Search

The distorted Born iterative method (DBIM) is a widely used quantitative reconstruction algorithm to solve the inverse scattering problems of microwave imaging. The major mathematical challenges in solving such problems are non-uniqueness, non-linearity, and ill-posedness. Due to these issues, the optimization algorithm converges to a local minimum. This drawback can be overcome by selecting the correct initial guess solution, which helps to escape local minima and thus guides the inversion algorithm to a satisfactory result. This study uses a back-propagation algorithm to calculate the initial estimate, which significantly accelerates the rate of convergence and improves the accuracy of the standard DBIM approach. The results of this method are compared with zero initialization and Born-approximated initialization. For comparison, weak as well as strong scattering profiles of synthetic and experimental dataset are considered. The results suggest that the proposed method provides a significant improvement in terms of computing cost and efficiency. Furthermore, the proposed technique has the potential to successfully push the limits of reconstructible contrast. [ABSTRACT FROM AUTHOR]

A small ultra-wideband (UWB) patch antenna for microwave breast imaging (MWI) applications is shown off in this paper. However, to improve the antenna performance relating to the bandwidth (BW), the radiating element of the suggested antenna is modified by adding slits in the patch as well as the ground plane. The proposed prototype has a relatively small size of 20 × 19 × 1.6 mm3 and it accomplishes a return loss below −10 dB (S 11< −10 dB) at an overall BW of 7 GHz (4–11 GHz) with more than 3 dBi realized gain. The antenna is designed and simulated by using a finite integration technique-based simulator. In this way, the characteristics of the fabricated antenna are measured to examine the antenna performance. Indeed, the fidelity factor of face-to-face (FtF) and side-by-side (SbS) scenarios are also noticed for the same frequency range. In the final analysis, a simulation model of the antennas, that operate as a transceiver, and a breast phantom model with tumor sample is proposed for detecting cancerous tumor cells within the breast. Hence, the proposed approach is suitable for UWB based MWI applications in tumor cell detection. [ABSTRACT FROM AUTHOR]

Automated brain tumor segmentation from reconstructed microwave (RMW) brain images and image classification is essential for the investigation and monitoring of the progression of brain disease. The manual detection, classification, and segmentation of tumors are extremely time-consuming but crucial tasks due to the tumor's pattern. In this paper, we propose a new lightweight segmentation model called MicrowaveSegNet (MSegNet), which segments the brain tumor, and a new classifier called the BrainImageNet (BINet) model to classify the RMW images. Initially, three hundred (300) RMW brain image samples were obtained from our sensors-based microwave brain imaging (SMBI) system to create an original dataset. Then, image preprocessing and augmentation techniques were applied to make 6000 training images per fold for a 5-fold cross-validation. Later, the MSegNet and BINet were compared to state-of-the-art segmentation and classification models to verify their performance. The MSegNet has achieved an Intersection-over-Union (IoU) and Dice score of 86.92% and 93.10%, respectively, for tumor segmentation. The BINet has achieved an accuracy, precision, recall, F1-score, and specificity of 89.33%, 88.74%, 88.67%, 88.61%, and 94.33%, respectively, for three-class classification using raw RMW images, whereas it achieved 98.33%, 98.35%, 98.33%, 98.33%, and 99.17%, respectively, for segmented RMW images. Therefore, the proposed cascaded model can be used in the SMBI system. [ABSTRACT FROM AUTHOR]

A super wideband coplanar waveguide-fed antenna is proposed for Microwave Imaging (MI) applications. The antenna comprising a slotted patch and a defected ground structure (DGS) loaded with a stub has been prototyped on a 1.6mm thick glass-reinforced FR4 material with an Er of 4.4. The antenna has a size of 0:12λ0 x 0:12λ0 at the lowest operating frequency of 1.21 GHz. The slotted patch coupled well with the stub-loaded DGS in the ground plane and led the proposed antenna to obtain a range of operational bandwidth from 1.21 GHz to 24.66 GHz. Initially, with a rectangular patch, a super wideband antenna with five notch bands is achieved. To eliminate four notch bands and realize the super wideband two rectangular slots are etched in the patch. The last notch band is eliminated by loading the ground with a stub. To make the proposed antenna a compact space-saving one, the patch is fitted in a hexagonal slot etched in the ground. The experimental result reveals a super wideband performance of 181% (1.21 GHz-24.66 GHz) with a consistent radiation pattern and peak gain of 9.4 dB in a compact area of 30mm². [ABSTRACT FROM AUTHOR]

This paper presents two flexible monopole antennas implemented on cotton substrate as sensors designed for radar-based microwave imaging and particularly breast imaging systems. The flexible antennas are basic building block of a radar-based microwave imaging system that could be integrated in clothes. Thus, these low-cost textile sensors could be used by women for self-monitoring and early breast cancer detection at an affordable price. The ultra-wideband fully textile sensors are designed as rectangular and circular monopole antennas. Both sensors have an impedance bandwidth ≤ − 10 dB from 2.2 to 8 GHz with an overall footprint of 50 × 50 mm2. Simulations for antenna in proximity to breast model with and without tumor as well as bending capacity are performed. The simulations are complemented with fabrication of a breast phantom and a tumor sample with parameters that mimic these of the human breast's healthy and malignant tissue. Measurements are compared to simulation results as well as the performance of antenna before and after subjected to washing. Finally, the specific absorption rate is also calculated and measured to ensure safety for on-body deployment. The proposed work demonstrates the potential to develop wearable microwave imaging system using fully textile antennas. [ABSTRACT FROM AUTHOR]

Microwaves are non-ionizing electromagnetic radiation with waves of electrical and magnetic energy transmitted at different frequencies. They are widely used in various industries, including the food industry, telecommunications, weather forecasting, and in the field of medicine. Microwave applications in medicine are relatively a new field of growing interest, with a significant trend in healthcare research and development. The first application of microwaves in medicine dates to the 1980s in the treatment of cancer via ablation therapy; since then, their applications have been expanded. Significant advances have been made in reconstructing microwave data for imaging and sensing applications in the field of healthcare. Artificial intelligence (AI)-enabled microwave systems can be developed to augment healthcare, including clinical decision making, guiding treatment, and increasing resource-efficient facilities. An overview of recent developments in several areas of microwave applications in medicine, namely microwave imaging, dielectric spectroscopy for tissue classification, molecular diagnostics, telemetry, biohazard waste management, diagnostic pathology, biomedical sensor design, drug delivery, ablation treatment, and radiometry, are summarized. In this contribution, we outline the current literature regarding microwave applications and trends across the medical industry and how it sets a platform for creating AI-based microwave solutions for future advancements from both clinical and technical aspects to enhance patient care. [ABSTRACT FROM AUTHOR]

