Microwave Imaging Technique Research Articles

Single-frequency microwave imaging with dynamic metasurface apertures

Conventional microwave imaging schemes, enabled by the ubiquity of coherent sources and detectors, have traditionally relied on frequency bandwidth to retrieve range information, while using mechanical or electronic beamsteering to obtain cross-range information. This approach has resulted in complex and expensive hardware when extended to large-scale systems requiring ultrawide bandwidth. Relying on bandwidth can create difficulties in calibration, alignment, and imaging of dispersive objects. We present an alternative approach using an electrically-large, dynamically reconfigurable, metasurface antenna that generates spatially-distinct radiation patterns as a function of tuning state. The metasurface antenna comprises a waveguide feeding an array of metamaterial radiators, each of whose properties can be modified by applying a voltage to a diode integrated into the element. By deploying two of these apertures, one as the transmitter and one as the receiver, we realize sufficient spatial diversity to alleviate the dependence on frequency bandwidth and obtain both range and cross-range information using measurements at a single frequency. We demonstrate the method experimentally, using one-dimensional dynamic metasurface apertures and reconstructing various two-dimensional scenes (range and cross-range). Furthermore, we modify a conventional microwave imaging technique---the range migration algorithm---to be compatible with such configurations, resulting in an imaging system that is fast and simple in both software and hardware. The imaging scheme presented in this paper has broad application to radio frequency imaging, including security screening, through-wall imaging, biomedical diagnostics, and synthetic aperture radar.

Early Diagnosis of Breast Cancer.

Early-stage cancer detection could reduce breast cancer death rates significantly in the long-term. The most critical point for best prognosis is to identify early-stage cancer cells. Investigators have studied many breast diagnostic approaches, including mammography, magnetic resonance imaging, ultrasound, computerized tomography, positron emission tomography and biopsy. However, these techniques have some limitations such as being expensive, time consuming and not suitable for young women. Developing a high-sensitive and rapid early-stage breast cancer diagnostic method is urgent. In recent years, investigators have paid their attention in the development of biosensors to detect breast cancer using different biomarkers. Apart from biosensors and biomarkers, microwave imaging techniques have also been intensely studied as a promising diagnostic tool for rapid and cost-effective early-stage breast cancer detection. This paper aims to provide an overview on recent important achievements in breast screening methods (particularly on microwave imaging) and breast biomarkers along with biosensors for rapidly diagnosing breast cancer.

A feasibility study of elastography based confocal microwave imaging technique for breast cancer detection

Breast cancer is a common cancer type in women and its death rate is second highest among different kind of cancers. Early detection is an efficient way for curing and recovery. Confocal microwave imaging (CMI) using electromagnetic method for detection of breast cancer can avoid ionization caused by mammography. CMI uses the contrast of electrical properties between tumor and normal breast tissue to identify the existence and location of the tumor. However, new research result shows that gland and tumor have similar dielectric constant and electrical conductivity, so it is hard to distinguish gland and tumor. This paper proposes a new method based on elastography for the tumor identification. The high Young's modulus contrast of the two tissues resulting in different level of deformation by compression will provide sufficient features to discriminate whether the reflected signal is belonged to gland or tumor. Finite-difference time-domain (FDTD) method was used in simulation. Some examples were presented to validate the identification.

$L_{1}$ -Regularization-Based SAR Imaging and CFAR Detection via Complex Approximated Message Passing

Synthetic aperture radar (SAR) is a widely used active high-resolution microwave imaging technique that has all-time and all-weather reconnaissance ability. Compared with traditionally matched filtering (MF)-based methods, ${L_{q}}({0 \le q \le 1})$ regularization technique can efficiently improve SAR imaging performance e.g., suppressing sidelobes and clutter. However, conventional $L_{q}$ -regularization-based SAR imaging approach requires transferring the 2-D echo data into a vector and reconstructing the scene via 2-D matrix operations. This leads to significantly more computational complexity compared with MF, and makes it very difficult to apply in high-resolution and wide-swath imaging. Typical $L_{q}$ regularization recovery algorithms, e.g., iterative thresholding algorithm, can improve imaging performance of bright targets, but not preserve the image background distribution well. Thus, image background statistical-property-based applications, such as constant false alarm rate (CFAR) detection, cannot be applied to regularization recovered SAR images. On the other hand, complex approximated message passing (CAMP), an iterative recovery algorithm for $L_{1}$ regularization reconstruction, can achieve not only the sparse estimation of the original signal as typical regularization recovery algorithms but also a nonsparse solution simultaneously. In this paper, two novel CAMP-based SAR imaging algorithms are proposed for raw data and complex radar image data, respectively, along with CFAR detection via the CAMP recovered nonsparse result. The proposed method for raw data can not only improve SAR image performance as conventional $L_{1}$ regularization technique but also reduce the computational cost efficiently. While only when we have MF recovered SAR complex image rather than raw data, the proposed method for complex image data can achieve a similar reconstructed image quality as the regularization-based SAR imaging approach using the full raw data. The most important contribution of this paper is that the proposed CAMP-based methods make CFAR detection based on the regularization reconstruction SAR image possible using their nonsparse scene estimations, which has a similar background statistical distribution as the MF recovered images. The experimental results validated the effectiveness of the proposed methods and the feasibility of the recovered nonsparse images being used for CFAR detection.

