Three-dimensional Microwave Imaging Research Articles

Three-Dimensional Time Reversal Microwave Imaging of Brain Stroke

Three-Dimensional Time Reversal Microwave Imaging of Brain Stroke

Machine-Learning-Based Inversion Scheme for Super-Resolution Three-Dimensional Microwave Human Brain Imaging

To realize efficient three-dimensional (3-D) super-resolution whole brain microwave imaging, a new machine- learning-based inversion method with a resolution enhancement technique is proposed. It consists of three parts: a parallel semiconnected backpropagation neural network (SJ-BPNN) scheme, a U-Net scheme, and a modified Akima piecewise cubic Hermite interpolation (MAPCHI) scheme. The parallel SJ-BPNN scheme is first employed to map the measured scattered field data to the preliminary electrical properties’ distribution of human brain. Then, U-Net is used to improve the quality of these preliminary reconstruction results. Finally, the MAPCHI scheme is adopted to greatly improve the resolution of reconstruction results with a very low computational cost. Numerical examples of a normal human brain and a human brain with abnormal scatterers show that the proposed method can achieve accurate high-resolution human brain imaging with 1024 × 1024 × 1024 voxels with a very low computational cost.

A 3-D Full Convolution Electromagnetic Reconstruction Neural Network (3-D FCERNN) for Fast Super-Resolution Electromagnetic Inversion of Human Brain.

Three-dimensional (3-D) super-resolution microwave imaging of human brain is a typical electromagnetic (EM) inverse scattering problem with high contrast. It is a challenge for the traditional schemes based on deterministic or stochastic inversion methods to obtain high contrast and high resolution, and they require huge computational time. In this work, a dual-module 3-D EM inversion scheme based on deep neural network is proposed. The proposed scheme can solve the inverse scattering problems with high contrast and super-resolution in real time and reduce a huge computational cost. In the EM inversion module, a 3-D full convolution EM reconstruction neural network (3-D FCERNN) is proposed to nonlinearly map the measured scattered field to a preliminary image of 3-D electrical parameter distribution of the human brain. The proposed 3-D FCERNN is completely composed of convolution layers, which can greatly save training cost and improve model generalization compared with fully connected networks. Then, the image enhancement module employs a U-Net to further improve the imaging quality from the results of 3-D FCERNN. In addition, a dataset generation strategy based on the human brain features is proposed, which can solve the difficulty of human brain dataset collection and high training cost. The proposed scheme has been confirmed to be effective and accurate in reconstructing the distribution of 3-D super-resolution electrical parameters distribution of human brain through noise-free and noisy examples, while the traditional EM inversion method is difficult to converge in the case of high contrast and strong scatterers. Compared with our previous work, the training of FCERNN is faster and can significantly decrease computational resources.

A Three-Dimensional Microwave Sparse Imaging Approach Using Higher-Order Basis Functions

This paper presents a novel method for three-dimensional microwave imaging based on sparse processing. To enforce the sparsity of the unknown function, we take advantage of the fact that arbitrary three-dimensional electromagnetic fields can be decomposed into two components with respect to the radial direction: one with transverse-magnetic polarization and the other with transverse-electric polarization. Each component can be further expressed as a sum of spherical harmonics, which provide the dictionary exploited by the sparse processing algorithm. Our measurement model relates the data and the parameters of the spherical harmonics’ sources, which are uniformly distributed on a grid sampling the imaging domain. By relying on the theory of degrees of freedom of electromagnetic fields, it can be shown that only a few harmonics are sufficient to accurately represent the measured scattered field from objects whose diameter is of the order of the wavelength, thus allowing reducing the dimension of the adopted dictionary. We analyze several imaging scenarios to assess the algorithm’s performance, including different object shapes, sensor orientations, and signal-to-noise ratios. Moreover, we compare the obtained results with other state-of-the-art linear imaging techniques. Notably, thanks to the adopted dictionary, the proposed algorithm can yield accurate images of both convex and concave objects.