In this paper, a wideband antenna is proposed for ultra-wideband microwave imaging applications. The antenna is comprised of a tapered slot ground, a rectangular slotted patch and four star-shaped parasitic components. The added slotted patch is shown to be effective in improving the bandwidth and gain. The proposed antenna system provides a realized gain of 6 dBi, an efficiency of around 80% on the radiation bandwidth, and a wide impedance bandwidth (S11 < −10 dB) of 6.3 GHz (from 3.8 to 10.1 GHz). This supports a true wideband operation. Furthermore, the fidelity factor for face-to-face (FtF) direction is 91.6%, and for side by side (SbS) is 91.2%. This proves the excellent directionality and less signal distortion of the designed antenna. These high figures establish the potential use of the proposed antenna for imaging. A heterogeneous breast phantom with dielectric characteristics identical to actual breast tissue with the presence of tumors was constructed for experimental validation. An antenna array of the proposed antenna element was situated over an artificial breast to collect reflected and transmitted waves for tumor characterization. Finally, an imaging algorithm was used to process the retrieved data to recreate the image in order to detect the undesirable tumor object inside the breast phantom. [ABSTRACT FROM AUTHOR]

Medical microwave imaging (MWI) research has gained more attention in recent decades, particularly with regard to the early detection of brain tumors. It offers numerous benefits over existing imaging technologies, including magnetic resonance imaging (MRI) and computed tomography (CT) scans. Antennas represent a key element of MWI systems, so optimizing their performance is critical. A new, simple patch antenna for MWI is presented in this article. The radiating patch has a circular slot and a tiny triangular cut at the top right corner, and it is designed and manufactured with a low-cost FR4-epoxy substrate with a loss tangent of 0.02 and a dielectric constant of 4.4. The overall dimensions are 0.28λ × 0.22λ × 0.005λ (where λ is the wavelength of a lower frequency of 1.22 GHz). A total impedance bandwidth (<-10 dB) of 2.23 GHz (1.22–3.45 GHz) is achieved with a very decent gain of above 5 dBi (measured) and above 85 % efficiency over the functional range. Time-domain assessment of the antenna confirms minimum signal distortion and shows a better response in terms of group delay (GD) and fidelity factor (FF, 92 %). Measurements confirm the results from simulations (CST and HFSS) with a reasonable degree of agreement. Finally, the 3D-electromagnetic CST simulator was utilized to analyze the behavior of a nine-antenna array placed around a 3D-realistic Hugo-head model; it yielded satisfactory results and proved the practical utility of the proposed antenna in the intended MWI application. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common and the fifth deadliest cancer worldwide. In more advanced stages of cancer, cancer cells metastasize through lymphatic and blood vessels. Currently there is no satisfactory neoadjuvant (i.e., preoperative) diagnosis to assess whether cancer has spread to neighboring Axillary Lymph Nodes (ALN). This paper addresses the use of radar Microwave Imaging (MWI) to detect and determine whether ALNs have been metastasized, presenting an analysis of the performance of different artifact removal and beamformer algorithms in distinct anatomical scenarios. We assess distinct axillary region models and the effect of varying the shape of the skin, muscle and subcutaneous adipose tissue layers on single ALN detection. We also study multiple ALN detection and contrast between healthy and metastasized ALNs. We propose a new beamformer algorithm denominated Channel-Ranked Delay-Multiply-And-Sum (CR-DMAS), which allows the successful detection of ALNs in order to achieve better Signal-to-Clutter Ratio, e.g., with the muscle layer up to 3.07 dB, a Signal-to-Mean Ratio of up to 20.78 dB and a Location Error of 1.58 mm. In multiple target detection, CR-DMAS outperformed other well established beamformers used in the context of breast MWI. Overall, this work provides new insights into the performance of algorithms in axillary MWI. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the leading causes of death for women worldwide. It is a multilevel disease characterised by aberrant breast tissue development. As a patient's stage progresses, his or her odds of being treated and of surviving decreases. As a result, it is critical to identify and treat breast cancer early. Microwave imaging for breast cancer detection is an intriguing technology for detecting tumours, and it has a lot of benefits over other breast cancer detection methods that are already in use. UWB technology is mainly widely prescribed for usage in such an applications because to its special qualities such as short-range, non-ionizing, and broad bandwidth have used a UWB antenna sensor network to image and detect breast cancer. Non-ionizing radio waves are one of them. Its benefits are adaptability, a low-cost technology, and human body safety. For receiving and sending microwaves, microwave vision relies on a microstrip patch antenna. As the term suggests, a micro strip patch antenna is a small, light antenna. Microstrip patch antennas are classified as flexibility or non-flexible dependent on the substrate surface. They designed a simple recursive antenna (peano patch antenna) for the Ultra-Wideband frequency (6.744 GHz) (-42.657 dB). The antenna options for breast imaging on a transmitter is an antenna array composed of 18 antennas. The antenna is designed in a circular shape to allow it to face exactly at the image of the breast for greater disease diagnosis. [ABSTRACT FROM AUTHOR]

Corn is an important food crop worldwide, and its yield is directly related to Chinese food security. Accurate remote sensing extraction of corn can realize the rational application of land resources, which is of great significance to the sustainable development of modern agriculture. In the field of large-scale crop remote sensing classification, single-period optical remote sensing images often cannot achieve high-precision classification. To improve classification accuracy, multiple time series image combinations have gradually been adopted. However, due to the influence of cloudy and rainy weather, it is often difficult to obtain complete time series optical images. Synthetic aperture radar (SAR) data are imaged by microwaves, which have strong penetrating power and are not affected by clouds. A critical way to solve this problem is to use SAR images to compensate for the lack of optical images and obtain a complete time series image in the corn-growing season. However, SAR images have limited wavelengths and cannot provide important wavelengths, such as visible light bands and near-infrared information. To solve this problem, this study took Zhaodong City, a vital corn-planting base in China, as the research area; took GF-6/GF-3 and Sentinel-1/Sentinel-2 as remote sensing data sources; designed12 classification scenarios; analyzed the best classification period and the best time series combination of corn classification; studied the influence of SAR images on the classification results of time series images; and compared the classification differences between GF-6/GF-3 and Sentinel-1/Sentinel-2. The results show that the classification accuracy of time series combinations is much higher than that of single-period images. The polarization characteristics of SAR images can improve the classification accuracy with time series images. The classification accuracy of GF series images from China is obviously higher than that of Sentinel series images. The research performed in this paper can provide a reference for agricultural classification by using remote sensing data. [ABSTRACT FROM AUTHOR]

Computerized brain tumor classification from the reconstructed microwave brain (RMB) images is important for the examination and observation of the development of brain disease. In this paper, an eight-layered lightweight classifier model called microwave brain image network (MBINet) using a self-organized operational neural network (Self-ONN) is proposed to classify the reconstructed microwave brain (RMB) images into six classes. Initially, an experimental antenna sensor-based microwave brain imaging (SMBI) system was implemented, and RMB images were collected to create an image dataset. It consists of a total of 1320 images: 300 images for the non-tumor, 215 images for each single malignant and benign tumor, 200 images for each double benign tumor and double malignant tumor, and 190 images for the single benign and single malignant tumor classes. Then, image resizing and normalization techniques were used for image preprocessing. Thereafter, augmentation techniques were applied to the dataset to make 13,200 training images per fold for 5-fold cross-validation. The MBINet model was trained and achieved accuracy, precision, recall, F1-score, and specificity of 96.97%, 96.93%, 96.85%, 96.83%, and 97.95%, respectively, for six-class classification using original RMB images. The MBINet model was compared with four Self-ONNs, two vanilla CNNs, ResNet50, ResNet101, and DenseNet201 pre-trained models, and showed better classification outcomes (almost 98%). Therefore, the MBINet model can be used for reliably classifying the tumor(s) using RMB images in the SMBI system. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of the Bayesian inversion method for the characterization and estimation of different dielectric material properties. The scattering parameters of single and multi-layer materials are measured using a free-space experimental setup using a standard gain horn antenna and a Vector Network Analyzer (VNA) at Ka-band (26–40 GHz). The relative permittivity, material thickness, and material positioning error are defined as model parameters and estimated using the observed (measured) data. The FR4 Epoxy, Rogers RT/Duriod 5880, and Rogers AD600 with different relative permittivities and thicknesses are used in the measurement setup. The results displayed good agreement between model parameters and estimated properties of the presented materials, while the corresponding eigenvectors provided a level of confidence in model parameter values. The results were compared with different reported techniques to showcase the possible use of the presented method in microwave imaging, non-destructive testing, and similar applications. [ABSTRACT FROM AUTHOR]

