David Vrba

Microwave imaging (MWI) offers a promising non-invasive approach for temperature monitoring during hyperthermia (HT) therapy. In this work, we investigate the extension of a clinical superficial HT system to facilitate non-invasive temperature assessment using microwave differential imaging. The integration of certified therapeutic technology with non-invasive monitoring represents an innovative step toward increasing the safety and effectiveness of clinical HT. A major challenge arises from the target region being located beneath a large-area applicator, which complicates the optimal placement of the imaging antennas and reduces system sensitivity. To address this, we evaluated the concept both numerically and experimentally using transmission coefficient thresholding (TCT) and multi-frequency imaging approaches. Experimental investigations focused on TCT index selection and the impact of frequency bandwidth on reconstruction quality. The results show that TCT improves spatial localization and yields a higher reconstruction intensity compared to truncated singular value decomposition (TSVD). Moreover, multi-frequency imaging in the range 0.6 – 0.8 GHz provided optimal performance. For temperature estimation, the single-frequency approach (0.7 GHz) achieved a low temperature estimation error but exhibited a less stable temperature estimation behavior. The multi-frequency approach (0.7 – 1 GHz) showed a higher temperature estimation error but demonstrated improved stability between the estimated and true temperatures. The proposed MWI framework demonstrates significant potential for integration into clinical HT workflows, offering enhanced temperature monitoring capabilities to improve treatment safety and efficacy.

Microwave hyperthermia (MWHT) enhances cancer treatment effectiveness by heating tumor tissues to a temperature range of 40–44°C. This, among other effects, inhibits DNA repair, increases local blood perfusion and oxygenation and makes cancer cells more sensitive to chemotherapy and radiotherapy. The temperature increase is usually achieved by constructive interference of several electromagnetic waves propagating from a phased antenna array surrounding the patient. This work explores the development and validation of a metamaterial (MTM) antenna, a single antenna element for the phased antenna array, based on composite right/left-handed (CRLH) transmission lines, which enables a more flexible antenna size in relation to wavelength and offers benefits such as larger effective field size (EFS) and improved uniformity of specific absorption rate (SAR) distribution. MTM antennas can achieve zeroth-order resonance (ZOR), which can be used to surpass conventional waveguide antennas by delivering more uniform heating profiles and better effective penetration depth (EPD). We have successfully developed and confirmed by measurements the functionality of a novel 70 MHz MTM antenna with an EFS value of 75%. Sensitivity analyses and experimental validations confirm that MTM antennas can be designed to arbitrary frequency with a desired SAR pattern regardless of their geometrical dimensions. As a result, MTM antennas hold the potential to improve hyperthermia outcomes by providing a more controllable, efficient, and patient-tailored approach, ultimately contributing to more effective oncological treatments.

Knowledge of the dielectric parameters of biological tissues is important in a wide range of medical applications of electromagnetic fields. Commercial systems using high-quality vector network analyzers (VNA) are expensive and can be a barrier to the development of existing and emerging applications. Therefore, the main objective of this work was to investigate the potential of using inexpensive VNA, which is available on the market, to measure the dielectric parameters of biological tissues. To accomplish this task, a system for the measurement of dielectric properties of biological tissues in the frequency range from 70 MHz to 3 GHz using inexpensive VNA was developed and presented. The system consists of pocketVNA 2.0 and a custom-made coaxial reflection probe with a diameter of 2.2 mm. The virtual line model of the coaxial probe was used to convert the measured reflection coefficients to the complex permittivity. A total of five ex-vivo tissues (heart, muscle, liver, kidney cortex, and fat) and two standard liquids (isopropanol and ethanol) were measured. The lower accuracy and lower signal-to-noise ratio of the inexpensive VNA were compensated for by post-processing of the measured data, including a time-domain gating and fitting of the evaluated dielectric properties to the Cole-Cole and Debye models for tissues and standard liquids, respectively. The measurement results were compared to the results obtained with a commercial measurement system, and both the standard deviation and mean absolute percentage error (MAPE) are evaluated and presented. The lowest achieved MAPE of biological tissues was for fitted relative permittivity 4.35% (fat, f < 200 MHz) and 0.66% (kidney cortex, f > 200 MHz) and for fitted equivalent electrical conductivity 2.58% (heart, f < 200 MHz) and 3.83% (heart, f > 200 MHz).