Experimental Microwave Imaging With a Novel Corrugated Vivaldi Antenna

In this communication, a novel corrugated Vivaldi antenna (CVA) is developed for microwave imaging (MWI) applications. In particular, different from the previous works, the lengths of the corrugations are independently optimized to reach the final design. It is shown that by letting more parameters to be optimized in the design process, we can obtain a more effective CVA, which has more suitable characteristics for MWI applications (i.e., higher gain, broader bandwidth) compared with the previous designs. Apart from that, the imaging performance of the proposed design is compared with a generic VA having the same size of the proposed antenna. As imaging algorithm, a recently introduced qualitative MWI technique, the scattering parameter-based linear sampling method (S-LSM), is employed. The similarity between exact shapes of the targets and the obtained qualitative results are compared with the help of the well-known Jaccard index. Experimental results show that the proposed CVA performs better than a generic VA in such real-world MWI problems.

Hybrid graphene–copper UWB array sensor for brain tumor detection via scattering parameters in microwave detection system

Hybrid graphene–copper ultra-wideband array sensor applied to microwave imaging technique is successfully used in detecting and visualizing tumor inside human brain. The sensor made of graphene coated film for the patch while copper for both the transmission line and parasitic element. The hybrid sensor performance is better than fully copper sensor. Hybrid sensor recorded wider bandwidth of 2.0–10.1 GHz compared with fully copper sensor operated from 2.5 to 10.1 GHz. Higher gain of 3.8–8.5 dB is presented by hybrid sensor, while fully copper sensor stated lower gain ranging from 2.6 to 6.7 dB. Both sensors recorded excellent total efficiency averaged at 97 and 94%, respectively. The sensor used for both transmits equivalent signal and receives backscattering signal from stratified human head model in detecting tumor. Difference in the data of the scattering parameters recorded from the head model with presence and absence of tumor is used as the main data to be further processed in confocal microwave imaging algorithm in generating image. MATLAB software is utilized to analyze S-parameter signals obtained from measurement. Tumor presence is indicated by lower S-parameter values compared to higher values recorded by tumor absence.

AN INVESTIGATION INTO ELECTROMAGNETIC BASED IMPEDANCE TOMOGRAPHY USING REALISTIC HUMAN HEAD MODEL

<p class="lead">The objective of this research is to investigate the feasibility of Electromagnetic based Impedance Tomography (EMIT) for brain stroke detection, localization and classification. Electromagnetic based Impedance Tomography employing microwave imaging technique is an emerging brain stroke diagnostic modality. It relies on the significant contrast between dielectric properties of the normal and abnormal brain tissues. To study the interaction between micro-wave signals and head tissues, the simulations are performed using a geometrically simple 3-D ellipsoid head model with emulated stroke. Finite Element numerical technique is adopted to find the solution of Maxwell’s equations to measure the transmitted and backscattered signals in forward problem. Contrast Source Inversion technique is proposed to solve the inverse scattering problem and reconstruct brain images based on calculated dielectric profiles. Detailed analysis is performed to determine the safety limits of transmitted signals to minimize ionizing effects while ensuring maximum penetration. The simulations verify the inhomogeneous and frequency-dispersive behavior of brain tissue’s dielectric properties. The solution of the forward problem demonstrates the microwave signals scattering by the multilayer structure of the head model, duly validated by analytical results. The scattering phenomena can be fully capitalized by image reconstruction algorithm to obtain brain images and detect stroke presence. The initial results obtained in this research and prior work indicates that EMIT-based head imaging system has a potential for rapid stroke detection, classification, and continuous brain monitoring and offers a comparatively cost-effective solution.</p>