Microwave imaging has been recently proposed as alternative technology for in-line inspection of packaged products in the food industry, thanks to its non-invasiveness and the low-cost of the equipment. In this framework, simple and effective detection/imaging strategies, able to reveal the presence of foreign bodies that may have contaminated the product during the packaging stage, are needed to allow real-time and reliable detection, thus avoiding delays along the production line and limiting occurrence of false detections (either negative or positive). In this work, a novel detection/imaging approach meeting these requirements is presented. The approach performs the detection/imaging of the contaminant by exploiting the symmetries usually characterizing the food items. Such symmetries are broken by the presence of foreign bodies, thereby determining a differential signal that can be processed to reveal their presence. In so doing, the approach does not require the prior measurement of a reference, defect-free, item. With respect to the quite common case of homogeneous food packaged in circular plastic/glass jars, numerical analyses are provided to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Microwave imaging is emerging as an alternative modality to conventional medical diagnostics technologies. However, its adoption is hindered by the intrinsic difficulties faced in the solution of the underlying inverse scattering problem, namely non-linearity and ill-posedness. In this paper, an innovative approach for a reliable and automated solution of the inverse scattering problem is presented, which combines a qualitative imaging technique and deep learning in a two-step framework. In the first step, the orthogonality sampling method is employed to process measurements of the scattered field into an image, which explicitly provides an estimate of the targets shapes and implicitly encodes information in their contrast values. In the second step, the images obtained in the previous step are fed into a neural network (U-Net), whose duty is retrieving the exact shape of the target and its contrast value. This task is cast as an image segmentation one, where each pixel is classified into a discrete set of permittivity values within a given range. The use of a reduced number of possible permittivities facilitates the training stage by limiting its scope. The approach was tested with synthetic data and validated with experimental data taken from the Fresnel database to allow a fair comparison with the literature. Finally, its potential for biomedical imaging is demonstrated with a numerical example related to microwave brain stroke diagnosis. [ABSTRACT FROM AUTHOR]

As an active microwave coherent imaging technology, synthetic aperture radar (SAR) images suffer from severe speckle noise and low-resolution problems due to the limitations of the imaging system, which cause difficulties in image interpretation and target detection. However, the existing SAR super-resolution (SR) methods usually reconstruct the images by a determined degradation model and hardly consider multiplicative speckle noise, meanwhile, most SR models are trained with synthetic datasets in which the low-resolution (LR) images are down-sampled from their high-resolution (HR) counterparts. These constraints cause a serious domain gap between the synthetic and real SAR images. To solve the above problems, this paper proposes an unsupervised blind SR method for SAR images by introducing SAR priors in a cycle-GAN framework. First, a learnable probabilistic degradation model combined with SAR noise priors was presented to satisfy various SAR images produced from different platforms. Then, a degradation model and a SR model in a unified cycle-GAN framework were trained simultaneously to learn the intrinsic relationship between HR–LR domains. The model was trained with real LR and HR SAR images instead of synthetic paired images to conquer the domain gap. Finally, experimental results on both synthetic and real SAR images demonstrated the high performance of the proposed method in terms of image quality and visual perception. Additionally, we found the proposed SR method demonstrates the tremendous potential for target detection tasks by reducing missed detection and false alarms significantly. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is an important active microwave imaging sensor. There is no doubt that applying DL to more SAR fields (such as terrain classification, SAR agriculture monitoring, SAR imaging algorithm updating, SAR forest applications, marine pollution, etc.) is of great significance for earth remote sensing. Conclusions Recently, as many SAR systems have been put into use, massive SAR data are available, providing important support for exploring how to apply DL to SAR fields. [Extracted from the article]

In this work, a resolution improvement strategy has been presented for microwave tomography imaging in the dense and heterogeneous breasts. While improving the spatial resolution by reducing the size of pixels, the resultant image will be affected by spatial oscillation, and it will also lack to differentiate the dielectric values of low contrast tissue regions which are the unavoidable problems in the microwave tomography dense breast imaging. These difficulties are addressed as discrete ill‐posed and ill‐condition problems. In this paper, Enriched Conjugate Gradient Least Square (ECGLS) regularization method has been proposed and it is associated with the Distorted Born Iterative Method (DBIM) to improve the quality of high‐resolution images of heterogeneous and dense type breasts. The Proposed‐ECGLS (PECGLS) method introduces the span of enriched subspace with external penalization value to resolve the discrete ill‐posed problem. The Gram‐Schmidt factorization (or QR factorization) based step length and the external penalization value control the spatial oscillations and resolve the ill‐condition problem with quick convergence. The performance of the Proposed‐ECGLS method has been tested on heterogeneous and dense breast phantoms for synthetically embedded malignant along with differences in permittivity value of +8% to +10% in the fibrogland tissues. The results from the simulation studies depict that the Proposed‐ECGLS has achieved up to 0.8276 of structural similarity in different level discretization error and it is better than the existing methods such as ECGLS and standard CGLS. This high‐resolution microwave tomography imaging method will be helpful in malignant detection and fibroadenoma diagnosis in dense type breasts. [ABSTRACT FROM AUTHOR]

The need for non-ionizing techniques for medical imaging applications has led to the use of microwave signals. Several systems have been introduced in recent years based on increasing the number of antennas and frequency bandwidth to obtain high resolution and good accuracy in locating objects. A novel microwave imaging system that reduces the number of required antennas for precise target location appropriate for medical applications is presented. The proposed system consists of four UWB extended gap ridge horn (EGRH) antennas covering the frequency band from 0.5 GHz to 1.5 GHz mounted on a cylindrical phantom that mimics the brain in an orthogonal set of two EGRH probes. This configuration has the ability to control both the longitudinal and transversal dimensions of the reconstructed target's image, rather than controlling the spatial resolution, by increasing the frequency band that can be easily affected by medium losses. The system is tested numerically and experimentally by the detection of a cylindrical target within a human brain model. [ABSTRACT FROM AUTHOR]

