A Reconstruction Procedure for Microwave Nondestructive Evaluation based on a Numerically Computed Green's Function

This paper describes a new microwave diagnostic tool for nondestructive evaluation. The approach, developed in the spatial domain, is based on the numerical computation of the inhomogeneous Green’s function in order to fully exploit all the available a-priori information of the domain under test. The heavy reduction of the computational complexity of the proposed procedure (with respect to standard procedures based on the free-space Green’s function) is also achieved by means of a customized hybrid-coded genetic algorithm. In order to assess the effectiveness of the method, the results of several simulations are presented and discussed

Characterization of an Implicitly Resistively-Loaded Monopole Antenna in Lossy Liquid Media

Abstract Microwave tomographic imaging of the breast for cancer detection is a topic of considerable interest because of the potential to exploit the apparent high-dielectric property contrast between normal and malignant tissue. An important component in the realization of an imaging system is the antenna array to be used for signal transmission/detection. The monopole antenna has proven to be useful in our implementation because it can be easily and accurately modeled and can be positioned in close proximity to the imaging target with high-element density when configured in an imaging array. Its frequency response is broadened considerably when radiating in the liquid medium that is used to couple the signals into the breast making it suitable for broadband spectral imaging. However, at higher frequencies, the beam patterns steer further away from the desired horizontal plane and can cause unwanted multipath contributions when located in close proximity to the liquid/air interface. In this paper, we have studied the behavior of these antennas and devised strategies for their effective use at higher frequencies, especially when positioned near the surface of the coupling fluid which is used. The results show that frequencies in excess of 2 GHz can be used when the antenna centers are located as close as 2 cm from the liquid surface

A computational technique based on a real-coded genetic algorithm for microwave imaging purposes

International audienc

A computational technique based on a real-coded genetic algorithm for microwave imaging purposes

International audienc

Tomografía de microondas : Aplicación a la evaluación de la calidad ósea

En este trabajo se estudió la factibilidad de la detección de cambios en las propiedades dieléctricas del hueso calcáneo mediante la medición de amplitud de microondas en un arreglo tomográfico de antenas monopolo. Con este fin, se llevaron a cabo análisis de sensibilidad local y global desde el punto de vista del problema electromagnético directo. La principal conclusión es que efectivamente es posible detectar, con equipos disponibles en la actualidad, cambios en las propiedades dieléctricas del hueso mediante mediciones de amplitud de campo eléctrico en arreglos tomográficos de microondas. En particular estos análisis muestran que el talón humano puede ser modelado a nivel de sus propiedades dieléctricas, como dos medios: calcáneo y tejido circundante. Basados en estos resultados, y a fin de implementar el problema electromagnético inverso, se estudiaron distintos modelos de redes neuronales artificiales como estimadores de parámetros dieléctricos y geométricos de dispersores cilíndricos homogéneos y heterogéneos, tanto bidimensionales como tridimensionales contenidos en un arreglo tomográfico similar al usado para el problema directo. Encontramos factibilidad en el uso del método, incluso para situaciones de alto contraste dieléctrico, donde los algoritmos más simples fallan. Esto sin mencionar que dichos algoritmos, a diferencia de los basados en redes neuronales artificiales, requieren en su mayoría información de ambos, amplitud y fase, para el proceso de inversión. Este resultado establece un método novedoso de calibración y medición de muestras a partir de información de amplitud de microondas, basado en redes neuronales artificiales. Aplicamos el método al problema de la reconstrucción de las propiedades dieléctricas y geométricas del calcáneo, concluyendo que con estas técnicas de redes neuronales y a partir de información de sólo la amplitud del campo eléctrico, es posible estimar de manera precisa la posición y el tamaño de este tejido y también, aunque con menor precisión, sus propiedades dieléctricas. En particular, el método resulta útil para estimar las propiedades dieléctricas del tejido alrededor del calcáneo, lo cual serviría para acelerar de manera significativa algoritmos iterativos determinísticos clásicos diseñados con ese fin.Se utilizaron métodos de Inteligencia Artificial y Redes Neuronales Artificiales (Deep Learning).Facultad de Ciencias Exacta