Detection of Cracks in Concrete Structure Using Microwave Imaging Technique

Cracks in concrete or cement based materials present a great threat to any civil structures; they are very dangerous and have caused a lot of destruction and damage. Even small cracks that look insignificant can grow and may eventually lead to severe structural failure. Besides manual inspection that is ineffective and time-consuming, several nondestructive evaluation techniques have been used for crack detection such as ultrasonic technique, vibration technique, and strain-based technique; however, some of the sensors used are either too large in size or limited in resolution. A high resolution microwave imaging technique with ultrawideband signal for crack detection in concrete structures is proposed. A combination of the delay-and-sum beamformer with full-view mounted antennas constitutes the image reconstruction algorithm. Various anomaly scenarios in cement bricks were simulated using FDTD, constructed, and measured in the lab. The reconstructed images showed a high similarity between the simulation and the experiment with a resolution of λ/14 which enables a detection of cracks as small as 5 mm in size.

Adaptive Back-Projection Algorithm Based on Climb Method for Microwave Imaging

In this paper, we propose a microwave imaging technique that involves an adaptive back-projection (BP) algorithm based on a climb method. In general, the range migration algorithm (RMA) provides high computational efficiency and high accuracy for synthetic aperture radar (SAR) imaging. However, RMA involves wavenumber domain interpolation, which may adversely affect the accuracy of the target imaging. The BP algorithm, which has its origins in medical imaging techniques, has been studied to overcome this problem in SAR imaging, because it avoids interpolation associated with domain conversion, but it also has a huge computational cost. Therefore, this paper proposes adaptive processing based on a climb method to find the target indicator as real target responses. The real target responses are predetermined and the raw data are reproduced to reduce the computational cost and to improve the image quality. Finally, the superiority of proposed algorithm is demonstrated using both the simulated and experimental data.

Fast Frequency-Based Multistatic Microwave Imaging Algorithm With Application to Brain Injury Detection

A multistatic microwave imaging technique is presented for fast diagnosis of medical emergencies pertaining to brain injuries. The frequency-based imaging method utilizes Bessel functions to estimate the scattered power intensity inside the imaged region from measured multistatic scattered signals outside the imaged region in a quasi-real-time manner. A theory is used to prove that the relation between the scattered fields outside the imaged object (the head) and the internal scattering profile follows the first order of first type Bessel function. To reconstruct the internal scattered power intensity accurately, the average-trace subtraction method is used to remove the skin reflections and clutters. The presented algorithm is verified using realistic numerical simulations and experimental measurements, which are performed using a radar-based head imaging system that includes an antenna array containing eight elements, microwave transceiver, and switching network. To emulate different brain injuries, realistic head phantoms are utilized. The obtained results using frequency steps that meet Nyquist criterion confirm the reliability of the proposed method in the successful detection of different sizes and locations of injuries inside the head phantom in a fast and consistent way. In comparison with existing multistatic time-domain methods, the presented approach is faster and more accurate.

Non-contact inspection of construction materials using 3-axis multifunctional imaging system with microwave and laser sensing techniques

Microwave imaging techniques have been applied in a wide variety of commercial and scientific applications such as non-destructive testing and evaluation, material characterization and medical applications [1]-[3]. Microwave non-contact techniques have demonstrated the ability to detect flaws in various dielectric and composite materials using relatively simple microwave reflectometers [3]-[8]. These materials include construction materials and composites such as cement-based materials and concrete structures strengthened by carbon fiber reinforced polymer (CFRP) laminates possessing delamination and debonds [3]-[7], and layered dielectric materials with a hidden crack [8]. The assessment of cracks in concrete structures is crucial for their safety and cost effective maintenance since they not only affect their appearance but also the load-carrying capacity and durability [9]-[11]. Several non-invasive testing techniques have been under investigation for the purpose of crack detection in concrete. They include acoustic testing, ultrasonic techniques, optical methods, and microwave techniques. However, if compared with non-invasive methods for metal structures, the non-invasive methods for concrete structures are at a relatively early stage of development [9]-[11].

