A vascular aneurysm (VA) is an abnormal bulge or ballooning in the wall of a blood vessel, which results in a great risk of death after rupture. The best way to avoid VA rupture is to detect it at an early stage and take proper measures. However, there is no suitable method for early detection of VA. In this paper, we propose a novel nanoparticles (NPs) based sensing method for VA, namely, NP tomography (NPT). We first construct multiple blood vessel models in the forward problem by varying the location of VA and obtain the time-of-arrival (TOA) of NPs. In the inverse problem, we then infer the actual location of VA based on the recorded TOA of NPs, thereby enabling early VA sensing. The proposed methodology is validated through comprehensive COMSOL simulations. Numerical results demonstrate that the VA detection rate through NPT can be as high as 96 %.

Microwave medical imaging and sensing (MMSI) operates within the frequency range of several hundred megahertz to several gigahertz. It has the potential to provide proactive healthcare solutions for both acute and chronic disease patients. This technology leverages tissue dielectric properties, employing quantitative or qualitative algorithms for disease diagnosis. MMSI has been proposed for applications such as cancer detection, stroke monitoring, cardiac imaging, skeletal imaging, and tracking of nanobots for in-body drug delivery. This paper proposes a novel microgel-based microwave sensing system for continuous blood glucose monitoring. The research contributions can be summarized in two main aspects. First, a novel microgel sensitive to the conditions of the internal microenvironment was synthesized and characterized. Second, a microwave sensing system was proposed to capture physiological signals from the human body and estimate blood glucose levels. Microgels serve as internal sensors, exhibiting different electrical properties based on changes in blood glucose information within the body, while external antennas are used to detect variations in dielectric constants. A new algorithm is presented for precise disease feature identification based on support vector machine. To validate the proposed system, the feasibility of classifying blood glucose into high and low levels was investigated. Compared to traditional microwave medical sensing methods without microgel sensors, the classification performance improved by 35%, demonstrating the effectiveness of the proposed system.

Alzheimer’s disease (AD) is a neurodegenerative disorder that leads to cognitive impairment and is characterized by high morbidity, mortality, and economic burden. Aβ amyloid is one of pathological features of AD, which has chiral properties, and there are limitations in conventional brain imaging methods for detecting Aβ. Computed tomography (CT) and magnetic resonance imaging (MRI) are unable to detect Aβ, and positron emission tomography (PET) imaging is expensive and has limited availability. Aβ can change the direction of polarization of electromagnetic waves, and thus microwave medical sensing imaging (MMSI) is expected to enable noninvasive measurement of Aβ. In this paper, we propose a generalized MMSI system based on planar Yagi antenna to detect Aβ features, which is mainly used to analyze the effect of chirality on the deflection of electromagnetic waves. In addition, due to the complex electromagnetic environment of the brain and the low signal-to-noise ratio (SNR) during information transmission, we propose to take the axial ratio as the main optimization target for antenna optimization, so as to make the antenna more sensitive to chirality, and then achieve the accurate detection of Aβ. A Yagi antenna with an operating frequency of 1.52–2.21GHz, and the average axial ratio over the operating band is 33dB, was designed and used. According to the simulation, the received signal amplitude will be amplified by a factor of 25 in the presence of chiral substances in the same volume. The results show that the proposed detection system is sensitive to Aβ amyloid and can realize microwave detection of AD.

Microwave medical imaging (MMI), functioning across a wide frequency range from hundreds of megahertz to tens of gigahertz, shows potential for offering proactive healthcare solutions to patients with acute (e.g., suitable for early diagnosis) or chronic (e.g., ideal for continuous monitoring) conditions. However, developing accurate and robust MMI algorithms is challenging due to inherently complex inverse scattering problems and low dielectric contrast between healthy and diseased tissues. Therefore, employing A-Priori-Knowledge (APK) is crucial for enhancing MMI’s effectiveness in practical applications and clinical settings. This paper proposes a microwave quantitative imaging algorithm DBIM-APK based on spatial APK. It involves fusing data from APK based on the current patient’s MMI and from APK based on the existing MRI database, resulting in a hybrid APK. This hybrid APK is then incorporated into DBIM as the final APK. Compared to traditional DBIM, this algorithm doesn’t solve the contrast function through step-by-step iteration, but instead, it is directly provided by the hybrid spatial APK. According to the numerical results, the boundary artifacts caused by the inverse scattering nonlinear problem are basically eliminated after the introduction of spatial APK and the SSIM (Structural Similarity Index Measure) of the reconstructed image compared to the original image has improved from 0.62 to 0.95. This algorithm optimizes computational resources, reduces imaging time, improves imaging accuracy, enhances spatial resolution, and more clearly restores the structure of the breast.

