Comparison of Microwave Hyperthermia Applicator Designs with Fora Dipole and Connected Array.

In microwave hyperthermia tumor therapy, electromagnetic waves focus energy on the tumor to elevate the temperature above its normal levels with minimal injury to the surrounding healthy tissue. Microwave hyperthermia applicator design is important for the effectiveness of the therapy and the feasibility of real-time application. In this study, the potential of using fractal octagonal ring antenna elements as a dipole antenna array and as a connected array at 2.45 GHz for breast tumor hyperthermia application was investigated. Microwave hyperthermia treatment models consisting of different fractal octagonal ring antenna array designs and a breast phantom are simulated in COMSOL Multiphysics to obtain the field distributions. The antenna excitation phases and magnitudes are optimized using the global particle swarm algorithm to selectively increase the specific absorption rate at the target region while minimizing hot spots in other regions within the breast. Specific absorption rate distributions, obtained inside the phantom, are analyzed for each proposed microwave hyperthermia applicator design. The dipole fractal octagonal ring antenna arrays are comparatively assessed for three different designs: circular, linear, and Cross-array. The 16-antenna dipole array performance was superior for all three 1-layer applicator designs, and no distinct difference was found between 16-antenna circular, linear, or cross arrays. Two-layer dipole arrays have better performance in the deep-tissue targets than one-layer arrays. The performance of the connected array with a higher number of layers exceeds the performance of the dipole arrays in the superficial regions, while they are comparable for deep regions of the breast. The 1-layer 12-antenna circular FORA dipole array feasibility as a microwave hyperthermia applicator was experimentally shown.

Broadband Measurements of Soil Complex Permittivity.

Agriculture is a major consumer of freshwater and is often associated with low water productivity. To prevent drought, farmers tend to over-irrigate, putting a strain on the ever-depleting groundwater resources. To improve modern agricultural techniques and conserve water, quick and accurate estimates of soil water content (SWC) should be made, and irrigation timed correctly in order to optimize crop yield and water use. In this study, soil samples common to the Maltese Islands having different clay, sand, and silt contents were, primarily, investigated to: (a) deduce whether the dielectric constant can be considered as a viable indicator of the SWC for the soils of Malta; (b) determine how soil compaction affects the dielectric constant measurements; and (c) to create calibration curves to directly relate the dielectric constant and the SWC for two different soil types of low and high density. The measurements, which were carried out in the X-band, were facilitated by an experimental setup comprising a two-port Vector Network Analyzer (VNA) connected to a rectangular waveguide system. From data analysis, it was found that for each soil investigated, the dielectric constant increases notably with an increase in both density and SWC. Our findings are expected to aid in future numerical analysis and simulations aimed at developing low-cost, minimally invasive Microwave (MW) systems for localized SWC sensing, and hence, in agricultural water conservation. However, it should be noted that a statistically significant relationship between soil texture and the dielectric constant could not be determined at this stage.

A Method for Extracting Debye Parameters as a Tool for Monitoring Watered and Contaminated Soils.

Soil monitoring is a key topic from several perspectives, such as moisture level control for irrigation management and anti-contamination purposes. Monitoring the latter is becoming even more important due to increasing environmental pollution. As a direct consequence, there is a strong demand for innovative monitoring systems that are low cost, provide for quasi-real time and in situ monitoring, high sensitivity, and adequate accuracy. Starting from these considerations, this paper addresses the implementation of a microwave reflectometry based-system utilizing a customized bifilar probe and a miniaturized Vector Network Analyzer (m-VNA). The main objective is to relate frequency-domain (FD) measurements to the features of interest, such as the water content and/or the percentage of some polluting substances, through an innovative automatable procedure to retrieve the Debye dielectric parameters of the soil under different conditions. The results from this study confirm the potential of microwave reflectometry for moisture monitoring and contamination detection.

The synthesis of a liver tissue mimicking solution for microwave medical applications.

This paper presents the synthesis of a mixture solution that is equivalent to ex-vivo liver tissue dielectric characteristics between 500 MHz and 5 GHz. The mimicking solution was synthesized using concentrations of two chemicals, the solute which is referred to as the inclusion phase and the solvent, referred to as the host phase. The inclusion phase consisted of bovine serum albumin (BSA) powder and the host phase consisted of a phosphate-buffered saline (PBS) solution with a concentration of Triton X-100 (TX-100). The dielectric properties of these two phases were substituted into Bruggeman's two-phase mixture equation to estimate the dielectric properties of excised liver. Furthermore, the study exploits Bruggeman's equation to investigate the impact of tissue dehydration levels on the dielectric properties of an excised tissue. The effect of dehydration has been characterised as a function of time based on the loss-on-drying technique (a substance is heated until it is completely dry). Dielectric parameters were measured as a function of frequency using the Slim Form open-ended coaxial probe at a constant room temperature of circa 25 °C. Measured dielectic data were fitted to the Cole-Cole model and good agreement with the mimicking solutions was obtained. These results indicate that these solutions can be used to model the human body phantoms for microwave medical applications.