In this work, the contrast source inversion method is combined with a finite element method to solve microwave imaging problems. The paper's major contribution is the development of a novel contrast source variable discretization that leads to simplify the algorithm implementation and, at the same time, to improve the accuracy of the discretized quantities. Moreover, the imaging problem is recreated in a synthetic environment, where the antennas, and their corresponding coaxial port, are modeled. The implemented algorithm is applied to reconstruct the tissues' dielectric properties inside the head for brain stroke microwave imaging. The proposed implementation is compared with the standard one to evaluate the impact of the variables' discretization on the algorithm's accuracy. Furthermore, the paper shows the obtained performances with the proposed and the standard implementations of the contrast source inversion method in the same realistic 3D scenario. The exploited numerical example shows that the proposed discretization can reach a better focus on the stroke region in comparison with the standard one. However, the variation is within a limited range of permittivity values, which is reflected in similar averages. [ABSTRACT FROM AUTHOR]

Intracerebral hemorrhage is a life-threatening condition where conventional imaging modalities such as CT and MRI are indispensable in diagnosing. Nevertheless, monitoring the evolution of intracerebral hemorrhage still poses a technological challenge. We consider continuous monitoring of intracerebral hemorrhage in this context and present a differential microwave imaging scheme based on a linearized inverse scattering. Our aim is to reconstruct non-anatomical maps that reveal the volumetric evolution of hemorrhage by using the differences between consecutive electric field measurements. This approach can potentially allow the monitoring of intracerebral hemorrhage in a real-time and cost-effective manner. Here, we devise an indicator function, which reveals the position, volumetric growth, and shrinkage of hemorrhage. Later, the method is numerically tested via a 3D anthropomorphic dielectric head model. Through several simulations performed for different locations of intracerebral hemorrhage, the indicator function-based technique is demonstrated to be capable of detecting the changes accurately. Finally, the robustness under noisy conditions is analyzed to assess the feasibility of the method. This analysis suggests that the method can be used to monitor the evolution of intracerebral hemorrhage in real-world scenarios. [ABSTRACT FROM AUTHOR]

A low cost, with high performance, reduced graphene oxide (RGO) Ultra-wide Band (UWB) array sensor is presented to be applied with a technique of confocal radar-based microwave imaging to recognize a tumor in a human brain. RGO is used to form its patches on a Taconic substrate. The sensor functioned in a range of 1.2 to 10.8 GHz under UWB frequency. The sensor demonstrates high gain of 5.2 to 14.5 dB, with the small size of 90 mm × 45 mm2, which can be easily integrated into microwave imaging systems and allow the best functionality. Moreover, the novel UWB RGO array sensor is established as a detector with a phantom of the human head. The layers' structure represents liquid-imitating tissues that consist of skin, fat, skull, and brain. The sensor will scan nine different points to cover the whole one-sided head phantom to obtain equally distributed reflected signals under two different situations, namely the existence and absence of the tumor. In order to accurately detect the tumor by producing sharper and clearer microwave image, the Matrix Laboratory software is used to improve the microwave imaging algorithm (delay and sum) including summing the imaging algorithm and recording the scattering parameters. The existence of a tumor will produce images with an error that is lower than 2 cm. [ABSTRACT FROM AUTHOR]

In this study, a breast phantom measurement system based on radar-based microwave imaging technique is proposed. For this purpose, a small-size Vivaldi antenna with high gain and ultra-wide band is designed. The antenna bandwidth is in the range of 3–10.6 GHz, and the maximum realized gain is 8.5 dB. The size of the antenna is 36 × 36 mm2, and FR4 was used as dielectric material. Bandwidth and gain are increased by using edge slots. After the antenna hardware had been manufactured with PCB printed circuit technology, a mechanism was developed to measure breast phantom. After the measurement process is completed, a computer program converts the collected signals into images. For the testing of the system, canola oil was used as a phantom and the object in the oil was successfully imaged. [ABSTRACT FROM AUTHOR]

Many forest cover maps have been generated by using optical and/or microwave images and various forest definitions, but these forest cover maps have large discrepancies. Both forest definition and validation data used for accuracy assessment of forest cover maps are often considered as two of the major factors for the discrepancy among these forest cover maps. To date, few studies have assessed forest cover maps in terms of two biophysical parameters used in forest definition: (1) tree canopy height and (2) canopy coverage. We generated annual forest cover maps from 2007 to 2010 and evergreen forest cover maps from 2000 to 2021 in the Brazilian Amazon using the images from the Phased Array type L-band Synthetic Aperture Radar and Moderate Resolution Imaging Spectroradiometer, and the forest definition of the Food and Agriculture Organization (FAO) of the United Nations (>5-m tree height and >10% canopy coverage). The canopy height and coverage datasets from the Geoscience Laser Altimeter System during 2003–2007 were used to assess annual forest cover maps from 2007 to 2010 and evergreen forest cover maps from 2003 to 2007, and the results show high accuracy of these forest and evergreen forest cover maps in the Brazilian Amazon. [ABSTRACT FROM AUTHOR]

U-Net and Object-Attentional Super-Resolution Network (OASRN) neural network for electromagnetic imaging are compared and investigated in this paper. The outcome shows that though under limited training data, the regeneration capability is still highly reliable. We first transmit the electromagnetic waves to the scatterer and use the received scattered field information to calculate the estimated permittivity distribution by Green's function, subspace method and Dominant Current Scheme (DCS). The estimation technique can effectively reduce the training process of the neural network modules. Next, we train the U-Net and OASRN modules for real-time images. Lastly, we used Root Mean Square Error (RMSE) and Structural Similarity Index Measure (SSIM) to compare and analyze the reconstructed images of the two neural networks. Numerical results show that the reconstructed image by OASRN is better than that by U-net with 5% or 20% Gaussian noise for different dielectric constant distributions. [ABSTRACT FROM AUTHOR]

BACKGROUND: Antennas for the microwave imaging system are large which results in higher radiation, manufacturing cost, poor radiation characteristics and it will be difficult to locate on breast tissues. OBJECTIVE: We propose a wearable ultra-wide band antenna for use in the diagnosis of breast cancer bio-medical applications. METHODS: The antenna has been fabricated on 1.6 mm FR4 substrate with a dimension of 28 × 14.4 mm 2 and can operate between 2 GHz–12 GHz with S 11 < - 10 dB with best radiation characteristics. The prototype of the proposed antenna was fabricated and practically tested and the results were found to be consistent with the simulated results. The proposed UWB antenna is intended to radiate and receive information covering the entire spectrum from 3 GHz to 13 GHz. For good impendence matching throughout the larger spectrum, the defected ground structure (diamond shape) was exploited. All the dimensions of the proposed design are confirmed by parametric study and optimization. RESULTS: The maximum simulated efficiency was ranging from 80 to 84% in the desired operating frequency. The maximum Specific Absorption Rate of the proposed antenna was 0.98 W/Kg. Therefore, the proposed UWB antenna could be the right structure for breast cancer diagnosis in terms of SAR. The antenna was found to have a substantial radiation efficiency of around 78%–84% in the desired operating bandwidth. The overall realized gain of the proposed UWB antenna was seen ranging from 1.8–4.2 dB which is sufficient for bio-medical applications. CONCLUSION: The breast phantom was modeled for the validation of the performance of the antenna and SAR was analyzed. The value of SAR of the designed antenna was observed at about 0.98 W/Kg, which is suitable for medical applications. [ABSTRACT FROM AUTHOR]

