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

This paper deals with the imaging problem from data collected by means of a microwave photonics-based distributed radar network. The radar network is leveraged on a centralized architecture, which is composed of one central unit (CU) and two transmitting and receiving dual-band remote radar peripherals (RPs), it is capable of collecting monostatic and multistatic phase-coherent data. The imaging is herein formulated as a linear inverse scattering problem and solved in a regularized way through the truncated singular value decomposition inversion scheme. Specifically, two different imaging schemes based on an incoherent fusion of the tomographic images or a fully coherent data processing are herein developed and compared. Experimental tests carried out in a port scenario for imaging both a stationary and a moving target are reported to validate the imaging approach. [ABSTRACT FROM AUTHOR]

In clinical settings, computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) are commonly employed in brain imaging to assist clinicians in determining the type of stroke in patients. However, these modalities are associated with potential hazards or limitations. In contrast, microwave imaging emerges as a promising technique, offering advantages such as non-ionizing radiation, low cost, lightweight, and portability. The primary challenges faced by microwave tomography include the severe ill-posedness of the electromagnetic inverse scattering problem and the time-consuming nature and unsatisfactory resolution of iterative quantitative algorithms. This paper proposes a learning electric field enhancement imaging method (LEFEIM) to achieve quantitative brain imaging based on a microwave tomography system. LEFEIM comprises two cascaded networks. The first, based on a convolutional neural network, utilizes the electric field from the receiving antenna to predict the electric field distribution within the imaging domain. The second network employs the electric field distribution as input to learn the dielectric constant distribution, thereby realizing quantitative brain imaging. Compared to the Born Iterative Method (BIM), LEFEIM significantly improves imaging time, while enhancing imaging quality and goodness-of-fit to a certain extent. Simultaneously, LEFEIM exhibits anti-noise capabilities. [ABSTRACT FROM AUTHOR]

The availability of multichannel ground-penetrating radar systems capable of gathering multiview, multistatic, multifrequency data provides novel chances to improve subsurface imaging results. However, customized data processing techniques and smart choices of the measurement setup are needed to find a trade-off between image quality and acquisition time. In this paper, we adopt a Born Approximation-based full 3D approach, which can manage multiview-multistatic, multifrequency data and faces the imaging as a linear inverse scattering problem. The inverse problem is solved by exploiting the truncated singular value decomposition as a regularization scheme. The paper presents a theoretical study aimed at assessing how the reconstruction capabilities of the imaging approach depend on the adopted measurement configuration. In detail, the performance achievable in the standard case of multimonostatic, multifrequency data is compared with that provided by a multiview-multistatic, multifrequency configuration, where the data are gathered by considering a progressively increasing number of transmitting antennas. The comparison of the achievable imaging performance is carried out by exploiting the spectral content and the point spread function, which are general tools to foresee the achievable reconstruction capabilities. Reconstruction results related to a numerical experiment based on full-wave data are also provided. [ABSTRACT FROM AUTHOR]

Background. The purpose of the work is the development and software implementation of the brute force method for solving inverse diffraction problem. Material and methods. For solving the problem integral equations approach is used; for the numerical solution of the Lippmann – Schwinger equation, the collocation method is applied; for the effective solution of systems of linear algebraic equations, a special method of matrix inversion is used. Results. An efficient method for solving the problem of localization of a region of inhomogeneity with a given refractive index has been developed. Conclusions. The proposed method can be used for solving problems of microwave tomography with a small number of field measurements.

Background. The purpose of the work is to develop, implement and test a projection method for comparing methods of focusing electromagnetic fields passing through a dielectric partially shielded half ball. Material and methods. The Galerkin method is implemented for the vector integro-differential equation of the diffraction problem; basic vector functions in a three-dimensional solid and a parameterized nonplanar screen are determined; a parallel algorithm for solving the problem using the MSMPI library is implemented. Results. Various options for shielding the half ball in order to focus the electric field inside the body were compared. Conclusions: the most effective method of focusing the wave in the proposed arrangement of the screen-body system has been selected.