Measurement and image-based estimation of dielectric properties of biological tissues -past, present, and future.

The dielectric properties of biological tissues are fundamental pararmeters that are essential for electromagnetic modeling of the human body. The primary database of dielectric properties compiled in 1996 on the basis of dielectric measurements at frequencies from 10 Hz to 20 GHz has attracted considerable attention in the research field of human protection from non-ionizing radiation. This review summarizes findings on the dielectric properties of biological tissues at frequencies up to 1 THz since the database was developed. Although the 1996 database covered general (normal) tissues, this review also covers malignant tissues that are of interest in the research field of medical applications. An intercomparison of dielectric properties based on reported data is presented for several tissue types. Dielectric properties derived from image-based estimation techniques developed as a result of recent advances in dielectric measurement are also included. Finally, research essential for future advances in human body modeling is discussed.

Review of Thermal and Physiological Properties of Human Breast Tissue.

Electromagnetic thermal therapies for cancer treatment, such as microwave hyperthermia, aim to heat up a targeted tumour site to temperatures within 40 and 44 °C. Computational simulations used to investigate such heating systems employ the Pennes' bioheat equation to model the heat exchange within the tissue, which accounts for several tissue properties: density, specific heat capacity, thermal conductivity, metabolic heat generation rate, and blood perfusion rate. We present a review of these thermal and physiological properties relevant for hyperthermia treatments of breast including fibroglandular breast, fatty breast, and breast tumours. The data included in this review were obtained from both experimental measurement studies and estimated properties of human breast tissues. The latter were used in computational studies of breast thermal treatments. The measurement methods, where available, are discussed together with the estimations and approximations considered for values where measurements were unavailable. The review concludes that measurement data for the thermal and physiological properties of breast and tumour tissue are limited. Fibroglandular and fatty breast tissue properties are often approximated from those of generic muscle or fat tissue. Tumour tissue properties are mostly obtained from approximating equations or assumed to be the same as those of glandular tissue. We also present a set of reliable data, which can be used for more accurate modelling and simulation studies to better treat breast cancer using thermal therapies.

Application of Machine Learning to Predict Dielectric Properties of In Vivo Biological Tissue.

In this paper we revisited a database with measurements of the dielectric properties of rat muscles. Measurements were performed both in vivo and ex vivo; the latter were performed in tissues with varying levels of hydration. Dielectric property measurements were performed with an open-ended coaxial probe between the frequencies of 500 MHz and 50 GHz at a room temperature of 25 °C. In vivo dielectric properties are more valuable for creating realistic electromagnetic models of biological tissue, but these are more difficult to measure and scarcer in the literature. In this paper, we used machine learning models to predict the in vivo dielectric properties of rat muscle from ex vivo dielectric property measurements for varying levels of hydration. We observed promising results that suggest that our model can make a fair estimation of in vivo properties from ex vivo properties.

Application of Artificial Neural Networks for Accurate Determination of the Complex Permittivity of Biological Tissue.

Medical devices making use of radio frequency (RF) and microwave (MW) fields have been studied as alternatives to existing diagnostic and therapeutic modalities since they offer several advantages. However, the lack of accurate knowledge of the complex permittivity of different biological tissues continues to hinder progress in of these technologies. The most convenient and popular measurement method used to determine the complex permittivity of biological tissues is the open-ended coaxial line, in combination with a vector network analyser (VNA) to measure the reflection coefficient (S11) which is then converted to the corresponding tissue permittivity using either full-wave analysis or through the use of equivalent circuit models. This paper proposes an innovative method of using artificial neural networks (ANN) to convert measured S11 to tissue permittivity, circumventing the requirement of extending the VNA measurement plane to the coaxial line open end. The conventional three-step calibration technique used with coaxial open-ended probes lacks repeatability, unless applied with extreme care by experienced persons, and is not adaptable to alternative sensor antenna configurations necessitated by many potential diagnostic and monitoring applications. The method being proposed does not require calibration at the tip of the probe, thus simplifying the measurement procedure while allowing arbitrary sensor design, and was experimentally validated using S11 measurements and the corresponding complex permittivity of 60 standard liquid and 42 porcine tissue samples. Following ANN training, validation and testing, we obtained a prediction accuracy of 5% for the complex permittivity.