This paper presents an alternative solution for detecting breast cancer through planar antennas. The designed antenna electric parameters are the best gain for tiny radiation elements, along with the suitable characteristic impedance and bandwidth focusing on a specific application. Antennas are deployed nowadays to provide access to the detection of malignant tumors. That solution coexists with those in the hospitals (X-ray Mammography, Biopsy, Ultrasound, and Tomography), as breast cancer is a worldwide health concern because many women die yearly. Unfortunately, none of these methods are efficient as microwave imaging techniques. In terms of rapidity, efficiency, sensitivity, and accuracy, a small microstrip patch antenna operating at the Industrial, Scientific, Medical (ISM) band (5.72-5.82 GHz) is proposed in this paper for early breast tumor screening. Designed from the High-Frequency Structure Simulator (HFSS), the rectangular microstrip patch-antenna of 12 × 12 × 1mm³, etched on an FR4 HTG-175 dielectric material (relative permittivity of 4.4 and 0.02 of loss tangent) has been simulated, prototyped, and experimentally measured with ZVA50 Vector Network Analyzer (VNA). The defective ground structure technique has been used to achieve the goals of the final prototype. The proposed antenna has 51.22 dB of return loss, 230MHz of bandwidth, with a radiation efficiency of 82% and a gain of 1.45 dBi at the resonance frequency of 5.73 GHz. Simulation results have been well-concluded through different tumor positions on the breast to take comprehensive precautions. Furthermore, a comparison with other antenna designs has been made. Due to the available laboratory equipment, the suggested work focused on the research part. [ABSTRACT FROM AUTHOR]

In the food industry, there is a growing demand for cost-effective methods for the inline inspection of food items able to non-invasively detect small foreign bodies that may have contaminated the product during the production process. Microwave imaging may be a valid alternative to the existing technologies, thanks to its inherently low-cost and its capability of sensing low-density contaminants. In this paper, a simple microwave imaging system specifically designed to enable the inspection of a large variety of food products is presented. The system consists of two circularly loaded antipodal Vivaldi antennas with a very large operative band, from 1 to 15 GHz, thus allowing a suitable spatial resolution for different food products, from mostly fatty to high water-content foods. The antennas are arranged in such a way as to collect a signal that can be used to exploit a recently proposed real-time microwave imaging strategy, leveraging the inherent symmetries that usually characterize food items. The system is experimentally characterized, and the achieved results compare favorably with the design specifications and numerical simulations. Relying on these positive results, the first experimental proof of the effectiveness of the entire system is presented confirming its efficacy.

Fourier-based imaging has been widely adopted for microwave imaging thanks to its efficiency in terms of computational complexity without compromising image resolution. Together with other backpropagation imaging algorithms like delay-and-sum (DAS), they are based on a far-field approach to the electromagnetic expression relating to fields and sources. To improve the accuracy of these techniques, this contribution presents a modified version of the well-known Fourier-based algorithm by taking into account the field radiated by the Tx/Rx antennas of the microwave imaging system. The impact on the imaged targets is discussed, providing a quantitative and qualitative analysis. The performance of the proposed method for subsampled microwave imaging scenarios is compared against other well-known aliasing mitigation methods.

In this paper, we present the microwave imaging of anisotropic objects by artificial intelligence technology. Since the biaxial anisotropic scatterers have different dielectric constant components in different transverse directions, the problems faced by transverse electronic (TE) polarization waves are more complex than those of transverse magnetic (TM) polarization waves. In other words, measured scattered field information can scarcely reconstruct microwave images due to the high nonlinearity characteristic of TE polarization. Therefore, we first use the dominant current scheme (DCS) and the back-propagation scheme (BPS) to compute the initial guess image. We then apply a trained convolution neural network (CNN) to regenerate the microwave image. Numerical results show that the CNN possesses a good generalization ability under limited training data, which could be favorable to deploy in image processing. Finally, we compare DCS and BPS reconstruction images for anisotropic objects by the CNN and prove that DCS is better than BPS. In brief, successfully reconstructing biaxial anisotropic objects with a CNN is the contribution of this proposal.

We present the design and the performance evaluation of a new interconnect for large-scale densely packed electronically scanned antenna arrays that utilize a high-speed digital board-to-board vertical connector. The application targets microwave tissue, imaging in the frequency range from 3 GHz to 8 GHz. The tissue-imaging arrays consist of hundreds of active antenna elements, which require low-reflection, low-loss, and low-crosstalk connections to their respective receiving and transmitting circuits. The small antenna size and the high array density preclude the use of coaxial connectors, which are also expensive and mechanically unreliable. Modern board-to-board high-speed connectors promise bandwidths as high as 12 GHz, along with high pin density, mechanical robustness, and low cost. However, their compatibility with the various transmission lines leading to/from the miniature printed antenna elements and microwave circuitry is not well studied. Here, we focus on the design of the transitions from coplanar waveguide transmission lines to/from a high-speed vertical connector. The performance of the interconnect is examined through electromagnetic simulations and measurements. Comparison is carried out with the expensive sub-miniature push-on sub-micro coaxial connectors commonly used in miniature radio-frequency electronics. The results demonstrate that high-speed vertical connectors can provide comparable performance in the UWB frequency range.

Background: microwave imaging (MWI) has emerged as a promising modality for breast cancer screening, offering cost-effective, rapid, safe and comfortable exams. However, the practical application of MWI for tumor detection and localization is hampered by its inherent low resolution and low detection capability. Methods: this study aims to generate an accurate tumor probability map directly from the scattering matrix. This direct conversion makes the probability map independent of specific image formation techniques and thus potentially complementary to any image formation technique. An approach based on a convolutional neural network (CNN) is used to convert the scattering matrix into a tumor probability map. The proposed deep learning model is trained using a large realistic numerical dataset of two-dimensional (2D) breast slices. The performance of the model is assessed through visual inspection and quantitative measures to assess the predictive quality at various levels of detail. Results: the results demonstrate a remarkably high accuracy (0.9995) in classifying profiles as healthy or diseased, and exhibit the model’s ability to accurately locate the core of a single tumor (within 0.9 cm for most cases). Conclusion: overall, this research demonstrates that an approach based on neural networks (NN) for direct conversion from scattering matrices to tumor probability maps holds promise in advancing state-of-the-art tumor detection algorithms in the MWI domain.

The problem of interaction of microwaves with plasma is complicated and has garnered considerable interest. Parameters of plasma density, collision frequency, and plasma size have been shown to exert substantial impacts on microwave transmission in plasma. The respective influences of plasma parameters can be evaluated relatively easily; however, plasma must also be considered integrally as it is an electrically neutral medium. In this paper, using a designed plasma-metal model, we assess how plasma parameters influence the interaction of microwaves in plasma, revealing a nonmonotonic and complex relationship. Using parameter optimization, we obtain plasma parameters for plasma device design that meet the requirements of microwave transmission in plasma; the findings should prove useful in applications of electromagnetic waves in plasma. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fractional regularized distorted Born iterative method (DBIM) to solve non-linear ill-posed problems of microwave imaging. Fractional regularization is a modification to Tikhonov regularization, where singular values are weighed with fractional power. As a result, the well-known effect of oversmoothing present in Tikhonov regularization is reduced, thereby the output image quality is improved. The results of this method are compared with standard DBIM using Tikhonov regularization. Various numerical examples of simulated and experimental datasets containing homogeneous as well as heterogeneous scatterers are considered to validate the effectiveness of the proposed approach. It is found that the proposed method improves the accuracy of estimated images over conventional DBIM.