Microwave medical sensing and imaging (MMSI) is a highly active research field. In MMSI, electromagnetic inverse scattering (EIS) is a commonly used technique that infers the internal characteristics of the diseased area by measuring the scattered field. It is worth noting that the image formed by EIS often exhibits the super-resolution phenomenon, which has attracted much research interest over the past decade. A classical perspective is that multiple scattering leads to super-resolution, but this is subject to debate. This paper aims to analyze the super-resolution behavior for Born-iterative-type algorithms for the following three aspects. Firstly, the resolution defined by the traditional Rayleigh criterion can only be applied to point scatterers. It does not suit general scatterers. By using the Sparrow criterion and the generalized spread function, the super-resolution condition can be derived for general scatterers even under the Born approximation (BA) condition. Secondly, an iterative algorithm results in larger coefficients in the high-frequency regime of the optical transfer function compared to non-iterative BA. Due to the anti-apodization effect, the spread function of the iterative method becomes steeper, which leads to a better resolution following the definition of the Sparrow criterion mentioned above. Thirdly, the solution from the previous iteration, as the prior knowledge for the next iteration, will cause changes in the total field, which provides additional information outside the Ewald sphere and thereby gives rise to super-resolution. Comprehensive numerical examples are used to verify these viewpoints.

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.

We propose a new theoretical framework for quantification and sensitization of disease state (DS). This is in contrast to the traditional discrete description of disease, where the inherent characteristic of its progression is ignored, and a finite number of DSs are resulted, effectively leading the sensing process to the act of classifying. Central to the framework is a canonical fuzzy model of DS that allows for conversion of its linguistic description into a normalized numerical variable. This generates the mapping between one set with the domain of the universe of DSs and another set with the domain of the universe of disease labels (DLs). Subsequently, the framework is analyzed from the fuzzy-set-theoretic perspective by utilizing the medical expert knowledge system of disease severity, consisting of the Acute Physiology and Chronic Health Evaluation (APACHE), which provides useful insight that enables the disease diagnosis process to be designed and optimized as a soft computing problem. Built on this fuzzy analysis, we present two multimodal strategies, namely the reversibility combining (RVC) and reliability combining (RLC), to enhance the sensing performance measured through the degree of non-reversibility of the surjective mapping from DLs to sensing outputs (SOs). Finally, we utilize some numerical examples based on highly realistic synthetic medical data to elaborate on the proposed framework, which offers a new perspective for disease diagnosis by monitoring continuously the individual's disease progression.

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.

This paper propose a novel disease retrospective monitoring strategy (DRMS) for optimal brain stroke diagnosis. We describe the disease monitoring process using a fuzzy-based model and demonstrate the use of information at different time points to improve disease diagnosis accuracy under the framework of fuzzy-inspired sensing (FIS). Numerical examples are used to demonstrate how the proposed DRMS can be used to determine the optimal treatment strategy with the least amount of fuzziness.

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 article, a novel microwave medical sensing (MMS) method is proposed for fast stroke classification and localization, which utilizes space division of the region under examination (RUE) (i.e., the head). The space division is enabled by the generalized scattering matrix (GSM) 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. To verify the proposed method, we investigate the feasibility of classifying brain strokes between an intracranial hemorrhage (ICH) stroke and an ischemic stroke (IS) 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.