This work, written for engineers or managers with no special knowledge of quantum mechanics, nor deep experience in radar, aims to help the scientific, industrial, and governmental community to better understand the basic limitations of proposed microwave quantum radar (QR) technologies and systems. Detection and ranging capabilities for QR are critically discussed and a comparison with its closest classical radar (CR), i.e., the noise radar (NR), is presented. In particular, it is investigated whether a future fielded and operating QR system might really outperform an "equivalent" classical radar, or not. The main result of this work, coherently with the recent literature, is that the maximum range of a QR for typical aircraft targets is intrinsically limited to less than one km, and in most cases to some tens of meters. Detailed computations show that the detection performance of all the proposed QR types are orders of magnitude below the ones of any much simpler and cheaper equivalent "classical" radar set, in particular of the noise radar type. These limitations do not apply to very-short-range microwave applications, such as microwave tomography and radar monitoring of heart and breathing activity of people (where other figures, such as cost, size, weight, and power, shall be taken into account). Moreover, quantum sensing at much higher frequencies (optical and beyond) is not considered here. [ABSTRACT FROM AUTHOR]

In this paper, a novel, cost-efficient 2-by-32 switch matrix for microwave imaging systems is designed and implemented. The switch’s operation was verified, with its performance assessed across different ports through testing and measurements. The microwave switch demonstrated versatility as it is able to handle up to 32 antennas, functioning as both transmitters and receivers. The switch is designed as part of a microwave imaging system for biomedical applications operating at 1 GHz. Nevertheless, the switch’s operability was beyond the required 1 GHz range, suggesting its potential use for a broad range of applications. Further, the microwave switch operation is automated, enabling computerized control with different modes of operation. The compact and portable design, automated control, and compatibility features make the microwave switch matrix a valuable component for microwave imaging applications.

This paper deals with the imaging problem from data collected by means of a microwave photonics-based distributed radar network. The radar network is leveraged on a centralized architecture, which is composed of one central unit (CU) and two transmitting and receiving dual-band remote radar peripherals (RPs), it is capable of collecting monostatic and multistatic phase-coherent data. The imaging is herein formulated as a linear inverse scattering problem and solved in a regularized way through the truncated singular value decomposition inversion scheme. Specifically, two different imaging schemes based on an incoherent fusion of the tomographic images or a fully coherent data processing are herein developed and compared. Experimental tests carried out in a port scenario for imaging both a stationary and a moving target are reported to validate the imaging approach.

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

In clinical settings, computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) are commonly employed in brain imaging to assist clinicians in determining the type of stroke in patients. However, these modalities are associated with potential hazards or limitations. In contrast, microwave imaging emerges as a promising technique, offering advantages such as non-ionizing radiation, low cost, lightweight, and portability. The primary challenges faced by microwave tomography include the severe ill-posedness of the electromagnetic inverse scattering problem and the time-consuming nature and unsatisfactory resolution of iterative quantitative algorithms. This paper proposes a learning electric field enhancement imaging method (LEFEIM) to achieve quantitative brain imaging based on a microwave tomography system. LEFEIM comprises two cascaded networks. The first, based on a convolutional neural network, utilizes the electric field from the receiving antenna to predict the electric field distribution within the imaging domain. The second network employs the electric field distribution as input to learn the dielectric constant distribution, thereby realizing quantitative brain imaging. Compared to the Born Iterative Method (BIM), LEFEIM significantly improves imaging time, while enhancing imaging quality and goodness-of-fit to a certain extent. Simultaneously, LEFEIM exhibits anti-noise capabilities.

The availability of multichannel ground-penetrating radar systems capable of gathering multiview, multistatic, multifrequency data provides novel chances to improve subsurface imaging results. However, customized data processing techniques and smart choices of the measurement setup are needed to find a trade-off between image quality and acquisition time. In this paper, we adopt a Born Approximation-based full 3D approach, which can manage multiview-multistatic, multifrequency data and faces the imaging as a linear inverse scattering problem. The inverse problem is solved by exploiting the truncated singular value decomposition as a regularization scheme. The paper presents a theoretical study aimed at assessing how the reconstruction capabilities of the imaging approach depend on the adopted measurement configuration. In detail, the performance achievable in the standard case of multimonostatic, multifrequency data is compared with that provided by a multiview-multistatic, multifrequency configuration, where the data are gathered by considering a progressively increasing number of transmitting antennas. The comparison of the achievable imaging performance is carried out by exploiting the spectral content and the point spread function, which are general tools to foresee the achievable reconstruction capabilities. Reconstruction results related to a numerical experiment based on full-wave data are also provided.

This work, written for engineers or managers with no special knowledge of quantum mechanics, nor deep experience in radar, aims to help the scientific, industrial, and governmental community to better understand the basic limitations of proposed microwave quantum radar (QR) technologies and systems. Detection and ranging capabilities for QR are critically discussed and a comparison with its closest classical radar (CR), i.e., the noise radar (NR), is presented. In particular, it is investigated whether a future fielded and operating QR system might really outperform an “equivalent” classical radar, or not. The main result of this work, coherently with the recent literature, is that the maximum range of a QR for typical aircraft targets is intrinsically limited to less than one km, and in most cases to some tens of meters. Detailed computations show that the detection performance of all the proposed QR types are orders of magnitude below the ones of any much simpler and cheaper equivalent “classical” radar set, in particular of the noise radar type. These limitations do not apply to very-short-range microwave applications, such as microwave tomography and radar monitoring of heart and breathing activity of people (where other figures, such as cost, size, weight, and power, shall be taken into account). Moreover, quantum sensing at much higher frequencies (optical and beyond) is not considered here.