For a first time 20 human blood samples with varying glucose concentrations using previously introduced metamaterial microwave (MTM) sensors, which had been already tested with phantoms. The MTM sensors based on composite right/left-handed metamaterial transmission line showed a detectable phase shift in the forward transmission parameter (∠S21) which is related to the blood glucose concentration. Application of a novel biochemical approach introduces separate mathematical models based on gender, reflecting physiological difference in blood, which were further evidenced by measured variations in blood dielectric properties. This contributed to enhanced sensor performance as well as the professional sample preservation system. The accuracy of glucose concentration estimation was analyzed using mean absolute relative difference (MARD) and Parkes error grids. The MARD values for the Female and Male group reached 23 % and 29 % respectively. The findings demonstrate the potential of the MTM sensor for non-invasive glycemia monitoring.

Ultra-wideband (UWB) radar differential imaging has a potential to be one of non-invasive, real-time, and non-ionizing methods for radiofrequency ablation (RFA) monitoring. In this study we developed a novel 3D UWB radar system for monitoring of the ablation zones (AZ) sizes and shapes. The system was first numerically tested on both homogeneous and heterogeneous patient models, followed by corresponding experiments. We applied a 3D Delay-and-Sum (DAS) algorithm to reconstruct and locate AZs, supported by an original method to determine average permittivity for heterogeneous DAS reconstructions. Additionally, an algorithm estimating the AZ size and shape was developed. The system demonstrated high precision with a monitoring error of 1.2 ± 0.9 mm in simulations and 1.3 ± 0.8 mm in ex vivo experiments. AZ volume and shape were approximated with relative errors of 14.8 ± 9.5 % in numerical models and 29.6 ± 24.3 % in experiments. These results indicate that the developed UWB radar system is a promising method for accurate RFA monitoring.

The primary objective of this study is to systematically evaluate the performance of the Support Vector Machine (SVM) algorithm, identifying optimal configurations and appropriate parameters for training and testing data, for microwave brain stroke classification. Using experimentally verified 3D numerical models, a large database of synthetic training and test data has been created with different levels of data variability. These models consist of an antenna array surrounding reconfigurable geometrically and dielectrically realistic human head models Within these models, strokes of varying sizes, types, and dielectric parameters are virtually inserted at different positions in brain within the plane of the antennas. Synthetic data sets have been generated to study the impact of reducing training data, data dimensionality, data format, and algorithm settings. The results of this study confirm that Principal Component Analysis (PCA) dimensionality reduction significantly improved the classification accuracy of the SVM algorithm, and datasets of subjects with smaller strokes appeared to be the most suitable for training. Furthermore, datasets that contain the real and imaginary parts of transmission and reflection coefficients result in the highest classification accuracy. For the current antenna array, the best observed setting and scenarios with high variability in training and test data, close to real clinical scenarios, the ability to accurately classify ischemic strokes and suggest safe initiation of thrombotic therapy is approximately 70%.

The aim of this work was to test microwave brain stroke detection and classification using support vector machines (SVMs). We tested how the nature and variability of training data and system parameters impact the achieved classification accuracy. Using experimentally verified numerical models, a large database of synthetic training and test data was created. The models consist of an antenna array surrounding reconfigurable geometrically and dielectrically realistic human head phantoms with virtually inserted strokes of arbitrary size, and different dielectric parameters in different positions. The generated synthetic data sets were used to test four different hypotheses, regarding the appropriate parameters of the training dataset, the appropriate frequency range and the number of frequency points, as well as the level of subject variability to reach the highest SVM classification accuracy. The results indicate that the SVM algorithm is able to detect the presence of the stroke and classify it (i.e., ischemic or hemorrhagic) even when trained with single-frequency data. Moreover, it is shown that data of subjects with smaller strokes appear to be the most suitable for training accurate SVM predictors with high generalization capabilities. Finally, the datasets created for this study are made available to the community for testing and developing their own algorithms.

Thermal ablation is a well-known method used in interventional radiology to treat cancer. The treatment success is closely related to the exact catheter location in the treated area. Current navigation methods are based mostly on ultrasound or computed tomography. This work explores the possibility of tracking the catheter position during ablation treatment of hepatocellular carcinomas (HCC) using an ultra-wideband (UWB) antenna array and microwave radar imaging based on the “Delay and Sum” (DAS) algorithm. The feasibility was first numerically studied on a simple homogeneous liver model. A heterogeneous anthropomorphic 3D model of the treated region consisting of the main organs within the treated area was then used. Various standard radiofrequency ablation (RFA) catheters were placed virtually in the heterogeneous model. The location and orientation of the antenna elements of the developed imaging system and the applied frequency band were studied. Subsequently, an experimental setup consisting of a 3D printed homogeneous anthropomorphic model, eight UWB dipole antennas, and catheters was created and used in a series of measurements. The average accuracy determining the catheter position from simulated and experimental data was 3.88 ± 0.19 and 6.13 ± 0.66 mm, which are close to the accuracy of clinical navigation systems.