Single and Dual Photonic Jets and Corresponding Backscattering Enhancement With Tipped Waveguides: Direct Observation at Microwave Frequencies

This paper reports experiments on single and dual electromagnetic jets (also called photonic jets) emerging from a weakly multimode waveguide excited by a plane wave in the microwaves frequencies (30 GHz), ended with a tip with a special shape. The measurements are performed with a two-dimensional spatial field mapping system. This system acquires the electric field distribution at the end of the tipped waveguide. Qualitatively, the electric field maps confirm the presence of photonic jets that were recently predicted by a computational model based on boundary integral equations. Backscattering induced by the interaction of a particle with the electromagnetic jet is also measured; particles with a size smaller than the incident wavelength can be easily detected. The high sensitivity feature of the backscattering can be used to develop a near-field technique for microwave or optical imaging.

Widefield microwave imaging in alkali vapor cells with sub-100 μm resolution

We report on widefield microwave vector field imaging with sub- resolution using a microfabricated alkali vapor cell. The setup can additionally image dc magnetic fields, and can be configured to image microwave electric fields. Our camera-based widefield imaging system records 2D images with a 6 × 6 mm2 field of view at a rate of 10 Hz. It provides up to spatial resolution, and allows imaging of fields as close as above structures, through the use of thin external cell walls. This is crucial in allowing us to take practical advantage of the high spatial resolution, as feature sizes in near-fields are on the order of the distance from their source, and represent an order of magnitude improvement in surface-feature resolution compared to previous vapor cell experiments. We present microwave and dc magnetic field images above a selection of devices, demonstrating a microwave sensitivity of per voxel, at present limited by the speed of our camera system. Since we image 120 × 120 voxels in parallel, a single scanned sensor would require a sensitivity of at least to produce images with the same sensitivity. Our technique could prove transformative in the design, characterization, and debugging of microwave devices, as there are currently no satisfactory established microwave imaging techniques. Moreover, it could find applications in medical imaging.

A Compressive Sensing Approach for 3D Breast Cancer Microwave Imaging With Magnetic Nanoparticles as Contrast Agent.

In microwave breast cancer imaging magnetic nanoparticles have been recently proposed as contrast agent. Due to the non-magnetic nature of human tissues, magnetic nanoparticles make possible the overcoming of some limitations of conventional microwave imaging techniques, thus providing reliable and specific diagnosis of breast cancer. In this paper, a Compressive Sensing inspired inversion technique is introduced for the reconstruction of the magnetic contrast induced within the tumor. The applicability of Compressive Sensing theory is guaranteed by the fact that the underlying inverse scattering problem is linear and the searched magnetic perturbation is sparse. From the numerical analysis, performed in realistic conditions in 3D geometry, it has been pointed out that the adoption of this new tool allows improving resolution and accuracy of the reconstructions, as well as reducing the number of required measurements.

A 3-D Level Set Method for Microwave Breast Imaging.

Conventional inverse-scattering algorithms for microwave breast imaging result in moderate resolution images with blurred boundaries between tissues. Recent 2-D numerical microwave imaging studies demonstrate that the use of a level set method preserves dielectric boundaries, resulting in a more accurate, higher resolution reconstruction of the dielectric properties distribution. Previously proposed level set algorithms are computationally expensive, and thus, impractical in 3-D. In this paper, we present a computationally tractable 3-D microwave imaging algorithm based on level sets. We reduce the computational cost of the level set method using a Jacobian matrix, rather than an adjoint method, to calculate Frechet derivatives. We demonstrate the feasibility of 3-D imaging using simulated array measurements from 3-D numerical breast phantoms. We evaluate performance by comparing full 3-D reconstructions to those from a conventional microwave imaging technique. We also quantitatively assess the efficacy of our algorithm in evaluating breast density. Our reconstructions of 3-D numerical breast phantoms improve upon those of a conventional microwave imaging technique. The density estimates from our level set algorithm are more accurate than those of the conventional microwave imaging, and the accuracy is greater than that reported for mammographic density estimation. Our level set method leads to a feasible level of computational complexity for full 3-D imaging, and reconstructs the heterogeneous dielectric properties distribution of the breast more accurately than conventional microwave imaging methods. 3-D microwave breast imaging using a level set method is a promising low-cost, nonionizing alternative to current breast imaging techniques.