Microwave imaging is one of the rapidly developing frontier disciplines in the field of modern medical imaging. The development of microwave imaging algorithms for reconstructing stroke images is discussed in this paper. Compared with traditional stroke detection and diagnosis techniques, microwave imaging has the advantages of low price and no ionizing radiation hazards. The research hotspots of microwave imaging algorithms in the field of stroke are mainly reflected in the design and improvement of microwave tomography, radar imaging, and deep learning imaging. However, the current research lacks the analysis and combing of microwave imaging algorithms. In this paper, the development of common microwave imaging algorithms is reviewed. The concept, research status, current research hotspots and difficulties, and future development trends of microwave imaging algorithms are systematically expounded. The microwave antenna is used to collect scattered signals, and a series of microwave imaging algorithms are used to reconstruct the stroke image. The classification diagram and flow chart of the algorithms are shown in this Figure. (The classification diagram and flow chart are based on the microwave imaging algorithms.) [ABSTRACT FROM AUTHOR]

This letter presents an imaging approach for multiple input–multiple output ground penetrating radar (MIMO GPR) systems working in down-looking contactless mode. The approach exploits a linear approximation of the scattering phenomenon and is based on a ray-based propagation model, which takes into account the presence of the air–soil interface. Accordingly, the interface reflection point (IRP) concept is extended to the case of MIMO GPR. The proposed approach performs the imaging in the 2-D scalar case and applies the truncated singular value decomposition regularization scheme to perform the inversion. The effectiveness of the approach is assessed by processing synthetic and real data. The real data are referred to the lunar soil and have been collected by means of the Lunar Regolith Penetrating Radar (LRPR), installed on the Chang’E-5 (CE-5) lander. [ABSTRACT FROM AUTHOR]

In this paper, a deep learning technique for tumor detection in a microwave tomography framework is proposed. Providing an easy and effective imaging technique for breast cancer detection is one of the main focuses for biomedical researchers. Recently, microwave tomography gained a great attention due to its ability to reconstruct the electric properties maps of the inner breast tissues, exploiting nonionizing radiations. A major drawback of tomographic approaches is related to the inversion algorithms, since the problem at hand is nonlinear and ill-posed. In recent decades, numerous studies focused on image reconstruction techniques, in same cases exploiting deep learning. In this study, deep learning is exploited to provide information about the presence of tumors based on tomographic measures. The proposed approach has been tested with a simulated database showing interesting performances, in particular for scenarios where the tumor mass is particularly small. In these cases, conventional reconstruction techniques fail in identifying the presence of suspicious tissues, while our approach correctly identifies these profiles as potentially pathological. Therefore, the proposed method can be exploited for early diagnosis purposes, where the mass to be detected can be particularly small. [ABSTRACT FROM AUTHOR]

Abstract Background Osteoporosis is the major cause of bone weakness and fragility in more than 10 million people in the United States. This disease causes bone fractures in the hip or spine, which result in increasing the risk of disabilities or even death. The current gold standard in osteoporosis diagnostics, X-ray, although reliable, it uses ionizing radiations that makes it unfeasible for early and continuous monitoring applications. Recently, microwave tomography (MWT) has been emerging as a biomedical imaging modality that utilizes non-ionizing electromagnetic signals to screen bones’ electrical properties. These properties are highly correlated to bones’ density, which makes MWT to be an effective and safe alternative for frequent testing in osteoporosis diagnostics. Results Both the conventional and wearable simulated systems were successful in localizing the tibia and fibula bones in the enhanced MWT images. Furthermore, structure extraction of the leg’s model from the blind MWT images had a minimal error compared to the original one (L2-norm: 15.60%). Under five sequentially incremental bone volume fraction (BVF) scenarios simulating bones’ treatment procedure, bones were detected successfully and their densities were found to be inversely proportional to the real part of the relative permittivity values. Conclusions This study paves the way towards implementing a safe and user-friendly MWT system that can be wearable to monitor bone degradation or treatment for osteoporosis cases. Methods An anatomically realistic finite-element (FE) model representing the human leg was initially generated and filled with corresponding tissues’ (skin, fat, muscles, and bones) dielectric properties. Then, numerically, the forward and inverse MWT problems were solved within the framework of the finite-element method-contrast source inversion algorithm (FEM-CSI). Furthermore, image reconstruction enhancements were investigated by utilizing prior information about different tissues as an inhomogeneous background as well as by adjusting the imaging domain and antennas locations based on the prior structural information. In addition, the utilization of a medically approved matching medium that can be used in wearable applications, namely an ultrasound gel, was suggested. Additionally, an approach based on k-means clustering was developed to extract the prior structural information from blind reconstructions. Finally, the enhanced images were used to monitor variations in BVF. Graphical Abstract

