMA-ZEBG is compact, measuring 35 × 40 × 1.524 mm (0.30λ0 × 0.34λ0 × 0.01λ0 at 2.61 GHz), and has a bandwidth of 3.2 GHz (2.61 GHz–5.81 GHz). Simulated Specific Absorption Rate (SAR) values at 20 mm between the breast and the antenna indicate that the antenna is within safe standards for microwave imaging. The S-parameters from the PMA-ZEBG can be used to identify and localize tumor presence by employing the Delay and Sum (DAS) algorithm within the Microwave Radar-based Imaging Toolbox (MERIT). The imaging results obtained using the PMA-ZEBG antenna display a more defined tumor image within the specified area and fewer discernible spots on the periphery.

We designed and optimized a miniaturized coplanar Vivaldi antenna specifically for microwave imaging in cerebral hemorrhage detection. The antenna measures 80 mm × 80 mm × 1 mm and features an arc-shaped radiating arm, a 3 mm × 3 mm optimized pad layout, and an improved metallized via structure with nine vias, each 0.5 mm in diameter. These enhancements significantly improve the antenna’s directivity, impedance matching, and signal penetration capability. Experimental results demonstrate that the antenna operates stably within the ultra-wide frequency band of 1.6–8 GHz, achieving a reflection coefficient as low as -45 dB at 4 GHz, a voltage standing wave ratio (VSWR) consistently below 1.5, and a peak gain of 9.5 dB at 6.5 GHz. These characteristics fully meet the sensitivity and penetration depth requirements for medical imaging. In addition to presenting a novel antenna design, this study validates its effectiveness under realistic biological conditions. Comparative analysis between 18- and 36-element antenna arrays demonstrates that the 36-element configuration improves image resolution and signal uniformity, while the 18-element array offers faster acquisition and better suitability for emergency or point-of-care screening scenarios. Additionally, in realistic skull model experiments, we employed rotating antenna technology (with a 20° step size) and multi-angle signal acquisition, further optimizing imaging uniformity and detection accuracy in hemorrhagic regions. By integrating real-time differential imaging technology and beamforming algorithms such as Delayed Sum (DAS) and Delayed Multiplication and Sum (DMAS), the experimental results indicate substantial progress in the identification of brain hemorrhage areas. This research provides critical technical support for the development of portable and non-invasive cerebral hemorrhage detection systems. Overall, by integrating miniaturization, performance optimization, and targeted enhancements, this study provides a robust technical basis for the development of early stroke detection systems.

Abstract. The Ice Cloud Imager (ICI) will be hosted on the second generation of the EUMETSAT Polar System (EPS-SG). By measuring at microwave and sub-millimetre wavelengths, ICI will provide unparalleled global observations of ice clouds. EUMETSAT's official ICI level-2 product will offer retrievals of ice mass column properties. This study explores whether the capabilities of ICI can be extended to retrieve vertical profiles of ice mass. Using a retrieval database of ICI simulations, we trained a quantile regression neural network (QRNN) to retrieve ice water content (IWC) and profiles of the mean mass diameter of ice hydrometeors. Our retrieval setup is fast and simpler to implement than previous ICI profile retrieval approaches, and the study is more comprehensive in scope than earlier efforts. Comparisons between our retrieved and database profiles demonstrate that ICI observations are sensitive to IWC within the range of 10−2 and 1 g m−3, and performance is strongest between altitudes of 3 and 14 km. Our results also show that ICI observations are sensitive to mean mass diameter values up to 600 µm, although successful retrievals of up to 800 µm are observed. To assess the vertical resolution of the retrievals, we computed approximations of averaging kernels on the model predictions. We estimate the resolution of IWC profiles to be ∼ 2.5 km. Retrievals of mean mass diameter achieve an estimated resolution of 2.5 km at an altitude of 5 km, with reduced resolution at higher altitudes. No operational product currently provides ice mass vertical information derived from passive microwave observations. However, this study demonstrates that ICI can fill this gap thanks to the presence of both microwave and sub-millimetre channels, with the sub-millimetre wavelengths providing particularly high sensitivity to cloud ice. Furthermore, the relatively broad swath of ICI observations lead to a higher spatial and temporal coverage than radar and lidar products can achieve. The global and long-term dataset that ICI will offer could therefore act as a valuable complement to CloudSat or EarthCARE-based retrievals. Future efforts could explore the inclusion of the Microwave Imager (MWI) observations to improve retrievals at low altitudes – a natural next step given that MWI is to be launched on the same platform as ICI.