Microwave medical sensing and imaging (MMSI) has been a research hotspot in the past years. Imaging algorithms based on electromagnetic inverse scattering (EIS) play a key role in MMSI due to the super-resolution phenomenon. EIS problems generally employ far-field scattered data to reconstruct images. However, the far-field data do not include information outside the Ewald’s sphere, so theoretically it is impossible to achieve super resolution. The reason for super resolution has not been clarified. The majority of the current research focuses on how nonlinearity affects the super-resolution phenomena in EIS. However, the mechanism of super-resolution in the absence of nonlinearity is routinely ignored. In this research, we address a prevalent yet overlooked problem where the image resolution due to scatterers of extended structures is incorrectly analyzed using the model of point scatterers. Specifically, the classical resolution of EIS is defined by the Rayleigh criterion which is only suitable for point-like scatterers. However, the super-resolution in EIS is often observed for general scatterers like cylinders, squares or Austria shapes. Subsequently, we provide theoretical results for the Born approximation framework in EIS, and employ the Sparrow criteria to quantify the resolution for symmetric objects of extended structures. Furthermore, the modified Sparrow criterion is proposed to calculate the resolution of asymmetric scatterers. Numerical examples show that the proposed approach can better explain the super-resolution phenomenon in EIS.

This article proposes a novel fuzzy-inspired nanobiosensing framework for soft classification of tumor by using multiple features of the tumor, where interrelationships between different features are accounted for. For illustration purpose, two features of breast cancer, namely, tumor tissue abnormality and tumor foci separation, are utilized to determine the disease state. The sensing performance is then evaluated through the fuzzy relation transfer analysis. This analysis enables an intuitive yet systematic way to characterize the variation of classification fuzziness occurred during the sensing process facilitated by nanoscale contrast agents. Subsequently, the mean absolute error of the pre- and post-contrast membership functions is employed to evaluate the sensing integrity. Finally, numerical results are presented to demonstrate the principles of the proposed framework.

In this paper, we propose a novel imaging-process-informed image segmentation method that accounts for uncertainty during the imaging process. A priori information is incorporated to enhance the contrast between stroke area and healthy tissues. The distorted Born iterative method (DBIM) is utilized to reconstruct the stroke area of the brain. Due to the non-linear relationship between actual and estimated dielectric constants resulting from DBIM, the microwave medical image lacks a clearly defined boundary, posing a challenge to accurately segment it using traditional methods. The proposed method achieves effective image segmentation by improving the traditional threshold method. From the simulation results, the region misclassified by the traditional method accounts for 89%, while the proposed method results in a misclassification rate of only 13%. The results demonstrate a significant improvement of 58.85% in accurately reproducing the dielectric constants.

This paper presents a novel methodology in the field of microwave medical sensing, which enables quick classification and localization of strokes without the need for imaging. The method introduced in this study utilizes space division within the region under examination, specifically the head, by leveraging the principles of the generalized scattering matrix theory and incorporating a prior knowledge of brain anatomy. Furthermore, a unique approach based on decision tree learning method is proposed to enhance the identification of stroke features for effective classification. The simulation results demonstrate noteworthy enhancements in both the accuracy of stroke classification and the efficiency of localization, surpassing the performance of the traditional method. The classification results have a 14% performance improvement, whereas the localization accuracy of the proposed method is 94%.

This paper presents a novel low-profile ultra-wideband antenna for wearable microwave medical imaging (MMI) applications. The proposed antenna has a monopole structure with two triangles and a few parallel slots cut at the bottom corners and top edge of the radiation patch, respectively, to achieve an optimized ultra-wide bandwidth and a smaller antenna size. The simulated and measured results show that the antenna prototype can realize a bandwidth of 93% from 0.9–3.0 GHz with a realized gain of 3.32 dBi. Furthermore, in order to verify the performance, we investigate the feasibility of classifying brain strokes between an intracranial haemorrhage stroke and an ischemic stroke with this antenna. The S parameter of the antenna varies significantly with the stroke types introduced (i.e., different dielectric constants), which demonstrates the applicability of the antenna for such use scenarios in MMI.

