We consider the widely-used weighted L2 norm total variation multiplicative regularizer (MR) for both the Gauss-Newton inversion (GNI) and contrast source inversion (CSI) algorithms in microwave imaging (MWI). It is shown that the proper numerical implementation of the discretized MR operator is important for the GNI algorithm whereas the CSI algorithm is more robust with respect to different implementations of this MR. For the GNI algorithm, the MR operator should be discretized such that high spatial frequency components are not present in its nullspace, and also the resulting discrete operator is positive definite.

We investigate the use of ultrasound images as prior structural information (also known as ultrasound spatial priors) to guide microwave breast imaging so as to enhance its achievable complex permittivity images. In the main approach considered herein, the edges within the discretized ultrasound compressibility image are fed as spatial priors into a microwave imaging algorithm. It is shown that this method requires minimal post-processing of the ultrasound image and can enhance the achievable microwave image accuracy. It is also demonstrated that small tumours can still go undetected in microwave breast imaging using this method if their edges are missing from the spatial priors.

A phaseless Gauss-Newton inversion (GNI) algorithm is developed for microwave imaging applications. In contrast to full-data microwave imaging inversion that uses complex (magnitude and phase) scattered field data, the proposed phaseless GNI algorithm inverts phaseless (magnitude-only) total field data. This phaseless Gauss-Newton inversion (PGNI) algorithm is augmented with three different forms of regularization, originally developed for complex GNI. First, we use the standard weighted L2 norm total variation multiplicative regularizer which is appropriate when there is no prior information about the object being imaged. We then use two other forms of regularization operators to incorporate prior information about the object being imaged into the PGNI algorithm. The first one, herein referred to as SL-PGNI, incorporates prior information about the expected relative complex permittivity values of the object of interest. The other, referred to as SP-PGNI, incorporates spatial priors (structural information) about the objects being imaged. The use of prior information aims to compensate for the lack of total field phase data. The PGNI, SL-PGNI, and SP-PGNI inversion algorithms are then tested against synthetic and experimental phaseless total field data.

Matching fluids used in microwave imaging are often lossy to reduce the reflections from the system enclosure. To enable the use of low-loss matching fluids, we investigate the use of absorbing metasurfaces as the enclosure. This has the potential to enhance the signal-to-noise ratio of the data, thus, enhancing the achievable image accuracy. The presented example has the following limitation: as opposed to design and simulation of the physical structure of the metasurface enclosure, it uses two impedance boundary conditions on top of a metallic-backed substrate. Thus, the angular dependency of a practical absorber is not completely taken into account by this model. At the conference, we’ll also consider a microwave imaging metasurface enclosure via design and simulation of a physical structure.

This paper summarizes and synthetically evaluates a method proposed for metasurface design. The method takes as input a set of desired far-field (FF) performance specifications and produces an effective susceptibility distribution that, when illuminated with a known incident field, produces a FF radiation pattern exhibiting the desired specifications. To this end, electromagnetic inversion (inverse source) is used to solve for the desired tangential fields on the output side of the metasurface. A finite-difference frequency-domain solver, recently developed for simulation of metasurfaces, is used to synthetically evaluate the proposed method using a two-dimensional (2D) example. It should be noted that microscopic metasurface design (i.e., the design of the physical metasurface implementation) is beyond the scope of this paper.

This paper summarizes, extends, and synthetically evaluates a method for metasurface design which uses electromagnetic inversion. After the inversion algorithm determines a homogenized surface susceptibility model to implement a desired power pattern, the susceptibility distribution is converted to a three-layer admittance sheet topology. Lastly, full-wave commercial software is used to simulate and verify the performance of a metasurface designed and implemented using the proposed procedure.

A method based on electromagnetic inversion is extended to facilitate the design of passive, lossless, and reciprocal metasurfaces. More specifically, the inversion step is modified to ensure that the field transformation satisfies local power conservation, using available knowledge of the incident field. This paper formulates a novel cost functional to apply this additional constraint, and describes the optimization procedure used to find a solution that satisfies both the user-defined field specifications and local power conservation. Lastly, the method is demonstrated with a two-dimensional (2D) example.