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]

(1) Background: In this paper, an artificial neural network approach for effective and real-time quantitative microwave breast imaging is proposed. It proposes some numerical analyses for the optimization of the network architecture and the improvement of recovery performance and processing time in the microwave breast imaging framework, which represents a fundamental preliminary step for future diagnostic applications. (2) Methods: The methodological analysis of the proposed approach is based on two main aspects: firstly, the definition and generation of a proper database adopted for the training of the neural networks and, secondly, the design and analysis of different neural network architectures. (3) Results: The methodology was tested in noisy numerical scenarios with different values of SNR showing good robustness against noise. The results seem very promising in comparison with conventional nonlinear inverse scattering approaches from a qualitative as well as a quantitative point of view. (4) Conclusion: The use of quantitative microwave imaging and neural networks can represent a valid alternative to (or completion of) modern conventional medical imaging techniques since it is cheaper, safer, fast, and quantitative, thus suitable to assist medical decisions. [ABSTRACT FROM AUTHOR]

Electromagnetic Imaging (EMI) systems use a large number of co-resident antennas usually connected to a Vector Network Analyzer via a switch. A numerical model is used to model the physical electromagnetic problem and an inversion algorithm is used to invert the collected data to produce an image of the target. However, before the computer model can be used, the raw VNA measurements must be calibrated to bridge the computational model and the true system physics. Traditional calibration approaches usually require two data sets: a data set measured from a known target for calibration purposes and a data set measured for the unknown target. In this paper, we introduce a new calibration method to calibrate and image using a single S-parameter measurement of the unknown target only. We apply this method to EMI inside of grain bins. This proposed calibration workflow: (1) estimates the bulk contents of the grain bin using a parametric inversion and (2) uses the bulk results to subsequently estimate per-channel calibration coefficients for both the transmit and receive paths to each antenna. The novel calibration procedure is demonstrated via both synthetic and experimental results, showing that single-data set calibration can provide similar quality results as traditional two-data set calibration.

Over the last two decades, metamaterials (MMs) and metasurfaces (MTSs) have been used to fabricate innovative antenna designs, offering cost-effective solutions compared to conventional radiating systems. This paper investigates the feasibility of combining MM design concepts and imaging techniques to create innovative microwave imaging systems. In particular, we present an experimental study with the aim of enhancing microwave imaging for haemorrhagic stroke detection using a new MTS design. First, we show the improvement in performance for a stand-alone MTS-loaded antenna, by studying its operating characteristics in the near and far fields. Then, we assess the performance of the MTS on the reconstruction results from simulations and measurements on two tissue-mimicking gel-based brain phantoms with a cylindrical target representing the bleeding in haemorrhagic stroke. The brain phantom was immersed inside an imaging tank filled with 90% glycerol matching liquid. To perform the image reconstructions, we used both a Huygens based radar algorithm and a DBIM-TwIST tomography algorithm. Our simulation and measurement results indicate that the proposed MTS design improves target localization and decreases image artefacts for the tomographic algorithm and enables target’s detection through our radar technique, paving the way for a hybrid microwave imaging prototype with MTS enhanced antennas.

We present an experimental validation of the distorted Born iterative method with the two-step iterative shrinkage thresholding (DBIM-TwIST) algorithm for the problem of brain stroke detection and differentiation, using an anatomically accurate, multi-layer head phantom. To this end, we have developed a gelatine-based, anatomically complex head phantom which mimics various brain tissues and also includes a target mimicking hemorrhagic or ischemic stroke. We simulated the model and setup using CST Microwave Studio and then used our experimental imaging setup to collect numerical and measured data, respectively. We then used our DBIM-TwIST algorithm to reconstruct the dielectric properties of the imaging domain for both simulated and measured data. Results from our CST simulations showed that we are able to locate and reconstruct the permittivity of different stroke targets using an approximate initial guess. Our experimental results demonstrated the potential and challenges for successful detection and differentiation of the stroke targets.