INVESTIGATION OF ANTENNA ARRAY CONFIGURATIONS USING FAR-FIELD HOLOGRAPHIC MICROWAVE IMAGING TECHNIQUE

Biomedical imaging has played an important role in identifying and monitoring the effectiveness of the current state of the art treatments for many diseases. The authors recently proposed a novel single-transmitter-multiple-receiver holographic microwave imaging (HMI) technique for imaging small inclusion embedded in a dielectric object which has potential application in medical diagnostics. HMI image quality depends highly on the antenna baseline difference, in order words, the antenna array configuration. Different antenna arrays produce different quality of dielectric images by using HMI imaging algorithm. This paper investigates the antenna array configurations effect on image quality by using HMI imaging approach. Three configurations including spiral, random and regularly spaced arrays are presented. Both simulated and experimental results are obtained and compared to fully demonstrate the effectiveness of antenna arrays to the HMI technique. The results show that the proposed spiral and random array configurations have an ability to produce high-resolution images at significantly lower costs than regularly spaced arrays.

Fast Microwave Medical Imaging Based on Iterative Smoothed Adaptive Thresholding

This letter presents a fast microwave imaging technique based on the concept of smoothed minimization and adaptive thresholding. The distorted Born iterative method (DBIM) is used to solve the electromagnetic (EM) inverse scattering problem. We propose to solve the set of underdetermined equations at each iteration of the DBIM algorithm using an L2 regularized iterative smoothed adaptive thresholding ( L2-ISATCS) technique. Our simulation results confirm that this technique can reduce considerably the required reconstruction times for the DBIM method relative to previously suggested compressed sensing (CS)-based approaches .

Efficient SOM-Based ATR Method for SAR Imagery With Azimuth Angular Variations

The microwave imaging technique, especially for synthetic aperture radar (SAR), has significant advantages in providing high-resolution complex target images, even in darkness or adverse weather conditions. Nevertheless, it is still difficult for human operators to identify targets on SAR images because they are generated using radio signals with wavelengths at the order of cm. To deal with this, various approaches for efficient automatic target recognition (ATR), based on neural networks or support vector machines (SVM), have been developed. Previously we proposed a promising ATR method using a supervised self-organizing map (SOM), where a binarized SAR image is accurately classified by exploiting the unified distance matrix (U-matrix) metric. Although this method enhances ATR performance considerably, even with SAR images heavily contaminated by random noise, the calculation burden is enormous under expansions of scale and then cannot maintain the ATR performance, especially in cases with azimuth angle variations. In this letter, we propose a constrained learning scheme for generating the SOM and introduce the A-star algorithm to handle SOM scale expansion. Experimental investigations demonstrate the effectiveness of our proposed method.

Piecewise and Wiener Filter-Based SAR Techniques for Monostatic Microwave Imaging of Layered Structures

In this paper, two new techniques for microwave imaging of layered structures are introduced. These techniques were developed to address the limiting issues associated with classical synthetic aperture radar (SAR) imaging techniques in generating focused and properly-positioned images of embedded objects in generally layered dielectric structures. The first method, referred to as piecewise SAR (PW-SAR), is a natural extension of the classical SAR technique, and considers physical and electrical properties of each individual layer and the discontinuity among them. Although this method works well with low loss dielectric media, its applicability to lossy media is limited. This is due to the fact that this method does not consider signal attenuation. Moreover, multiple reflections within each layer are not incorporated. To improve imaging performance in which these important phenomena are included, a second method was developed that utilizes the Green's function of the layered structure and casts the imaging approach into a deconvolution procedure. Subsequently, a Wiener filter-based deconvolution technique is used to solve the problem. The technique is referred to as Wiener filter-based layered SAR (WL-SAR). The performance and efficacy of these SAR based imaging techniques are demonstrated using simulations and corresponding measurements of several different layered media.

HUYGENS PRINCIPLE BASED IMAGING OF MULTILAYERED OBJECTS WITH INCLUSIONS

The application of a recently introduced microwave imaging technique based on the Huygens principle (HP), has been extended to multilayered objects with inclusions in this paper. The methodology of HP permits the capture of contrast such that difierent material properties within the region of interest can be discriminated in the flnal image, and its simplicity removes the need to solve inverse problems when forward propagating the waves. Therefore the procedure can identify and localize signiflcant scatterers inside a multilayered volume, without having apriori knowledge on the dielectric properties of the target object. Additionally, an analytically-based approach for analyzing UltraWide Bandwidth (UWB) body propagation is presented, where the body is modeled as a 3-layer stratifled cylinder with an eccentric inclusion. Validation of the technique through both simulations and measurements on multilayered cylindrical objects with inclusions has been performed.