This article proposes a novel contrast-enhanced microwave cancer detection (MCD) system to accurately detect the location of a tumor targeted by nanoscale contrast agents. This system adopts the angle-of-arrival (AoA) positioning approach that is conventionally applied to the region where plane waves dominate the propagation. Hence, to ensure the effectiveness of the AoA approach when it is working in close proximity to the human body, a new algorithm is proposed to transfer the antenna’s radiation pattern from the far-field to the detection region where spherical waves can still be observed. This AoA-based MCD system requires fewer antennas than other radar-based positioning approaches, thereby creating a low-profile hardware architecture. In addition, during the detection process, a differential technique is incorporated in order to track the signal change due to the administration of contrast agents, which can significantly suppress the noise inside the biological medium. The AoA-based differential MCD system is numerically studied on an anatomically realistic breast phantom subject to a variety of signal-to-noise ratios (SNRs). The results show that the proposed system can successfully locate the tumor with an average resolution of 0.6 mm. When the SNR in the biological medium is 10 dB, the average positioning error is less than 1.5 mm with sensitivity maintained above 60%, which surpasses the performance of other similar systems. The system is also experimentally validated with a physical breast phantom and stepper-motor-driven rotating antennas, where the reconstructed image shows a minor deviation of 3.61 mm with that of the simulated one.

In this paper, a novel fuzzy-receiver operating characteristics (F-ROC) is proposed to evaluate the performance of fuzzy-inspired biosensing (FIB). The development process of diseases is fuzzy, and the traditional classification of diseases is an either-or hard classification, which cannot reflect the developmental stages of diseases correctly. FIB utilizes fuzzy theory to realize the disease classification, and the traditional ROC curve is not suitable as an evaluation mechanism for the disease fuzzy process. Therefore, this paper proposes a general model to describe the transfer process of the membership function of FIB, and proposes the reasons why the traditional ROC is not applicable. After that, this paper rewrites the parameter definitions in the ROC curve and establishes the F-ROC curve to evaluate the transfer performance of FIB. An example of tumor stage classification using multi-contrast-agent strategies (MCAS) strategy is utilized to verify the evaluation of the FIB performance of the proposed F-ROC curve.

DNA circuits, consisting of DNAzyme library logic gates that relay DNA strand displacement reactions, have been proven to harness massive parallel computing abilities in a biologically friendly environment. Sequential logic is critical to detecting and visualising the biological micro-environment, which concerns both the previous and current states. However, the research to date has primarily focused only on combinational logic rather than sequential logic. Thus, this paper proposes novel sequential logic DNA circuits through the competition mechanism, allowing recognising previous and current triggering sequential signals. We report the sequential logical gates using DNAzymes and illustrate their potential applications. This work would be a valid building block in molecular computing to perform more complicated tasks in the biological microenvironment.

This work aims to demonstrate through computational analysis that, by monitoring the trajectory of externally manipulable nanoswimmers (NS), the in vivo biological gradient field (BGF) interacting with the NS can be indirectly observed. This observability is fundamental to the recently proposed framework of computational nanobiosensing (CONA) for "smart" cancer detection. We first present a novel NS propagation model to emulate the complex and chaotic NS kinetics inside the capillary network. Next, we propose an efficient control method that is able to employ the NS as in vivo sensors for the measurement of a specific BGF such as blood viscosity. The proposed method, based on the Linear Quadratic Regulator (LQR), effectively stabilizes the signal-to-noise ratio (SNR) induced by the Brownian motion of NS at a level above 10 dB to enhance the accuracy of viscosity estimation.

This paper studies the feasibility of detecting the pneumonia due to the COVID-19 with microwave medical imaging. One challenge while formulating such a problem is to identify the disease in lungs whose dielectric permittivity is dynamically fluctuating with the respiration. In this paper, we utilize this feature by assuming that the permittivity of the disease has minor variation at microwave frequencies during the respiration, and thus the dielectric variance of the pixels at the diseased site over a number of consecutive images significantly differs from those of the other tissues in the thorax. Based on this assumption, we propose two approaches that make use of the a priori information (API) on the position of the heart and the symmetry of the thorax, respectively, to identify a diseased lung. Finally, these two approaches are numerically validated on a thorax phantom, and their performance is compared.