Hybrid Microwave Imaging of 3-D Objects Using LSM and BIM Aided by a CNN U-Net

This paper presents an efficient and accurate three-dimensional (3-D) quantitative hybrid microwave imaging method. The linear sampling method (LSM) is first carried out to quickly find the approximate shapes and locations of the unknown objects in the imaging domain based on the scattered field data recorded by receivers which are placed in the far-field zone and wrap the domain. Then the full-wave inversion (FWI) is implemented in a downsized domain which tightly encloses the unknown objects instead of in the whole domain through the Born iterative method (BIM) to quantitatively retrieve the dielectric model parameters of the objects. Because the LSM fails to obtain the sufficiently accurate shapes of the unknown objects, a trained 3-D CNN U-Net is inserted between the LSM imager and the BIM solver to further refine the obtained shapes of LSM, which is expected to aid the following FWI. The proposed hybrid method is validated via the quantitative imaging of both inhomogeneous isotropic scatterers and multiple homogeneous anisotropic scatterers. It is shown that the hybrid method can achieve both higher reconstruction accuracy and lower computational cost compared with the direct BIM inversion. Meanwhile, its antinoise ability is also tested.

Derivation and Validation of Three-Dimensional Microwave Imaging Using a W-Band MIMO Radar

Derivation and Validation of Three-Dimensional Microwave Imaging Using a W-Band MIMO Radar

Fast and Cost-Effective Three-Dimensional Microwave Imaging Using a Cylindrical Setup

In this paper, we propose a fast and low-cost cylindrical microwave imaging system based on the use of arrays of transmitter and receiver antennas and a customized low-cost data acquisition circuit using off-the-shelf components. The complex-valued scattered data captured with the proposed system is processed using near-field holographic image reconstruction. To enhance this technique, standardized minimum norm (SMN) approach is employed to solve the relevant systems of equations. The performance of the proposed imaging technique and the data acquisition system is demonstrated via simulations and experiments.

Three-Dimensional Microwave Imaging: Fast and Accurate Computations with Block Resolution Algorithms.

This paper considers the microwave imaging reconstruction problem, based on additive penalization and gradient-based optimization. Each evaluation of the cost function and of its gradient requires the resolution of as many high-dimensional linear systems as the number of incident fields, which represents a large amount of computations. Since all such systems involve the same matrix, we propose a block inversion strategy, based on the block-biconjugate gradient stabilized (BiCGStab) algorithm, with efficient implementations specific to the microwave imaging context. Numerical experiments performed on synthetic data and on real measurements show that savings in computing time can reach a factor of two compared to the standard, sequential, BiCGStab implementation. Improvements brought by the block approach are even more important for the most difficult reconstruction problems, that is, with high-frequency illuminations and/or highly contrasted objects. The proposed reconstruction strategy is shown to achieve satisfactory estimates for objects of the Fresnel database, even on the most contrasted ones.

A Fast Back-Projection Approach to Diffraction Tomography for Near-Field Microwave Imaging

A novel fast back-projection (FBP) approach is presented as a more robust alternative to conventional diffraction tomography (DT) for near-field three-dimensional (3-D) microwave imaging. Like DT, the FBP algorithm utilizes the spectral Green's function and stationary phase solution in the forward model, enabling the implementation of the fast Fourier transform to achieve real-time image generation with the same efficiency as DT. However, if the Green's function or the antenna gain pattern has any nulls within the imaging volume, the DT can have singularities that give rise to unbalanced images. The FBP not only naturally avoids the singularity problem and obtains balanced images through the point spread function, but is also much less sensitive to noise due to the matched-filter property of back-projection. 3-D simulations and measurement of a monostatic near-field free-space imaging scenario are presented to demonstrate the efficiency of the proposed FBP method in comparison with DT, and the improved uniformity and noise resistance of the image.

A Low-Cost and Compact Three-Dimensional Microwave Holographic Imaging System

With the significant growth in the use of non-metallic composite materials, the demands for new and robust non-destructive testing methodologies is high. Microwave imaging has attracted a lot of attention recently for such applications. This is in addition to the biomedical imaging applications of microwave that are also being pursued actively. Among these efforts, in this paper, we propose a compact and cost-effective three-dimensional microwave imaging system based on a fast and robust holographic technique. For this purpose, we employ narrow-band microwave data, instead of wideband data used in previous three-dimensional cylindrical holographic imaging systems. Three-dimensional imaging is accomplished by using an array of receiver antennas surrounding the object and scanning that along with a transmitter antenna over a cylindrical aperture. To achieve low cost and compact size, we employ off-the-shelf components to build a data acquisition system replacing the costly and bulky vector network analyzers. The simulation and experimental results demonstrate the satisfactory performance of the proposed imaging system. We also show the effect of number of frequencies and size of the objects on the quality of reconstructed images.