In this paper, a deep learning technique for tumor detection in a microwave tomography framework is proposed. Providing an easy and effective imaging technique for breast cancer detection is one of the main focuses for biomedical researchers. Recently, microwave tomography gained a great attention due to its ability to reconstruct the electric properties maps of the inner breast tissues, exploiting nonionizing radiations. A major drawback of tomographic approaches is related to the inversion algorithms, since the problem at hand is nonlinear and ill-posed. In recent decades, numerous studies focused on image reconstruction techniques, in same cases exploiting deep learning. In this study, deep learning is exploited to provide information about the presence of tumors based on tomographic measures. The proposed approach has been tested with a simulated database showing interesting performances, in particular for scenarios where the tumor mass is particularly small. In these cases, conventional reconstruction techniques fail in identifying the presence of suspicious tissues, while our approach correctly identifies these profiles as potentially pathological. Therefore, the proposed method can be exploited for early diagnosis purposes, where the mass to be detected can be particularly small.

When using the statistical inversion framework in microwave tomography (MWT), generally, the real and imaginary parts of the unknown dielectric constant are treated as uncorrelated and independent random variables. Thereby, in the maximum a posteriori estimates, the two recovered variables may show different structural changes inside the imaging domain. In this work, a correlated sample-based prior model is presented to incorporate the correlation of the real part with the imaginary part of the dielectric constant in the statistical inversion framework. The method is used to estimate the inhomogeneous moisture distribution (as dielectric constant) in a large cross section of polymer foam. The targeted application of MWT is in industrial drying to derive intelligent control methods based on tomographic inputs for selective heating purposes. One of the features of the proposed method shows how to integrate lab-based dielectric characterization, often available in MWT application cases, in the prior modeling. The method is validated with numerical and experimental MWT data for the considered moisture distributions. [ABSTRACT FROM AUTHOR]

Microwave Tomography Imaging System (MwTIS) is an emerging tool for medical diagnosis in the non-invasive screening process. This paper addresses the ill-condition problem by proposing two new schemes incorporated into the DBIM image reconstructed algorithm for high frequencies in MwTIS. The first scheme is to propose an optimal step frequency using the degree of ill-posedness value for reducing the frequency diversity problem. The second scheme is to propose Krylov Subspace-based regularization method called Flexible Preconditioned Conjugate Gradient Least Square (FPCGLS) method to resolve the ill-condition problem. The iteratively updated preconditioner matrix in the proposed FP-CGLS method reduces the number of iterations and it is stable in high-level Gaussian noise. The efficiency of the proposed FP-CGLS method is validated by imposing Gaussian noise up to 30% in scattered breast phantom in the multifrequency range of 2 GHz -3 GHz It achieves an enhanced reconstructed image at 12 iterations with a relative error of 0.1802 for 20% of Gaussian noise and for the same scheme the existing CGLS method has a 0.4480 relative error at the 77 iterations. Further, the FP-CGLS along with the DBIM method produces a reconstructed image with the accuracy of 0.8760 in four DBIM iterations. [ABSTRACT FROM AUTHOR]

Breast cancer is the most commonly diagnosed cancer type and is the leading cause of cancer-related death among females worldwide. Breast screening and early detection are currently the most successful approaches for the management and treatment of this disease. Several imaging modalities are currently utilized for detecting breast cancer, of which microwave imaging (MWI) is gaining quite a lot of attention as a promising diagnostic tool for early breast cancer detection. MWI is a noninvasive, relatively inexpensive, fast, convenient, and safe screening tool. The purpose of this paper is to provide an up-to-date survey of the principles, developments, and current research status of MWI for breast cancer detection. This paper is structured into two sections; the first is an overview of current MWI techniques used for detecting breast cancer, followed by an explanation of the working principle behind MWI and its various types, namely, microwave tomography and radar-based imaging. In the second section, a review of the initial experiments along with more recent studies on the use of MWI for breast cancer detection is presented. Furthermore, the paper summarizes the challenges facing MWI as a breast cancer detection tool and provides future research directions. On the whole, MWI has proven its potential as a screening tool for breast cancer detection, both as a standalone or complementary technique. However, there are a few challenges that need to be addressed to unlock the full potential of this imaging modality and translate it to clinical settings. [ABSTRACT FROM AUTHOR]

The contrast source inversion (CSI) algorithm is one of the primary techniques used for solution of non-linear inverse problems in microwave tomography. In this paper, we describe a modification of the CSI method adapted to imaging of the 2-D objects in the presence of the focusing media under TM-polarization. The focusing media is presented in the form of the Veselago lens. The data domain and imaging domain are properly positioned with respect to the location of the lens. Specifically, the sensors are located at the focal points of the lens with respect to the location of the individual pixels discretizing the contrast source. Such positioning of the source and observation locations in the presence of the lens, eliminates rank deficiency in the formulation of the inverse problem and results in significant improvements to both convergence speed of underlying conjugate gradient iterations and the accuracy of the image reconstruction in the CSI method.

