The open-ended coaxial probe (OECP) method is frequently used for the microwave dielectric property (DP) characterization of high permittivity and conductivity materials due to inherent advantages including minimal sample preparation requirements and broadband measurement capabilities. However, the OECP method is known to suffer from high measurement error. One well-known contributor to the high error rates is tissue heterogeneity, which can potentially be managed through the selection of a probe with a proper sensing depth (SD). The SD of the OECP is dependent on many factors including sample DPs and probe aperture diameter. Although the effects of sample DPs on SD have been investigated to some extent in the literature, the probe aperture diameters, particularly small diameters, have not been fully explored. To this end, the SDs of probes with three different apertures (0.5, 0.9 and 2.2 mm-diameters) were analyzed in this study. Probes’ SDs were first investigated with simulations using a double-layered sample configuration (skin tissue and olive oil). Next, experiments were performed using a commercial OECP with a 2.2 mm aperture diameter. The SD was categorized based on 5%, 20% and 80% DP change. Among these threshold values, a 5% DP change was selected as the benchmark for SD categorization. The findings suggest that probes with a smaller aperture size and correspondingly smaller SD should be utilized when measuring the DPs of thin and multilayered samples, such as healthy and diseased skin tissues, to increase the measurement accuracy.

In this work, we proposed a general guideline and a technique for retrieving the complex permittivities (CPs) of material under test (MUT) from the measured reflection coefficients (RCs) obtained with the openended coaxial probe (OECP) through a deep learning model (DLM). Particularly, lossy materials such as phantoms mimicking the biological tissues are considered as MUT in this study. The dataset used to design (train, validate, and test) the DLM is synthetically generated using the relationship between the CPs of the MUT, namely, admittance model, and the fourpole Cole–Cole relaxation model. Moreover, the technique is implemented to accurately predict the CPs of biological tissues with real measured RCs’ data from 0.5 to 20 GHz with a 50-MHz resolution. This technique eliminates the need to physically perform measurements required to create the dataset for DLM and it can be easily implemented for realtime CPs’ measurement of biological tissues. The designed DLM is initially trained, validated, and tested with 80%, 10%, and 10% of the total generated synthetic dataset, respectively. A percent relative error of 1.5 $\pm$ 0.88% is obtained for CPs’ prediction at the test stage. Furthermore, DLM is tested with real RCs’ data measured from four different tissuemimicking phantoms: skin, muscle, blood, and fat. Predicted CPs from DLM are compared with the CPs’ results obtained from a commercially available OECP measurement kit. A (mean) $\pm$ (std.dev) percent relative error ranging from 2.08 $\pm$ 0.4 to 10.84 $\pm$ 2.43 within the frequency band of interest was obtained after comparison.

Dielectric properties of biological tissues are traditionally measured with open-ended coaxial probes. Despite being commercially available for laboratory use, the technique suffers from high measurement error. This prevents the practical applications of the open-ended coaxial probes. One such application is the utilization of the technique for skin cancer detection. To enable a diagnostic tool, there is a need to address the error sources. Among others, tissue heterogeneity is a major contributor to measurement error. The effect of tissue heterogeneity on measurement accuracy can be decreased by quantifying the probe sensing depth. To this end, this work (1) investigates the sensing depth of the 2.2 mm-diameter open-ended coaxial probe for skin mimicking material and (2) offers a simple experimental setup and protocol for sensing depth characterization of open-ended coaxial probes. The sensing depth characterized through simulation and experiments using two double-layered configurations composed to mimic the skin tissue heterogeneity. Three thresholds in percent increase of dielectric property measurements were chosen to determine the sensing depth. Based on the experiment results, it was concluded that the sensing depth was effected by the dielectric property contrast between the layers. That is, high contrast results in rapid change whereas low contrast results in a slower change in measured dielectric properties. It was also concluded that the sensing depth was independent of frequency between 0.5 to 6 GHz and was mostly determined by the material located immediately at the aperture of the probe.

This work presents a technique for dielectric property retrieval through Debye parameter reconstruction from open-ended coaxial probe (OECP) response. Debye parameters were obtained with the application of a deep learning (DL) model to the reflection coefficient response of the OECP when terminated with a material under test. The OECP was modelled with the well-known admittance technique from 0.5 to 6 GHz with 20 MHz resolution. A dataset was generated using the admittance technique and obtained data was utilized to design the DL model. As part of the standard procedure, the dataset was separated to train, validate, and test parts by allocating the 80%, 10%, and 10% of the dataset to each section, respectively. Obtained percent relative error for Debye parameters were 1.86±3.01%, 3.33±9.52%, and 2.07±7.42% for $\epsilon _{s}$ , $\epsilon _\infty $ and $\tau $ , respectively. To further test the constructed DL model, OECP responses were measured at the same frequency band when it was terminated with five different standard liquids, four mixtures, and a gel-like material. Reconstructed Debye parameters from the DL model were used to retrieve the complex dielectric properties and obtained results were compared with the literature data. Obtained mean percent relative error was ranging from 1.21±0.06 to 10.89±0.08 within the frequency band of interest.

This work presents large-scale measurements of in vivo rat breast tissue dielectric properties (DPs) from 0.5 GHz to 6 GHz and classifies the collected data using supervised machine learning (ML) algorithms. The main goals of this work are, first, to report in vivo animal tissue DPs for microwave medical device development and, second, to demonstrate that microwave devices can be utilized for diagnostics. To this end, we separated 18 Sprague-Dawley female rats into control and experimental groups. The experimental group was subjected to chemically induced breast cancer, and the DPs of normal tissues and tumor tissues from the control and experimental groups were measured using the open-ended coaxial probe (OECP) technique. DPs of rat breast tissues are presented with Cole-Cole parameters. The OECP method is preferred since it can collect broadband measurements without sample preparation. Due to these advantages, the method was previously envisioned as a diagnostic tool to aid in biopsy procedures. However, high measurement error prevented the specialized device’s development and, consequently, the clinical deployment of OECP. We demonstrate that high error rates can be mitigated with the application of ML algorithms. Among seven different ML algorithms, the Support Vector Machines (SVM) algorithm classifies rat malignant, benign, and normal tissues with a median accuracy of 94.4 %, Matthews Correlation Coefficient (MCC) of 91.9 %, recall of 94.4 %, precision of 94.9 % and F1 score of 94.4 %.

Dielectric properties of biological materials are commonly characterized with open-ended coaxial probes due to the broadband and non-destructive measurement capabilities. Recently, potential diagnostics applications of the technique have been investigated. Although the technique can successfully classify the tissues with different dielectric properties, the classification accuracy can be improved for tissues with similar dielectric properties. Increase in classification accuracy can be achieved by addressing the error sources. One well-known error source contributing to low measurement accuracy is tissue heterogeneity. To mitigate this error source, there is a need define the probe sensing depth. Such knowledge can enable application-specific probe selection or design. The sensing depth can also be used as an input to the classification algorithms which can potentially improve the tissue classification accuracy. Towards this goal, this work investigates the sensing depth of a commercially available 2.2 mm aperture diameter probe with double-layered configurations using ex vivo rat breast and skin tissues. It was concluded that the dielectric property contrast between the heterogeneous tissue components has an effect on the sensing depth. Also, a membrane layer (between 0.4–0.8 mm thickness) on the rat wet skin tissue and breast tissue will potentially affect the dielectric property measurement results by 52% to 84%.