Microwave Imaging of 3D Dielectric Structures by Means of a Newton-CG Method in lp Spaces

An increasing number of practical applications of three-dimensional microwave imaging require accurate and efficient inversion techniques. In this context, a full-wave 3D inverse-scattering method, aimed at characterizing dielectric targets, is described in this paper. In particular, the inversion approach has a Newton-based structure, in which the internal linear solver is a conjugate gradient-like algorithm in lp spaces. The presented results, which include the inversion of both numerical and experimental scattered-field data obtained in the presence of homogeneous and inhomogeneous targets, validate the reconstruction capabilities of the proposed technique.

Full-Vectorial 3D Microwave Imaging of Sparse Scatterers through a Multi-Task Bayesian Compressive Sensing Approach.

In this paper, the full-vectorial three-dimensional (3D) microwave imaging (MI) of sparse scatterers is dealt with. Towards this end, the inverse scattering (IS) problem is formulated within the contrast source inversion (CSI) framework and it is aimed at retrieving the sparsest and most probable distribution of the contrast source within the imaged volume. A customized multi-task Bayesian compressive sensing (MT-BCS) method is used to yield regularized solutions of the 3D-IS problem with a remarkable computational efficiency. Selected numerical results on representative benchmarks are presented and discussed to assess the effectiveness and the reliability of the proposed MT-BCS strategy in comparison with other competitive state-of-the-art approaches, as well.

Three-Dimensional Microwave Imaging in $L^{p}$ Banach Spaces: Numerical and Experimental Results

In this paper, a microwave imaging method able to inspect three-dimensional configurations is proposed. The approach is developed in $L^{p}$ Banach spaces, and allows to suitably face the ill-posedness of the considered full vector inverse scattering problem. The assumed nonconventional spaces require several modifications in the original inexact-Newton scheme, which give rise to a significant improvement in reducing the oversmoothing effects in the reconstructed distributions of the dielectric properties of the targets. The proposed approach is discussed in details and validated by using both numerical simulations and real data.

Sampling Design of Synthetic Volume Arrays for Three-Dimensional Microwave Imaging

In this paper, sampling design of three-dimensional (3-D) synthetic array (i.e., synthetic volume array) for microwave imaging is considered. Generally, the spatial sampling criteria for one- or two-dimensional arrays can be determined based on some narrowband/ultrawideband array theories. However, for 3-D arrays, where antennas are located in a volume instead of over a surface, these existing array theories are no longer straightforwardly applicable. To address the spatial sampling problem of 3-D arrays, we formulate it as a sensor/observation selection problem in this paper. Although some selection approaches exist and are conveniently applicable to small-scale problems, they are either less efficient or provide less optimal results for selection problems with data dimensions of hundreds or even thousands which is typical for microwave imaging. To get the (near-) optimal spatial sampling scheme for 3-D arrays, a greedy algorithm named clustered maximal projection on minimal eigenspace (CMPME) is proposed to select the most informative sampling positions based on some optimality criteria. This algorithm attempts to select the fewest sampling positions by considering an error threshold for the estimated images. Moreover, it has higher computational efficiency compared to the existing approaches. Finally, its effectiveness and selection performances are demonstrated through some imaging examples.

Three-Dimensional Holographic Imaging Using Single Frequency Microwave Data

Three-dimensional (3D) microwave and millimeter wave imaging techniques based on the holographic principles have been successfully employed in several applications such as security screening, body shape measurement for the apparel industry, underground imaging, and wall imaging. The previously proposed 3D holographic imaging techniques require the acquisition of wideband data over rectangular or cylindrical apertures. Requirement for wideband data imposes limitations on the hardware (in particular at very high or very low frequencies). It may also lead to errors in the produced images if the media is dispersive (e.g., in biomedical imaging) and not modeled properly in the image reconstruction process. To address these limitations, here, we propose a technique to perform 3D imaging with single frequency data. Instead of collecting data at multiple frequencies, we acquire the backscattered fields with an array of resonant antennas. We demonstrate the possibility of 3D imaging with the proposed setup and perform a comprehensive study of the capabilities and limitations of the technique via simulations. To perform a realistic study, the simulation data is contaminated by noise.

