Two real-time reconstruction algorithms, quantitative microwave holography and scattered-power mapping, have been shown to be successful in the imaging of compressed tissue of relatively small thicknesses such as 1 cm and 2 cm. In both cases, planar data acquisition of frequency-swept transmission coefficients has been employed. Despite the fact that these algorithms are based on a linear forward model of scattering, they have been capable of providing quantitative estimates of the tissue permittivity due to the experimentally derived kernel of the scattering integral. Here, we demonstrate similar performance with a thicker (about 5 cm) compressed-breast phantom. This thickness is greater or comparable to the median thickness employed in mammography, depending on the view (craniocaudal or mediolateral oblique). The two methods are described in a common mathematical framework for the first time. The importance of the system calibration and the choice of a host medium are discussed through experiments. A new method for focusing onto suspect regions is demonstrated. The limitations of real-time imaging are highlighted along with an outlook to improving the image resolution and suppressing artifacts without sacrificing the reconstruction speed.

Microwave and millimeter-wave measurements acquire total-field responses from measurements, yet imaging algorithms instead require the data in the form of scattered-field responses. Two approaches exist for the extraction of the scattered-field data from the total-field responses, namely, the Born and the Rytov data approximations. It is well known that, depending on the target’s size, contrast, and structural complexity, one approximation can achieve an improved accuracy over the other. Yet, the Rytov approximation is rarely used in microwave and millimeter-wave imaging, likely due to phase-unwrapping problems occurring in the case of strongly heterogeneous electrically large targets. Here, we propose a method to utilize the Born and the Rytov approximations simultaneously in a single inversion step for real-time imaging with quantitative microwave holography (QMH). We show through examples with simulated and experimental data that in near-field imaging scenarios, including the imaging of a breast-tissue phantom, there are significant benefits in employing the new method.

We present a general theory of the holographic image reconstruction with linear frequency modulation (LFM) radars. For the first time, the system limitations in terms of the object extent and distance are derived and explicitly related to the LFM radar frequency-modulation slope ɣ . The holographic inversion formula is improved to account for the spherical spread of the scattered wave. The theory and the generalized holographic inversion algorithm are validated by synthetic benchmark data as well as experimental data from an in-house LFM-radar prototype operating at 29.9-GHz central frequency and bandwidth of 5.8 GHz. Experiments confirm that the lateral spatial resolution is about 5 mm. For optimal performance, the system is calibrated using a simple but effective calibration approach based on a measurement with a metallic plate. Experiments, with a volunteer carrying metallic and nonmetallic objects, demonstrate very good performance in realistic scenarios.

An ultra-wide band (UWB) active slot antenna for tissue sensing arrays is developed and measured to operate from 3 GHz to 8 GHz. The sensing element reported here is to be used in the imaging of the breast. It integrates a low-noise amplifier (LNA) with a printed-slot antenna to achieve gain enhancement of about 20 dB. The first prototype integrates the biasing of the LNA on the same board as the antenna. The second prototype uses an external bias-tee providing DC power to the LNA through the coaxial connector of the active antenna. Both prototypes achieve the expected gain enhancement in comparison with the passive antenna element.

In this article, we honor Prof. John W. Bandler and his legacy in RF and microwave modeling and automated design optimization. We showcase his pioneering breakthroughs in minimax optimization, p th norm formulations, yield optimization, and nonlinear circuit design optimization. We highlight advances in direct electromagnetic (EM) microwave optimization, circuit response sensitivities, and efficient S -parameters sensitivity calculations. We explore the port-tuning version of space mapping (SM) for EM-based analysis, techniques for industrial microwave design of satellite systems, and post-manufacture hardware tuning. The integration of artificial neural networks (ANNs) with SM for enhanced EM-based design optimization and yield prediction, cognition-driven microwave filter design, and parallels between SM and artificial intelligence (AI) is examined. Finally, we speculate on the future integration of cognitive science with engineering design, leveraging the synergy of AI, machine learning (ML), and SM.

General theory of holographic inversion with linear frequency modulation (GHI-LFM) has been proposed recently, providing the framework for further advances in the holographic methods for image reconstruction with millimeter-wave linear frequency modulation (LFM) radar. Here, accelerated holographic imaging (AHI)-LFM, an AHI method for LFM radar that exploits range stacking, is proposed. The method is developed and validated for the purpose of whole-body security screening. The image quality and the computational efficiency are compared with the original GHI-LFM algorithm. It is demonstrated that the AHI-LFM algorithm improves the reconstruction speed by at least an order of magnitude while preserving the same image quality for the image sizes arising in practical whole-body security screening.