Ground-penetrating radar (GPR) technology for underground exploration consists of the transmission of an electromagnetic signal in the ground for sensing the presence of buried objects. While monostatic or bistatic configurations are usually adopted, a limited number of multistatic GPR systems have been proposed in the scientific literature. In this article, we investigate the recovery performance of a specific and unconventional contactless multistatic GPR system, designed at the Georgia Institute of Technology for the subsurface imaging of antitank and antipersonnel plastic mines. In particular, for the first time, tomographic approaches are tested against this experimental multistatic GPR system, while most GPR processing in the scientific literature processes multimonostatic experimental data sets. First, by mimicking the system at hand, an accurate theoretical as well as numerical analysis is performed in order to estimate the data information content and the performance achievable. Two different tomographic linear approaches are adopted, i.e., the linear sampling method and the Born approximation (BA) method, this latter enhanced by means of the compressive sensing (CS) theoretical framework. Then, the experimental data provided by the Georgia Institute of Technology are processed by means of a multifrequency CS- and BA-based method, thus generating very accurate 3D maps of the investigated underground scenario.

One of the most challenging problems in microwave tomography is the reconstruction of dielectric properties inside parts of the human body. This is a crucial task for medical applications such as early cancer diagnostics and focused microwave thermotherapy. If not enough antennas are used, the amount of microwave scattering measurement data will be insufficient for satisfactory imaging with the conventional approach. This paper suggests a method for overcoming insufficient microwave scattering data using simultaneous reconstruction of the body shape and its interior dielectric properties, given the restriction that the shape must be regular and smooth. This restriction allows for reduced uncertainty in the reconstructed images. We developed a 2.5D electromagnetic solver and an iterative method for estimating body shape in the process of image reconstruction. This approach provides successful imaging even with insufficient scattering data. In addition, the simultaneous reconstruction leads to improved accuracy in both restorations.

Particle drying in fluidized beds is a common and important process in food and pharmaceutical industry. However, the fluidization characteristic is complex due to the moisture content change in the process and it is a challenge to monitor the flow characteristics by conventional measurement approach, likes pressure and thermal probes. In this research, process tomography including capacitance and microwave tomographic systems are used to investigate the flow characteristics in a fluidized bed dryer with high-moisture content particles. Static tests were firstly carried out to investigate the image characteristics based on microwave tomography (MWT) and electrical capacitance tomography (ECT). The imaging ability was compared between MWT and ECT. In the dynamic tests, the drying process for particles with high-moisture content was monitored on-line by MWT and ECT separately. The static test results show that the high calibration for ECT is critical and particles with low moisture content should be used for high calibration of an ECT system to achieve high quality images for the dynamic tests. For the high calibration of ECT, there is an upper limit for the moisture content. With a fixed calibration background, the moisture content of particles should not exceed this upper limit value. MWT basically has no limitation on the moisture content both for high calibration and measurement. Dynamic test results illustrate that MWT is a good option to monitor high-moisture content particles drying in a fluidized bed and it is possible to combine ECT together to identify the gas–solids flow regime transit and to determine the moisture content by MWT in a fluidized bed drying process. [ABSTRACT FROM AUTHOR]

Background: Osteoporosis is the major cause of bone weakness and fragility in more than 10 million people in the United States. This disease causes bone fractures in the hip or spine, which result in increasing the risk of disabilities or even death. The current gold standard in osteoporosis diagnostics, X-ray, although reliable, it uses ionizing radiations that makes it unfeasible for early and continuous monitoring applications. Recently, microwave tomography (MWT) has been emerging as a biomedical imaging modality that utilizes non-ionizing electromagnetic signals to screen bones' electrical properties. These properties are highly correlated to bones' density, which makes MWT to be an effective and safe alternative for frequent testing in osteoporosis diagnostics.Results: Both the conventional and wearable simulated systems were successful in localizing the tibia and fibula bones in the enhanced MWT images. Furthermore, structure extraction of the leg's model from the blind MWT images had a minimal error compared to the original one (L2-norm: 15.60%). Under five sequentially incremental bone volume fraction (BVF) scenarios simulating bones' treatment procedure, bones were detected successfully and their densities were found to be inversely proportional to the real part of the relative permittivity values.Conclusions: This study paves the way towards implementing a safe and user-friendly MWT system that can be wearable to monitor bone degradation or treatment for osteoporosis cases.Methods: An anatomically realistic finite-element (FE) model representing the human leg was initially generated and filled with corresponding tissues' (skin, fat, muscles, and bones) dielectric properties. Then, numerically, the forward and inverse MWT problems were solved within the framework of the finite-element method-contrast source inversion algorithm (FEM-CSI). Furthermore, image reconstruction enhancements were investigated by utilizing prior information about different tissues as an inhomogeneous background as well as by adjusting the imaging domain and antennas locations based on the prior structural information. In addition, the utilization of a medically approved matching medium that can be used in wearable applications, namely an ultrasound gel, was suggested. Additionally, an approach based on k-means clustering was developed to extract the prior structural information from blind reconstructions. Finally, the enhanced images were used to monitor variations in BVF. [ABSTRACT FROM AUTHOR]