On the 3D electromagnetic quantitative inverse scattering problem: algorithms and regularization

In this thesis, 3D quantitative microwave imaging algorithms are developed with emphasis on efficiency of the algorithms and quality of the reconstruction. First, a fast simulation tool has been implemented which makes use of a volume integral equation (VIE) to solve the forward scattering problem. The solution of the resulting linear system is done iteratively. To do this efficiently, two strategies are combined. First, the matrix-vector multiplications needed in every step of the iterative solution are accelerated using a combination of the Fast Fourier Transform (FFT) method and the Multilevel Fast Multipole Algorithm (MLFMA). It is shown that this hybridMLFMA-FFT method is most suited for large, sparse scattering problems. Secondly, the number of iterations is reduced by using an extrapolation technique to determine suitable initial guesses, which are already close to the solution. This technique combines a marching-on-in-source-position scheme with a linear extrapolation over the permittivity under the form of a Born approximation. It is shown that this forward simulator indeed exhibits a better efficiency. The fast forward simulator is incorporated in an optimization technique which minimizes the discrepancy between measured data and simulated data by adjusting the permittivity profile. A Gauss-Newton optimization method with line search is employed in this dissertation to minimize a least squares data fit cost function with additional regularization. Two different regularization methods were developed in this research. The first regularization method penalizes strong fluctuations in the permittivity by imposing a smoothing constraint, which is a widely used approach in inverse scattering. However, in this thesis, this constraint is incorporated in a multiplicative way instead of in the usual additive way, i.e. its weight in the cost function is reduced with an improving data fit. The second regularization method is Value Picking regularization, which is a new method proposed in this dissertation. This regularization is designed to reconstruct piecewise homogeneous permittivity profiles. Such profiles are hard to reconstruct since sharp interfaces between different permittivity regions have to be preserved, while other strong fluctuations need to be suppressed. Instead of operating on the spatial distribution of the permittivity, as certain existing methods for edge preservation do, it imposes the restriction that only a few different permittivity values should appear in the reconstruction. The permittivity values just mentioned do not have to be known in advance, however, and their number is also updated in a stepwise relaxed VP (SRVP) regularization scheme. Both regularization techniques have been incorporated in the Gauss-Newton optimization framework and yield significantly improved reconstruction quality. The efficiency of the minimization algorithm can also be improved. In every step of the iterative optimization, a linear Gauss-Newton update system has to be solved. This typically is a large system and therefore is solved iteratively. However, these systems are ill-conditioned as a result of the ill-posedness of the inverse scattering problem. Fortunately, the aforementioned regularization techniques allow for the use of a subspace preconditioned LSQR method to solve these systems efficiently, as is shown in this thesis. Finally, the incorporation of constraints on the permittivity through a modified line search path, helps to keep the forward problem well-posed and thus the number of forward iterations low. Another contribution of this thesis is the proposal of a new Consistency Inversion (CI) algorithm. It is based on the same principles as another well known reconstruction algorithm, the Contrast Source Inversion (CSI) method, which considers the contrast currents – equivalent currents that generate a field identical to the scattered field – as fundamental unknowns together with the permittivity. In the CI method, however, the permittivity variables are eliminated from the optimization and are only reconstructed in a final step. This avoids alternating updates of permittivity and contrast currents, which may result in a faster convergence. The CI method has also been supplemented with VP regularization, yielding the VPCI method. The quantitative electromagnetic imaging methods developed in this work have been validated on both synthetic and measured data, for both homogeneous and inhomogeneous objects and yield a high reconstruction quality in all these cases. The successful, completely blind reconstruction of an unknown target from measured data, provided by the Institut Fresnel in Marseille, France, demonstrates at once the validity of the forward scattering code, the performance of the reconstruction algorithm and the quality of the measurements. The reconstruction of a numerical MRI based breast phantom is encouraging for the further development of biomedical microwave imaging and of microwave breast cancer screening in particular