Electromagnetic (EM) medical technologies are rapidly expanding worldwide for both diagnostics and therapeutics. As these technologies are low-cost and minimally invasive, they have been the focus of significant research efforts in recent years. Such technologies are often based on the assumption that there is a contrast in the dielectric properties of different tissue types or that the properties of particular tissues fall within a defined range. Thus, accurate knowledge of the dielectric properties of biological tissues is fundamental to EM medical technologies. Over the past decades, numerous studies were conducted to expand the dielectric repository of biological tissues. However, dielectric data is not yet available for every tissue type and at every temperature and frequency. For this reason, dielectric measurements may be performed by researchers who are not specialists in the acquisition of tissue dielectric properties. To this end, this paper reviews the tissue dielectric measurement process performed with an open-ended coaxial probe. Given the high number of factors, including equipment- and tissue-related confounders, that can increase the measurement uncertainty or introduce errors into the tissue dielectric data, this work discusses each step of the coaxial probe measurement procedure, highlighting common practices, challenges, and techniques for controlling and compensating for confounders.

Abstract-Microwave Imaging (MI) has been widely investigated as a method to detect early stage breast cancer based on the dielectric contrast between normal and cancerous breast tissue at microwave frequencies. Furthermore, classification methods have been developed to differentiate between malignant and benign tumours. To successfully classify tumours using Ultra Wideband (UWB) radar, other features have to be examined other than simply the dielectric contrast between benign and malignant tumours, as contrast alone has been shown to be insuficient. In this context, previous studies have investigated the use of the Radar Target Signature (RTS) of tumours to give valuable information about the size, shape and surface texture. In this study, a novel classification method is examined, using Principal Component Analysis (PCA) to extract the most important tumour features from the RTS. Support Vector Machines (SVM) are then applied to the principal components as a method of classifying these tumours. Finally, several different classification architectures are compared. In this study, the performance of classifiers is tested using a database of 352 tumour models, comprising four different sizes and shapes, using the cross validation method.

Abstract-Ultra Wideband (UWB) radar has been extensively investigated as a means of detecting early-stage breast cancer.

Microwave imaging via space-time (MIST) beamforming has been shown to be one of the most promising imaging modalities for detecting small malignant breast tumors. This paper outlines two modifications to the MIST system developed by Hagness for the early detection of breast cancer, resulting in a quasi-multistatic MIST beamformer (multi-MIST). Multistatic MIST beamforming involves illuminating the breast with an ultrawideband (UWB) signal from one antenna while collecting the reflections at an array of antennas, as opposed to traditional monostatic MIST beamforming where only the transmitting antenna records the reflections from the breast. In order to process the multistatic data, traditional data-adaptive artifact removal algorithms have to be modified to accommodate signals from all antennas. Also, the MIST beamforming algorithm, which spatially focuses the signal and compensates for frequency-dependent propagation effects, has to be modified. The algorithms are tested on a 2-D anatomically accurate finite-difference time-domain model of the breast. The multi-MIST beamformer described here is shown to offer an improved signal to clutter ratio when compared to the traditional monostatic MIST beamformer.

Abstract-Ultra wideband (UWB) Microwave imaging is one of the most promising emerging imaging technologies for breast cancer detection, and is based on the dielectric contrast between normal and cancerous tissues at microwave frequencies. UWB radar imaging involves illuminating the breast with a microwave pulse and reflected signals are used to determine the presence and location of significant dielectric scatterers, which may be representative of cancerous tissue within the breast. Beamformers are used to spatially focus the reflected signals and to compensate for path dependent attenuation and phase effects. While these beamforming algorithms have often been evaluated in isolation, variations in experimental conditions and metrics prompts the assessment of the beamformers on common anatomically and dielectrically representative breast models in order to effectively compare the performance of each. This paper seeks to investigate the following beamforming algorithms: Monostatic and Multistatic DelayAnd-Sum (DAS), Delay-Multiply-And-Sum (DMAS) and Improved Delay-And-Sum (IDAS). The performance of each beamformer is evaluated across a range of appropriate metrics.

Existing treatments for Alzheimer’s disease (AD) have questionable efficacy with a need for research into new and more effective therapies to both treat and possibly prevent the condition. This review examines a novel therapeutic modality that shows promise for treating AD based on modulating neuronal activity in the gamma frequency band through external brain stimulation. The gamma frequency band is roughly defined as being between 30 Hz-100 Hz, with the 40 Hz point being of particular significance. The epidemiology, diagnostics, existing pathological models, and related current treatment targets are initially briefly reviewed. Next, the concept of external simulation triggering brain activity in the gamma band with potential demonstration of benefit in AD is introduced with reference to a recent important study using a mouse model of the disease. The review then presents a selection of relevant studies that describe the neurophysiology involved in brain stimulation by external sources, followed by studies involving application of the modality to clinical scenarios. A table summarizing the results of clinical studies applied to AD patients is also reported and may aid future development of the modality. The use of a therapy based on modulation of gamma neuronal activity represents a novel non-invasive, non-pharmacological approach to AD. Although use in clinical scenarios is still a relatively recent area of research, the technique shows good signs of efficacy and may represent an important option for treating AD in the future.

Confocal Microwave Imaging (CMI) for the early detection of breast cancer has been under development for over two decades and is currently going through early-phase clinical evaluation. The image reconstruction algorithm is a key signal processing component of any CMI-based breast imaging system and impacts the efficacy of CMI in detecting breast cancer. Several image reconstruction algorithms for CMI have been developed since its inception. These image reconstruction algorithms have been previously evaluated and compared, using both numerical and physical breast models, and healthy volunteer data. However, no study has been performed to evaluate the performance of image reconstruction algorithms using clinical patient data. In this study, a variety of imaging algorithms, including both data-independent and data-adaptive algorithms, were evaluated using data obtained from a small-scale patient study conducted at the University of Calgary. Six imaging algorithms were applied to reconstruct 3D images of five clinical patients. Reconstructed images for each algorithm and each patient were compared to the available clinical reports, in terms of abnormality detection and localisation. The imaging quality of each algorithm was evaluated using appropriate quality metrics. The results of the conventional Delay-and-Sum algorithm and the Delay-Multiply-and-Sum (DMAS) algorithm were found to be consistent with the clinical information, with DMAS producing better quality images compared to all other algorithms.

The dielectric properties of tissues are key parameters in electromagnetic medical technologies. Despite the apparent simplicity of the dielectric measurement process, reported data has been inconsistent for heterogeneous tissues. Dielectric properties may be attributed to heterogeneous tissues by identifying the tissue types that contributed to the measurement through histological analysis. However, accurate interpretation of the measurements with histological analysis requires first defining an appropriate histology region to examine. Here, we investigate multiple definitions for the probe sensing depth and uniquely calculate this parameter for measurements with a realistic range of tissues. We demonstrate that different sensing depth definitions are not equivalent, and may introduce error in dielectric data. Lastly, we propose an improved definition, given by the depth to which the probe can detect changes in the tissue sample, within the measurement uncertainty. We equate this sensing depth with histology depth, thus supporting the need of having the tissue region that contributes to the dielectric data be the same as that which is analysed histologically. This study demonstrates that, for these tissues, the histology depth is both frequency and tissue dependent. Therefore, the histology depth should be selected based on the measurement scenario, otherwise inaccuracies in the data may result.