Microwave imaging technology is a useful method often applied in medical diagnosis and can be used by the food industry to ensure food safety and quality. For fruit, ripeness is the primary characteristic which determines quality for the consumer. This paper proposes a novel microwave imaging system to determine the ripeness of watermelon as a proof of concept. The design employs a circular array with 10 Coplanar Vivaldi antennas offering wide bandwidth, high gain, and high efficiency. S-parameters between antennas are collected quickly via automated channel switching for fast image generation. Eight different watermelon samples of varying ripeness, type, dimensions, and origin are scanned and imaged. Comparisons with sample cross-sections show distinct differences in image characteristics based on watermelon maturity. Sugar concentration of unripe and ripe watermelon is also measured and plotted for further validation of the imaging technique.

Multi-source remote sensing data can provide abundant Earth observation information for lithology classification and identification, especially in some areas with complex geological conditions where the field geological survey is difficult to carry out. Compared with mountainous areas with large outcrops of rock, the lithology information obtained based on traditional field measurement and single optical remote sensing data in semi-arid areas is greatly limited due to the uneven vegetation coverage. While the combination of microwave and optical remote sensing technologies can effectively improve the integrality and reliability of the obtained lithology information in semi-arid areas. This paper selected Duolun County of Inner Mongolia Autonomous Region as the study area, added vegetation suppression for microwave and optical images into the conventional lithology classification process, and integrated Sentinel-1A and Landsat-8 images of the study area to carry out the experiment. The improved water-cloud model with the parameter of vegetation coverage and the method of feature-oriented principal component analysis based on multiple vegetation indices were used to realize the vegetation suppression in Synthetic Aperture Radar (SAR) backscattering images in two polarization modes and multispectral images. The processed SAR backscattering, SAR texture and spectral feature images were used to form seven feature combinations for lithology classification by the maximum likelihood method. The results showed that the proposed lithology classification scheme cannot achieve high-precision classification of all lithologic types, but it was effective in identifying the major lithologies dominated in the Quaternary deposits of the study area. In all feature combinations, the combination of all the three types of features reached the highest classification accuracy, with the overall classification accuracy of 88.60% and the kappa coefficient of 0.75 for five major lithologies. The experimental results fully demonstrated the advantages of integrating microwave and optical remote sensing data in lithology classification in a semiarid area. [ABSTRACT FROM AUTHOR]

Analysis of seasonal and interannual variations of the brightness temperature of the Yenisei, Pechora, and Khatanga estuaries is performed using the SMOS (Soil Moisture and Ocean Salinity) satellite MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) data for the period 2012–2020. It is shown that during the freezing period, with low river flow, Khatanga and Pechora bays are strongly influenced by the salty waters of the Laptev Sea and the Pechora Sea, respectively. In Yenisei Bay, the analysis reveals two characteristic areas for the winter period, delimited by a narrow strait between Sopochnaya Karga and Oshmarin capes. In the southern part of the bay, the water remains fresh or slightly salty, while in the northern part (north of the Sopochnaya Karga post), the water is always brackish, as this is the mixing zone of the fresh water of the Yenisei River and the salty water of the Kara Sea. The sea areas adjacent to the Yenisei and Pechora estuaries (the Kara Sea and Pechora Sea, respectively) are dynamic zones with brackish or salty water and constantly breaking ice under the influence of hydrological and climatic factors. The results show that SMOS MIRAS data can be used to estimate water salinity and movement of the transition zone under the ice in large bays and estuaries; to analyze the stability and dynamics of the Arctic sea-ice cover; and to determine the beginning of ice melt in large sea and freshwater areas. [ABSTRACT FROM AUTHOR]

Quasi-periodic pulsations (QPPs) are frequently detected in solar and stellar flares, but the underlying physical mechanisms are still to be ascertained. Here, we show microwave QPPs during a solar flare originating from quasi-periodic magnetic reconnection at the flare current sheet. They appear as two vertically detached but closely related sources with the brighter ones located at flare loops and the weaker ones along the stretched current sheet. Although the brightness temperatures of the two microwave sources differ greatly, they vary in phase with periods of about 10–20 s and 30–60 s. The gyrosynchrotron-dominated microwave spectra also present a quasi-periodic soft-hard-soft evolution. These results suggest that relevant high-energy electrons are accelerated by quasi-periodic reconnection, likely arising from the modulation of magnetic islands within the current sheet as validated by a 2.5-dimensional magnetohydrodynamic simulation. Quasi-periodic pulsations (QPPs) are frequently detected in solar and stellar flares, but the underlying physical mechanisms are still to be ascertained. Here, the authors show microwave QPPs during a solar flare originating from quasi-periodic magnetic reconnection at the flare current sheet. [ABSTRACT FROM AUTHOR]

Purpose: We have developed a fully 3D data acquisition system for microwave breast imaging which can operate simultaneously inside a magnetic resonance imaging (MRI). MRI is used regularly for breast imaging to distinguish tumors from normal tissue. It generally has poor specificity unless used with a gadolinium contrast agent. Microwave imaging could fill this need because of the good endogenous tumor:normal tissue property contrast, especially in light of safety concerns for gadolinium. The antenna array consists of 16 monopole antennas positioned in a horizontal circle surrounding the breast which can then be moved vertically for 3D coverage of the breast. The tank system materials were chosen to minimize artifacts in the MR image within the specific shared imaging zone. The support rods are stainless steel, albeit positioned sufficiently far from the imaging target to have little effect. The mechanical motion parts are all 3D printed plastic. Unlike many conventional antennas, the monopoles consist of just the center conductor and insulator of the coaxial cable, making it one of the least possible metallic structures. Methods: Data were acquired both inside and outside of the MR bore to confirm that the MR bore did not have adverse effects on the microwave imaging process. The imaging tank was filled with a mixture of glycerin and water to both provide a reasonable property match to the phantom and to highly attenuate the fields which also acted to suppress multi‐path signals. Microwave images were reconstructed using our Gauss–Newton scheme combined with a log transformation for a more linear convergence. MR images were also acquired to assess the effects of the microwave tank structures on the imaging. Results: The microwave measurement data were acquired in log magnitude and phase format at 200 MHz increments from 700–1900 MHz. Each antenna acted sequentially as a transmitter while the complement of 15 acted as a receiver. The single frequency images were reconstructed using a Gauss–Newton iterative technique with a standard log transformation to linearize the process. The data showed that the signal strengths were between 7–10 dB lower for the case when the array was inside the MRI versus when not. Notwithstanding, the image quality was still high because of the significant signal to noise ratio. The reconstructed images in both situations demonstrated good 3D object recovery of the vertically size and shaped varying object. The MR images were not adversely affected by the presence of antennas or feed structures. Conclusions: We have demonstrated that our technique can recover high‐quality images of a 3D varying object within an MRI system. Compatibility issues have been addressed for both the microwave and MRI systems. The reduced SNR for the case operating in the MRI did not adversely affect the images. To the best of our knowledge, this is the first example of a microwave imaging system operating in an MRI with full 3D volumetric capability. [ABSTRACT FROM AUTHOR]