Abstract This study presents the design and analysis of a dual linear polarized sinuous antenna (DLPSA) optimized for ultra-wideband applications, such as remote sensing of longitudinal metallic targets and microwave imaging systems. The capability of the sinuous antenna to generate dual linearly polarized radiation patterns makes it a strong candidate for these applications. A key design challenge lies in developing a practical feeding network that requires modifications to the antenna feed region. The proposed DLPSA antenna achieves unidirectional radiation patterns in the 2–5 GHz frequency band. A prototype was fabricated, with measured results closely aligned with the simulations. The antenna demonstrates enhanced return loss, gain, and radiation pattern performance compared to existing designs. Additionally, the dual linear polarization capability was verified through co- and cross-polarization measurements conducted in an anechoic chamber.

Microwave Imaging With a Reduced Number of Transmission Channels in a Semi-Circular Antenna Array

Abstract The Defense Meteorological Satellite Program (DMSP) Block 5D series of satellite sensors have been a cornerstone of the global effort to monitor tropical cyclone (TC) location, structure, and intensity since 1976. These satellite-based sensors uniquely complement land and ocean surface reports, radiosondes, aircraft, and geostationary visible (Vis) and infrared (IR) imagery. DMSP sensors have incorporated new technology with the ability to penetrate through clouds to see the critical TC structures needed to make accurate nowcasts and forecasts. The DMSP uses a combination of Vis/IR optical sensors and state-of-the-art passive microwave (PMW) imagers and sounders. Specific spectral bands are selected to observe desired phenomena (rain, temperature and moisture profiles, total precipitable water, cloud liquid water, etc.). These bands were first demonstrated by the National Aeronautics and Space Administration (NASA) satellites and then updated and expanded by DMSP operationally. The DMSP sounders were the first to exploit the water vapor sounding channels (183 GHz), now global weather constellation standards. These attributes have directly benefited the worldwide TC community by mitigating the inherent Vis/IR cloud limitations. Researchers have devised objective products that add forecaster value. These automated products provide near-real-time guidance supplementing human analyst’s outputs via the state-of-the-art TC structure and intensity characteristics. Follow-on civilian sensors continue these efforts and engineering strides now enable smaller and more cost-effective options that permit constellations that provide otherwise unattainable temporal sampling. This paper will focus on the DMSP Block 5D beginning in 1976 and continuing through satellites and sensors still operational today. Significance Statement Tropical cyclones (TCs) impact a large percentage of the global population due to our desire to live near the coastline and in tropical climates, but few people are aware of the satellite sensors used to monitor their at-sea birth and life cycle before landfall. This summary highlights the history of TC remote sensing and focuses on the Defense Meteorological Satellite Program (DMSP). The DMSP Block 5D visible (Vis) and infrared (IR) and especially the passive microwave imager and sounders have pioneered new sensor capabilities that have greatly advanced our ability to accurately monitor TCs in near–real time (NRT) globally as well as assist the research community. These sensor attributes have been incorporated into follow-on sensors that are operational today with wide-ranging benefits.

EconoScan: Affordable Microwave Imaging for Nondestructive Strength Testing of Bituminous Materials

A quad-static microwave imaging system designed for medical use, with an emphasis on early-stage breast tumour detection, is presented in this paper. A compact two-element MIMO Vivaldi antenna, designed for practical microwave imaging, is used in the proposed system. The antenna is appropriate for radar-based diagnostic systems due to its end-fire radiation characteristic. With a maximum gain of 16.98 dBi at 2.75 GHz, it operates across a broad frequency range from 2 GHz to 14.8 GHz. Additionally, it satisfies the FCC (USA) limit for localized SAR, which is 1.6 W/kg averaged over 1 gram of tissue. With overall dimensions of 49 × 85 × 0.8 mm³, the antenna is designed and simulated on an affordable FR4 substrate that offers both structural compactness and a wide bandwidth. For validation, HFSS was used to create and simulate a breast phantom model that replicated the dielectric characteristics of human tissue. When the transmission coefficient (S21 and S41 parameters) is used to analyse the system, it is shown that tumours as small as 4 mm in diameter can be detected. The findings support the suggested antenna and imaging system’s ability to accurately detect small breast tumours, potentially leading to earlier diagnosis and better treatment outcomes.