Three-Dimensional Microwave Imaging for Concealed Weapon Detection Using Range Stacking Technique

Three-dimensional (3D) microwave imaging has been proven to be well suited for concealed weapon detection application. For the 3D image reconstruction under two-dimensional (2D) planar aperture condition, most of current imaging algorithms focus on decomposing the 3D free space Green function by exploiting the stationary phase and, consequently, the accuracy of the final imagery is obtained at a sacrifice of computational complexity due to the need of interpolation. In this paper, from an alternative viewpoint, we propose a novel interpolation-free imaging algorithm based on wavefront reconstruction theory. The algorithm is an extension of the 2D range stacking algorithm (RSA) with the advantages of low computational cost and high precision. The algorithm uses different reference signal spectrums at different range bins and then forms the target functions at desired range bin by a concise coherent summation. Several practical issues such as the propagation loss compensation, wavefront reconstruction, and aliasing mitigating are also considered. The sampling criterion and the achievable resolutions for the proposed algorithm are also derived. Finally, the proposed method is validated through extensive computer simulations and real-field experiments. The results show that accurate 3D image can be generated at a very high speed by utilizing the proposed algorithm.

Multi-resolution subspace-based optimization method for solving three-dimensional inverse scattering problems.

An innovative methodology is proposed to solve quantitative three-dimensional microwave imaging problems formulated within the contrast source framework. The introduced technique is based on the combination of an efficient iterative multiscaling strategy aimed at mitigating local minimum issue arising in inverse scattering problems, and a local search algorithm based on the subspace-based optimization method (SOM) devoted to effectively retrieving both the "deterministic" and the "ambiguous" parts of the unknown contrast currents. To achieve this goal, a nested iteration process is adopted in which the outer loop iteratively refines the region of interest (ROI) where the scatterers are detected, while the inner loop retrieves the dielectric properties of the scatterers within the ROIs. Selected numerical examples are also given to show the validity and robustness of the proposed algorithm in comparison with state-of-the-art techniques.

Three-Dimensional Sparse Turntable Microwave Imaging Based on Compressive Sensing

In this letter, we propose a fast reconstruction algorithm for 3-D turntable microwave imaging from sparse measurements. A conventional Fourier-transform-based 3-D microwave imaging method collects data over densely azimuth-elevation samples and needs a large amount of data storage and long collection time. To reduce the cost of data acquisition, the proposed method exploits the sparsity in the image domain to achieve 3-D microwave imaging by utilizing sparse measurements. For this aim, the signal model is first represented as a tensor array, and then, a novel sparse reconstruction algorithm called 3-D-SL0 is applied to recover the 3-D scattering reflectivity, i.e., a 3-D image. Simulation results are finally shown to investigate the validity of the proposed method.

Three-Dimensional Near-Field Microwave Imaging Using Hybrid Linear Sampling and Level Set Methods in a Medium With Compact Support

Three-Dimensional Near-Field Microwave Imaging Using Hybrid Linear Sampling and Level Set Methods in a Medium With Compact Support

Weakly convex discontinuity adaptive regularization for 3D quantitative microwave tomography

We present an analysis of weakly convex discontinuity adaptive (WCDA) models for regularizing three-dimensional (3D) quantitative microwave imaging. In particular, we are concerned with complex permittivity reconstructions from sparse measurements such that the reconstruction process is significantly accelerated. When dealing with such a highly underdetermined problem, it is crucial to employ regularization, relying in this case on prior knowledge about the structural properties of the underlying permittivity profile: we consider piecewise homogeneous objects. We present a numerical study on the choice of the potential function parameter for the Huber function and for two selected WCDA functions, one of which (the Leclerc–Cauchy-Lorentzian function) is designed to be more edge-preserving than the other (the Leclerc–Huber function). We evaluate the effect of reducing the number of (simulated) scattered field data on the reconstruction quality. Furthermore, reconstructions from subsampled single-frequency experimental data from the 3D Fresnel database illustrate the effectiveness of WCDA regularization.