This work presents a flat‐gain antipodal Vivaldi antenna (AVA) for ultra‐wideband (UWB) microwave imaging applications. The proposed antenna demonstrates measured and simulated impedance bandwidth from 2.3 GHz to 20 GHz with a flat gain over the band, with minimum gain variation of 2.9 dBi. The antenna is tested initially for its practical utility by measuring its specific absorption rate (SAR) value. The SAR value is observed in the simulator by modeling a realistic homogeneous as well as a heterogeneous breast phantom. The SAR value obtained complies with both American and European standards when averaged over 1 g as well as 10 g of tissue. The designed antenna is further utilized to detect multiple tumors in a realistic homogeneous and heterogeneous breast phantoms developed in the laboratory environment. The widely popular delay and sum (DAS) algorithm is utilized to reconstruct the tumor images. The imaging is done outside the anechoic chamber with an in‐house monostatic microwave imaging setup just to make it more convincing with real‐world dynamic scenario. The homogeneous phantom with four embedded tumors each of radius 4 mm and the heterogeneous phantom with two tumors, one of radius 3.5 mm and another of radius 5 mm are imaged in this work. The imaging results demonstrate that tumors of different sizes can be detected accurately in the case of both homogeneous and heterogeneous breast phantoms. AVA with an oil paper layer for bandwidth enhancement as well for achieving flat‐gain response is first of its kind to be reported in this work Also, the proposed work utilizes different phantoms for imaging several tumors of differing sizes, which has not before been described. [ABSTRACT FROM AUTHOR]

With the improvement of design capability and manufacture technology, the integration as well as the complexity of chips have also increased dramatically, and it becomes critical to realize noninvasive electromagnetic inspection of chips at higher resolutions. In this paper, we propose a submicron‐resolution microwave near‐field imaging system based on the nitrogen‐vacancy center (NV) in diamond, which mainly consists of an optical hardware system and a software control system, and can be used for nondestructive imaging of microwave fields on the surface of integrated circuits with submicron resolution. We applied this technique to scan the surface microwave field distribution of a GaAs single pole double throw switch chip. The scanning imaging system can image the near‐field magnetic distribution for the chip, which provides a new solution for analyzing the current distribution on the chip surface and thus addressing problems such as excessive current or crosstalk. It is worth mentioning that the diamond NV color center‐based sensor has a very high‐spatial resolution and magnetic field detection sensitivity at room temperature, while the dielectric constant of natural diamond is 5.5, which also provides the possibility of diamond as a sensor in complex radiation environments and is an ideal detector material for near‐field imaging of electromagnetic fields. [ABSTRACT FROM AUTHOR]

In this article, a circular‐polarized microwave correlation imaging antenna which is based on rotating elements is presented. The proposed reflect array consists of the circular‐polarized patches, the filling substrates, and the grounded metal plates. It is known that the key to microwave correlation imaging is the construction of a two‐dimensional random radiation field in space. Reflect array antennas use element rotation to achieve phase compensation, different compensation phases can be achieved through different rotation angles of elements, the rotation angle of the element is more random, and a more stray pattern for microwave correlation imaging can be obtained. In the reflector array, every single patch is randomized the rotation angle within a certain range, then a 25 × 25 unit random array is obtained by using HFSS‐MATLAB co‐simulation. The effective area of the feed source irradiated on the reflector is certain each time, so the use of different units to compensate for the difference in phase can make the pattern group obtained with high randomness. For verification, a sample with the overall size of 450 mm × 450 mm is designed, fabricated, and measured. When the feed source and the array move relative to each other, the correlation between the directional patterns is mainly concentrated between 0.5 and 0.8. The radiation field is sufficiently random, and the gain of the pattern can be reached 15 dB when the operating frequency is 12.5 GHz, which can effectively obtain high‐quality imaging results. [ABSTRACT FROM AUTHOR]

The inherent advantages of microwave imagining (MWI) being noninvasive and nonionizing for cancer detection, has attracted the attention of researchers in this area. Therefore, the present research article proposes an UWB (ultra‐wideband) stacked MSA (Micro‐strip antenna) for MWI of skin cancer. The proposed research work precisely focuses on the diagnosis of skin cancer in human forearm using the designed MSA in mono‐static radar based configuration to collect S parameter data and process it to form an image of the scanned body area. The proposed UWB SAMPA (stacked aperture coupled micro‐strip patch antenna) has three layered stacked aperture coupled geometry with a defected ground structure with dimensions 36 × 30 × 4.85 mm3. A simulated and measured bandwidth of 6.23 GHz (6.14 to 12.37 GHz) and 5.28 GHz (5.72 to 11 GHz) are observed and a gain of 6.3 and 6.4 dB is reported respectively. The in vitro detection of skin cancer is done by fabricating a bio mimic of human forearm with same electrical properties as that of human skin. The proposed SAMPA is used to collect S parameter data from the scan of forearm phantom. This data is then processed in beamforming algorithms to create a 2D dielectric profile of the scanned forearm area. The multiply Delay and Sum with Coherent factor (CF‐DMAS) beam forming algorithm is utilized for reconstruction of the 2D image of the cancerous effected area along with its locations and size in MATLAB. The proposed MWI procedure is validated experimentally with the prototype of proposed SAMPA and a human forearm phantom after simulations in CST MWS. The proposed SAMPA is also safe for the human exposure, as it has simulated specific absorption rate (SAR) on the forearm phantom of 1.013 and 1.09 at frequency 7.04 and 8.67 GHz respectively. [ABSTRACT FROM AUTHOR]

(1) Background: Microwave breast imaging (MBI) is a promising breast-imaging technology that uses harmless electromagnetic waves to radiate the breast and assess its internal structure. It utilizes the difference in dielectric properties of healthy and cancerous tissue, as well as the dielectric difference between different cancerous tissue types to identify anomalies inside the breast and make further clinical predictions. In this study, we evaluate the capability of our upgraded MBI device to provide breast tissue pathology. (2) Methods: Only patients who were due to undergo biopsy were included in the study. A machine learning (ML) approach, namely Gradient Boosting, was used to understand information from the frequency spectrum, collected via SAFE, and provide breast tissue pathology. (3) Results: A total of 54 patients were involved in the study: 29 of them had benign and 25 had malignant findings. SAFE acquired 20 true-positive, 24 true-negative, 4 false-positive and 4 false-negative findings, achieving the sensitivity, specificity and accuracy of 80%, 83% and 81%, respectively. (4) Conclusions: The use of harmless tissue radiation indicates that SAFE can be used to provide the breast pathology of women of any age without safety restrictions. Results indicate that SAFE is capable of providing breast pathology at a high rate, encouraging further clinical investigations. [ABSTRACT FROM AUTHOR]