Breast cancer remains one of the leading causes of death among women worldwide. One major challenge in early and accurate detection is breast density. High breast density not only obscures tumors on current imaging modalities, making them harder to identify, but also significantly increases the likelihood of diagnostic errors, both by medical professionals and automated detection systems. As a result, accurately classifying the breast density is crucial, and can lead to better, more tailored screening approaches and reduce the chances of error. This is especially critical for younger women, who are usually excluded from national screenings due to concerns such as radiation exposure. Microwave imaging offers a promising solution to this problem. Unlike traditional imaging methods, it uses safe, non-ionizing radiation, making it suitable for women of all ages. Beyond its safety, microwave imaging has the potential not only to detect breast cancer, but also to classify breasts into high or low density. This dual capability allows for more personalized and accurate cancer detection based on breast density, improving outcomes and reducing diagnostic uncertainty. Our microwave imaging prototype called MammoWave works by scanning the breast using a wide range of low-power electromagnetic signals captured from multiple positions around the breast. This approach provides a rich set of data that helps create an internal map of breast tissue without exposing patients to harmful radiation. This technique makes it possible to extract frequency-based characteristics from both the spatial and spectral domains, taking advantage of not just the signal's magnitude but also its phase information. These rich features can offer deeper insights into tissue composition and improve the accuracy of breast density classification. Our analysis shows that by fusing features from both the magnitude and phase of the signals-and focusing on approximately the first 40 components of the fast Fourier transform (FFT)-it's possible to achieve an accuracy of around 70% in classifying breast density using a support vector machine (SVM) with a radial basis function (RBF) kernel. Furthermore, instead of using the full frequency range (1 to 9 GHz), selecting specific sub-bands (1, 3, 4, 5, and 6 GHz) can improve the accuracy to approximately 73%. Importantly, the results also reveal that when breast density is correctly identified and taken into account, the performance of machine learning models in detecting breast cancer improves significantly boosting specificity and sensitivity by around 10% and 5%, respectively for low-density breasts, and by 15% and 10% respectively for high-density breasts.

Synthetic aperture radar (SAR) is an active microwave imaging system equipped with penetration capability, enabling all-time and all-weather Earth observation, and demonstrates significant advantages in large-scale surface water-body detection. Although SAR images can provide relatively clear water-body details, they are susceptible to interference from external factors such as complex terrain and background noise, resulting in fragmented detection outcomes and poor connectivity. Therefore, a Connectivity Refinement Network (ConRNet) is proposed in this study to address the issue of fragmented water-body regions in water-body detection results, combining HISEA-1 and Chaohu-1 SAR data. ConRNet is equipped with attention mechanisms and a connectivity prediction module, combined with dual supervision from segmentation and connectivity labels. Unlike conventional attention modules that only emphasize pixel-wise saliency, the proposed Dual Self-Attention Module (DSAM) jointly captures spatial and channel dependencies. Meanwhile, the Connectivity Prediction Module (CPM) reformulates water-body connectivity as a regression problem to directly optimize structural coherence without relying on post-processing. Leveraging dual supervision from segmentation and connectivity labels, ConRNet achieves simultaneous improvements in topological consistency and pixel-level accuracy. The performance of the proposed ConRNet is evaluated by con-ducting comparative experiments with five deep learning models: FCN, U-Net, DeepLabv3+, HRNet, and MAGNet. The experimental results demonstrate that the ConRNet achieves the highest accuracy in water-body detection, with an intersection over union (IoU) of 88.59% and an F1-score of 93.87%.