(1) Background: In this paper, an artificial neural network approach for effective and real-time quantitative microwave breast imaging is proposed. It proposes some numerical analyses for the optimization of the network architecture and the improvement of recovery performance and processing time in the microwave breast imaging framework, which represents a fundamental preliminary step for future diagnostic applications. (2) Methods: The methodological analysis of the proposed approach is based on two main aspects: firstly, the definition and generation of a proper database adopted for the training of the neural networks and, secondly, the design and analysis of different neural network architectures. (3) Results: The methodology was tested in noisy numerical scenarios with different values of SNR showing good robustness against noise. The results seem very promising in comparison with conventional nonlinear inverse scattering approaches from a qualitative as well as a quantitative point of view. (4) Conclusion: The use of quantitative microwave imaging and neural networks can represent a valid alternative to (or completion of) modern conventional medical imaging techniques since it is cheaper, safer, fast, and quantitative, thus suitable to assist medical decisions.

A deep learning approach is proposed for performing tissue-type classification of tomographic microwave and ultrasound property images of the breast. The approach is based on a convolutional neural network (CNN) utilizing the U-net architecture that also quantifies the uncertainty in the classification of each pixel. Quantitative tomographic reconstructions of dielectric properties (complex-valued permittivity), ultrasonic properties (compressibility and attenuation), as well as their combination, with the corresponding actual tissue-type classification constitute the training set. The CNN learns to map the quantitative property reconstructions to a single tissue-type image. The level of confidence in predicting a tissue-type at each pixel is determined. This uncertainty quantification is diagnostically critical for biomedical applications, especially when attempting to distinguish between cancerous and healthy tissues. The Gauss-Newton Inversion algorithm is used for the quantitative reconstruction of both dielectric and ultrasonic properties. Electromagnetic and ultrasound scattered-field data is obtained from MRI-derived numerical breast phantoms. Several numerical breast phantoms types, from fatty to dense, are considered. The proposed classification and uncertainty quantification approach is shown to outperform a previously studied tissue-type classification method based on a Bayesian approach.

This article describes a fast microwave tomography reconstruction algorithm based on the 2-D discrete dipole approximation (DDA). Synthetic data from a finite-element-based solver and experimental data from a microwave imaging system are used to reconstruct images and to validate the algorithm. The microwave measurement system consists of 16 monopole antennas immersed in a tank filled with lossy coupling liquid and a vector network analyzer. The low-profile antennas and lossy nature of the system make the DDA an ideal forward solver in image reconstructions. The results show that the algorithm can readily reconstruct a 2-D plane of a cylindrical phantom. The proposed forward solver combined with the nodal adjoint method for computing the Jacobian matrix enables the algorithm to reconstruct an image within 6 s. This implementation provides significant time savings and reduced memory requirements and is a dramatic improvement over previous implementations. [ABSTRACT FROM AUTHOR]

This letter proposes a hybrid microwave medical imaging (MMI) approach to reconstruct the inner structure of the human body with improved accuracy on mapping the dielectric permittivities. Specifically, the results of the quantitative and qualitative MMI are employed to act as the a priori information for each other through an iterative process. Hence, the hybrid MMI approach combines the advantages from both the quantitative and qualitative algorithms to visualize simultaneously the inner body structure with clear tissue boundaries and the positions of strong scatterers. This proposed approach is evaluated by considering the medical scenario of bone fracture detection. The resultant image shows that the relative error of the reconstructed permittivity can drop by 33% for the scanned body area when compared to the one generated by the conventional quantitative MMI, whereas the position of a bone fracture can still be precisely indicated. [ABSTRACT FROM AUTHOR]

Electromagnetic inversion systems require that the experimental data be calibrated to the computational inversion model being used. In addition, accurate prior information provided to the inversion algorithm leads to higher-quality images. For some applications of inversion, such as stored grain imaging or geophysical inversion, known (calibration) targets cannot be easily introduced into the imaging region and the ability to determine prior information can be limited. In an attempt to solve the problem of calibrating data from such field-inversion systems, we introduce a work flow where: (1) a simple parametric physical model of the scattering background is obtained via a phaseless (magnitude only data) inversion algorithm that works on phase-corrupted, uncalibrated total-field measurements, and (2) we then use this simple physical model to generate calibration and prior information for subsequent full-data (magnitude and phase) inversion. Using an example of in-bin stored grain imaging, the inverted parameters are the grain angle of repose, grain height, and the average bulk permittivity of the grain. Using uncalibrated total-field data, we show that the proposed work flow obtains the overall structure of the grain in a bin despite the use of this raw data. We then show that the simple physical model can be used as both a calibration data set as well as the prior information about the grain target in a full-data (magnitude and phase) inversion. The use of this phaseless algorithm means we are able to remotely calibrate imaging systems, and obtain critical prior information about the imaging region without introducing a calibration target or physically measuring the imaging region in other ways.