In microwave medical imaging, the human skin reflects most of microwave energy due to the impedance mismatch between the air and the body. As a result, only a small portion of the microwave energy can enter the body and work for medical purpose. One solution to tackle this issue is to use a coupling (or matching) medium, which can reduce unwanted reflections on the skin and meanwhile improve spatial imaging resolution. A few types of fluids were measured in this paper for their dielectric properties between 500 MHz and 13.5 GHz. Measurements were performed by a Keysight programmable network analyzer (PNA) with a dielectric probe kit, and dielectric constant and conductivity of the fluids were presented in this paper. Then, quantitative computations were exercised to present the attenuations due to the reflection on the skin and to the loss in each coupling medium, based on the measured liquid dielectric values. Finally, electromagnetic simulations verified that the coupling liquid can allow more microwave energy to enter the body to allow for a more efficient medical examination. [ABSTRACT FROM AUTHOR]

Patient-specific information on the depth of Axillary Lymph Nodes (ALNs) is important for the development of new diagnostic imaging technologies, e.g. Microwave Imaging (MWI), aiming to assess the diagnosis of ALNs during breast cancer staging. Studies about ALNs depth have been presented for treatment planning, but they lack information on sample size and usability of the data to infer the depth of ALNs. The aim of this study was to create a mathematical model that can be used to predict a depth interval where level I ALNs are likely to be located. We extracted biometric features of 98 patients who underwent breast Magnetic Resonance Imaging (MRI) to train two types of regression models. We then tested different combination of features to predict ALNs depth and found the best predictor. The final prediction models were then implemented in an algorithm used for MWI and tested with anthropomorphic phantoms of the axillary region. Body Mass Index (BMI) was the feature with best performance to predict ALNs depth with coefficient of determination (R2) ranging from 0.49 to 0.55 and Root Mean Squared Error (RMSE) ranging from 0.68 to 0.91 cm. The proposed model showed satisfactory results in microwave images of patients with different BMIs. The presented results contribute to the development of reconstruction algorithms for new imaging technologies and to the assessment of ALNs in other medical applications. • Estimating patient-specific information on the depth of Axillary Lymph Nodes (ALNs). • Treatment planning and new imaging modalities can be improved with ALN depth. • Mathematical model created with biometric features from Magnetic Resonance Imaging. • Prediction of an interval of depth where ALNs are likely to be located. • Validation of the usefulness of the model for a Microwave Imaging application. [ABSTRACT FROM AUTHOR]

Holographic microwave imaging (HMI) has been proposed for early breast cancer diagnosis. Automatically classifying benign and malignant tumors in microwave images is challenging. Convolutional neural networks (CNN) have demonstrated excellent image classification and tumor detection performance. This study investigates the feasibility of using the CNN architecture to identify and classify HMI images. A modified AlexNet with transfer learning was investigated to automatically identify, classify, and quantify four and five different HMI breast images. Various pre-trained networks, including ResNet18, GoogLeNet, ResNet101, VGG19, ResNet50, DenseNet201, SqueezeNet, Inception v3, AlexNet, and Inception-ResNet-v2, were investigated to evaluate the proposed network. The proposed network achieved high classification accuracy using small training datasets (966 images) and fast training times. [ABSTRACT FROM AUTHOR]

Reperfusion therapy administration timing in acute ischaemic stroke is the main determinant of patients' mortality and long-term disability. Indeed, the first hour from the stroke onset is defined the "golden hour", in which the treatment has the highest efficacy and lowest side effects. Delayed ambulance transport, inappropriate triage and difficulty in accessing CT scans lead to delayed onset to treatment time (OTT) in clinical practice. To date brain CT scan is needed to rule out intracranial haemorrhage, which is a major contraindication to thrombolytic therapy. The availability, dimension and portability make CT suitable mainly for intrahospital use, determining further delays in the therapies administration. This review aims at evaluating portable neurophysiology technologies developed with the scope of speeding up the diagnostic phase of acute stroke and, therefore, the initiation of intravenous thrombolysis. Medline databases were explored for studies concerning near infrared spectroscopy (NIRS), bioelectrical impedance spectroscopy (BIS) and Microwave imaging (MWI) as methods for stroke diagnosis. A total of 1368 articles were found, and 12 of these fit with our criteria and were included in the review. For each technology, the following parameters were evaluated: diagnostic accuracy, ability to differentiate ischaemic and haemorrhagic stroke, diagnosis time from stroke onset, portability and technology readiness level (TRL). All the described methods seem to be able to identify acute stroke even though the number of studies is very limited. Low cost and portability make them potentially usable during ambulance transport, possibly leading to a reduction of stroke OTT along with the related huge benefits in terms of patients outcome and health care costs. In addition, unlike standard imaging techniques, neurophysiological techniques could allow continuous monitoring of patients for timely intrahospital stroke diagnosis. First hour from the stroke onset is defined the "golden hour", in which the treatment has the highest efficacy and lowest side effects. The delay for stroke onset to brain imaging time is one of the major reasons why only a minority of patients with acute ischaemic stroke are eligible to reperfusion therapies. Neurophysiology techniques (NIRS, BIS and MWI) could have a potential high impact in reducing the time to treatment in stroke patients. [ABSTRACT FROM AUTHOR]

A rectangular DRA with a Sierpinski fractal defected ground structure has been simulated and experimentally tested for UWB operation to be used in the detection of breast tumor using monostatic radar-based microwave imaging (MRMI). The proposed fractal DRA is designed to cover UWB characteristics (5.6–14.2 GHz) and high peak gain (5.8 dB). Then it is used as a sensor for extracting S-parameters from the breast phantom for tumor detection. In this regard, the fractal DRA is positioned in front of phantom, revolved around it (10°) in elevation and azimuthal plane to collect backscattered signals (with/without tumor). For validation, an artificial phantom of breast consisting of three layers, gelatin (skin), petroleum jelly (fat) and wheat flour (tumor), with electrical properties same as that of human breast is prepared and the S-parameters are recorded at different intervals. The captured reflection parameters processed in different imaging algorithms are: delay and sum and coherent factor (DAS-CF) and delay multiply and sum and coherent factor (DMAS-CF) to reconstruct 2D images of the breast tumor using MATLAB. The simulated specific absorption rate of the proposed DRA on the breast phantom at the resonant frequencies of 6.1 and 13.9 GHz was observed to be 1.02 and 1.13 W/Kg, respectively, which is safe limit for human exposure. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) uses microwave coherent imaging, so SAR images are complex in essence. Traditional SAR image target recognition methods based on neural networks usually only process the amplitude information of SAR images, but cannot effectively use the unique complex information of SAR images. This paper aims at the application of ship target recognition in SAR image. Starting from the essence of SAR image, the complex information representation of input data is implicitly provided by combining the real part, imaginary part and amplitude information of SAR image. Then the channel attention mechanism is introduced on the basis of the ResNet18 network and is structure, so that the network can adaptively learn the complex information contained in the three channels of real part, imaginary part and amplitude. Finally, label smoothing regularization is introduced to solve the over-fitting phenomenon due to the lack of samples in complex data sets. The experimental results based on the OpenSARShip data set show that the method proposed in this paper can make better use of the complex information of the SAR image itself, and to a certain extent improve the effect of ship target recognition based on neural network. [ABSTRACT FROM AUTHOR]