This paper presents the design of a novel low-profile ultra-wideband textile antenna for wearable microwave medical imaging systems. The proposed antenna is based on a monopole structure, where two triangles and a few parallel slots are cut at the bottom corners and top edge of the radiation patch, respectively, to achieve an optimized ultra-wide bandwidth and a reduced size of the antenna. Polyester fabrics and conductive copper taffeta, which are universally available on the market, are selected to construct the antenna. Thus, the fabricated prototypes well suit the needs of wearable applications, where flexible components are required to conform to the curved human bodies. The measured results show that the antenna prototype can realize a bandwidth of 109% from 1.198–4.055 GHz with a realized gain of 2.9 dBi. Furthermore, the on-phantom measurements show that there is limited effect on the operating bandwidth and realized gain of the antenna when it is working in the proximity to human bodies. The antenna is used to monitor the recovery process of a bone fracture that is emulated by a body-mimicking phantom with a size-varying blood strip. The time-domain reflection coefficient of the antenna varies significantly with the size of the fracture introduced, which demonstrates the applicability of the antenna for such use scenarios in microwave medical imaging.

Microwave medical imaging systems have shown a competitive advantage in stroke detection due to their cost-effectiveness, non-ionization, and portability. However, these systems often rely on time-consuming image reconstruction techniques, which are disadvantageous for timely stroke treatment. In this paper, a novel microwave medical sensing (MMS) method is proposed for fast stroke classification and localization, which utilizes space division of the region under examination (i.e., the head). The space division is enabled by the generalized scattering matrix theory and incorporates the brain anatomy. Then a novel decision tree learning method is proposed, which facilitates efficient stroke feature identification for classification. The spatial information acquired from the decision tree also results in rapid stroke localization. In order to verify the proposed method, we investigate the feasibility of classifying brain strokes between an intracranial haemorrhage stroke and an ischemic stroke with a wearable MMS system. Both numerical and experimental results are obtained. Compared to the traditional method, the classification rates for simulation and experimental results are improved by 14.1% and 19.2%, respectively. Furthermore, by utilizing the a priori information, the localization time is reduced by 21.1%. Finally, the localization accuracy is higher than 0.90 in both simulation and experimental studies. The classification accuracy and localization efficiency are shown to be greatly improved compared to the traditional method, which has great significance for wearable devices. This study proposes an efficient space-division-based detection method to localize the brain stroke without imaging.

This letter proposes a general formula to determine the amount of compound required to produce water-oil-based phantoms that can emulate the electrical properties of a specific body tissue at microwave frequencies. The existing process to produce such a phantom usually involves mixing together a number of different materials, where the proportion of each compound needs to be known a priori to reproduce the dielectric properties of a specific tissue. In this letter, we first investigate the dielectric variation of the mixture by changing the proportion of each compound over a wide frequency band. By utilizing Lichtenecker's logarithmic mixture formula, the gradient descent iterative optimization method is applied to train the relations obtained, and a formula that determines the amount of each compound required to produce a phantom given a specific tissue type is derived. The formula is verified by constructing representative tissues of the human head, with the durability and fidelity of the produced phantom also analyzed.

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 is 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 techniques to simultaneously visualize the inner body structure with clear tissue boundaries and highlight the positions of strong scatterers. This proposed approach is numerically 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.

Focused microwave breast hyperthermia (FMBH) employs a phased antenna array to perform beamforming that can focus microwave energy at targeted breast tumors. Selective heating of the tumor endows the hyperthermia treatment with high accuracy and low side effects. The effect of FMBH is highly dependent on the applied phased antenna array. This work investigates the effect of polarizations of antenna elements on the microwave-focusing results by simulations. We explore two kinds of antenna arrays with the same number of elements using different digital realistic human breast phantoms. The first array has all the elements’ polarization in the vertical plane of the breast, while the second array has half of the elements’ polarization in the vertical plane and the other half in the transverse plane, i.e., cross polarization. In total, 96 sets of different simulations are performed, and the results show that the second array leads to a better focusing effect in dense breasts than the first array. This work is very meaningful for the potential improvement of the antenna array for FMBH, which is of great significance for the future clinical applications of FMBH. The antenna array with cross polarization can also be applied in microwave imaging and sensing for biomedical applications.

Microwave medical imaging (MMI) operating over the frequency range covering hundreds of megahertz to tens of gigahertz has the potential to provide proactive healthcare solutions to patients with acute (for early diagnosis) or chronic (for daily monitoring) medical conditions. This technology exploits the tissue dielectric properties for disease diagnosis by using quantitative or qualitative algorithms. The advantages of MMI include low health risk, low operational cost, lightweight implementation, and ease of use, given its perspective of miniaturization and integration into portable and handheld devices with networking capability. MMI has been proposed for cancer detection, stroke detection, heart imaging, bone imaging, tracking of in-body drug-loaded nanorobots, etc. It is, however, challenging to develop accurate and robust MMI algorithms for both sensitive and selective diagnosis, due to the inherently ill-conditioned inverse scattering problems and the low dielectric contrast between healthy and diseased tissues. As such, using the a priori knowledge (APK) about the scattering profile to improve the performance of MMI is crucial for practical implementation and clinical deployment of MMI systems. This perspective article presents a new viewpoint of categorizing and utilizing various types of APK, which is acquired from the space, time, or frequency (STF) domain. The article starts with a general categorization framework of APK, followed by formulations of MMI algorithms utilizing APK. Subsequently, the existing APK-oriented MMI algorithms are reviewed in the respective STF domain. Finally, the influence of accuracy of APK on MMI performance is discussed using numerical examples. Through the analysis of the distorted Born iterative method (DBIM) and the pulse radar method, we have discussed the accurate usage of time-domain APK for both quantitative and qualitative evaluations, and the performance improvements of the quantitative and qualitative algorithms are 92% and 80%, respectively. The results demonstrate that the proper implementation of APK can significantly improve imaging accuracy, further validating the effectiveness and generalizability of the proposed model. This perspective would offer some useful insights into the future directions of MMI algorithmic development.