Breast cancer is the most commonly diagnosed cancer type and is the leading cause of cancer-related death among females worldwide. Breast screening and early detection are currently the most successful approaches for the management and treatment of this disease. Several imaging modalities are currently utilized for detecting breast cancer, of which microwave imaging (MWI) is gaining quite a lot of attention as a promising diagnostic tool for early breast cancer detection. MWI is a noninvasive, relatively inexpensive, fast, convenient, and safe screening tool. The purpose of this paper is to provide an up-to-date survey of the principles, developments, and current research status of MWI for breast cancer detection. This paper is structured into two sections; the first is an overview of current MWI techniques used for detecting breast cancer, followed by an explanation of the working principle behind MWI and its various types, namely, microwave tomography and radar-based imaging. In the second section, a review of the initial experiments along with more recent studies on the use of MWI for breast cancer detection is presented. Furthermore, the paper summarizes the challenges facing MWI as a breast cancer detection tool and provides future research directions. On the whole, MWI has proven its potential as a screening tool for breast cancer detection, both as a standalone or complementary technique. However, there are a few challenges that need to be addressed to unlock the full potential of this imaging modality and translate it to clinical settings.

Microwave imaging is one of the emerging noninvasive portable imaging techniques which use non ionized radiations to take the detailed view of biological tissues in the microwave frequency range. In this paper Ultra wideband Vivaldi antenna is designed as a radar technology to view the properties of head tissue at a resonance frequency of 2.2GHz and 3.5 GHz. The proposed Vivaldi structure is simulated with FR-4 substrate (dielectric constant of 4.3, thickness of h=1.6 mm) using CST microwave simulation software (2020 version). This near field imaging arrangement is proposed for identification of blood clot/stroke at an early stage as well as for continuous monitoring. [ABSTRACT FROM AUTHOR]

This letter deals with an imaging strategy able to manage effectively data collected on multiple lines by means of a Mini-Unmanned Aerial Vehicle (M-UAV) radar system operating in Sounder mode. The strategy, named multilines imaging approach (MIA), allows an effective 3-D pseudo-representation of the investigated volume. At the first step, MIA faces the problem of reconstructing 2-D domains (slices) by exploiting data collected on one or more lines. Then, MIA interpolates the 2-D reconstructions to provide the 3-D representation. The imaging of each slice is formulated as a linear inverse scattering problem, which is solved by means of the truncated singular value decomposition (TSVD) regularization scheme. The MIA effectiveness is assessed by processing real data collected at Archeological Park of Paestum and Velia, Paestum, Italy. [ABSTRACT FROM AUTHOR]

The problem of electromagnetic inverse scattering under an inhomogeneous background is analyzed. This paper presents a modified contrast source inversion (CSI) method, named incremental distorted multiplicative regularized contrast source inversion (ID-MRCSI) to solve the inverse problems in two dimensions. By redefining data function, state function, and cost functional, the differences between the real target and the known arbitrary background can be reconstructed. Numerical results show that the ID-MRCSI algorithm is suitable for the applications of continuous repeated measurements to a specific target. It has the prospect of being utilized in the wearable imaging equipment to recover the information of changes dynamically. [ABSTRACT FROM AUTHOR]

The aim of this study is to use a multilayer perceptron (MLP) artificial neural network (ANN) for phaseless imaging the human heel (modeled as a bilayer dielectric media: bone and surrounding tissue) and the calcaneus cross-section size and location using a two-dimensional (2D) microwave tomographic array. Computer simulations were performed over 2D dielectric maps inspired by computed tomography (CT) images of human heels for training and testing the MLP. A morphometric analysis was performed to account for the scatterer shape influence on the results. A robustness analysis was also conducted in order to study the MLP performance in noisy conditions. The standard deviations of the relative percentage errors on estimating the dielectric properties of the calcaneus bone were relatively high. Regarding the calcaneus surrounding tissue, the dielectric parameters estimations are better, with relative percentage error standard deviations up to ≈ 15%. The location and size of the calcaneus are always properly estimated with absolute error standard deviations up to ≈ 3 mm. Microwave tomography of the calcaneus using phaseless data. Simulations were inspired in Computed Tomography images from real heels (above). Inverse problem was solved using Multilayer Perceptron Artificial Neural Network (below). [ABSTRACT FROM AUTHOR]