Abstract Dataset contamination is a typical challenge when dealing with large quantities of multi-dimensional data and is expected to impact any data-specific applications. While several error diagnostic methods exist to assess the datasets for biases, some errors might go unnoticed by developers and users. In this paper, we document our experience detecting errors in the dataset that were missed by other validation methods but identified by analyzing machine-learning model predictions trained on contaminated datasets. Specifically, we used simulated observations of a future space-borne passive microwave sensor, the Second-Generation EUMETSAT Polar System Microwave Imager, to develop a convolutional neural network-based model to predict Total Column Water Vapor. An oversight in time-matching during the data creation led to contaminating a small portion of the dataset. Although the simulated data did not show any signs of the time mismatch and the overall statistical scores of predictions were good, artifacts identified in the model output led to an in-depth analysis of individual input orbits revealing high root mean square error values for the contaminated samples, highlighting the erroneous points in the simulated brightness temperature data. This work also evaluates the impact of data contamination on data-driven frameworks across different proportions of erroneous samples while highlighting the importance of data curation.

MRI is widely used for breast cancer screening for women at a high risk including those with dense breasts. Gadolinium-based contrast agents provide excellent sensitivity for tumor detection; however, some reports suggest that significant health risks may be associated with gadolinium use. Alternative techniques to improve breast MRI specificity that do not reply on gadolinium injection are needed. Microwave dielectric properties provide excellent endogenous contrast between malignant and normal breast tissue. While standalone microwave imaging (MI) methods are available, they typically suffer from poor spatial resolution. Integrating concurrent MI with MRI would allow high resolution from MRI to be combined with high specificity from MI. We have developed a MI system that operates inside the bore of an MR scanner without disruption to image acquisition from either modality. Results are presented from the first set of experiments in anthropomorphic breast phantoms conducted with the integrated imaging configuration. MRI and microwave data were acquired simultaneously with a system which met size, materials, and operational constraints imposed by the MRI scanner bore and associated electromagnetic environment. Functionality included coaxial antenna feedlines mounted to a plate underneath an imaging tank with integrated breast imaging coils, which was moved vertically to provide multiples planar views of the imaging region of interest. The MI technique imported MR spatial information directly into a soft prior-based algorithm for 3D image reconstruction. The method did not utilize any prior information other than the MRI structural data. MI recovered spherical fibroglandular and tumor equivalent tissue inclusions within a predominant adipose tissue. Data were presented at three frequencies-900, 1100, and 1300MHz, respectively-with comparable results. Microwave property distributions were relatively homogeneous across each tissue zone with steep gradients at their associated interfaces. Permittivity images recovered properties well for all three tissue types, while conductivity images maintained fidelity for adipose and tumor tissues but became inaccurate in the fibroglandular tissue equivalent zones. To the best of our knowledge, these microwave images are the first to be reconstructed from 3D data acquired inside a MRI scanner bore where MRI data were incorporated as spatial priors within the resultant microwave images. MR-MI system logistical challenges were overcome sufficiently to recover accurate images of phantoms making the multi-modality approach ready for actual patient examinations in the future.

Breast cancer is the most common malignancy among women, and early detection and treatment are critical for improving clinical outcomes. X-ray mammography remains the standard screening modality; however, it has several limitations, including radiation exposure, patient discomfort, and reduced sensitivity in women with dense breast tissue. Microwave imaging, a non-ionizing technique, has emerged as a promising alternative. We have developed a device that reconstructs breast tissue structures by solving the inverse scattering problem and is currently undergoing clinical trials. This system generates three-dimensional tomographic images without the use of contrast agents and without causing pain during examination. To date, 24 breast cancer patients have been imaged, with an accuracy of 86% for tumors ≥ 1 cm in diameter, and 58% when tumors < 1 cm are included. In this article, we present a detailed protocol for device preparation, clinical imaging, and data processing, along with representative imaging results from selected patients.

Abstract Ultrawideband frequency-modulated continuous-wave (UWB-FMCW) radar is a solution for through-the-wall (TTW) imaging and detection technology. TTW radar is an attractive UWB microwave imaging system that comprises radars capable of localizing and monitoring humans in hazardous locations or under unfavorable conditions. This study presents a novel approach for simulating the RF front-end transceiver of a UWB-FMCW radar system. The proposed methodology is implemented on a 9 GHz FMCW radar, utilizing a single antenna for both transmission and reception. The simulation model proposed in the paper is shown to be a valuable tool for designing UWB-FMCW through-wall imaging radar systems.