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.

Microwave Breast Lesion Classification - Results from Clinical Investigation of the SAFE Microwave Breast Cancer System.

RATIONALE AND OBJECTIVES: Microwave breast cancer imaging (MWI) is an emerging non-invasive technology used to clinically assess the internal breast tissue inhomogeneity. MWI utilizes the variance in dielectric properties of healthy and cancerous tissue to identify anomalies inside the breast and make further clinical predictions. In this study, we evaluate our SAFE MWI system in a clinical setting. Capability of SAFE to provide breast pathology is assessed. MATERIALS AND METHODS: Patients with BI-RADS category 4 or 5 who were scheduled for biopsy were included in the study. Machine learning approach, more specifically the Adaptive Boosting (AdaBoost) model, was implemented to determine if the level of difference between backscattered signals of breasts with the benign and malignant pathological outcome is significant enough for quantitative breast health classification via SAFE. RESULTS: A dataset of 113 (70 benign and 43 malignant) breast samples was used in the study. The proposed classification model achieved the sensitivity, specificity, and accuracy of 79%, 77%, and 78%, respectively. CONCLUSION: The non-ionizing and non-invasive nature gives SAFE an opportunity to impact breast cancer screening and early detection positively. Device classified both benign and malignant lesions at a similar rate. Further clinical studies are planned to validate the findings of this study.

Monitoring of intracerebral hemorrhage with a linear microwave imaging algorithm.

Intracerebral hemorrhage is a life-threatening condition where conventional imaging modalities such as CT and MRI are indispensable in diagnosing. Nevertheless, monitoring the evolution of intracerebral hemorrhage still poses a technological challenge. We consider continuous monitoring of intracerebral hemorrhage in this context and present a differential microwave imaging scheme based on a linearized inverse scattering. Our aim is to reconstruct non-anatomical maps that reveal the volumetric evolution of hemorrhage by using the differences between consecutive electric field measurements. This approach can potentially allow the monitoring of intracerebral hemorrhage in a real-time and cost-effective manner. Here, we devise an indicator function, which reveals the position, volumetric growth, and shrinkage of hemorrhage. Later, the method is numerically tested via a 3D anthropomorphic dielectric head model. Through several simulations performed for different locations of intracerebral hemorrhage, the indicator function-based technique is demonstrated to be capable of detecting the changes accurately. Finally, the robustness under noisy conditions is analyzed to assess the feasibility of the method. This analysis suggests that the method can be used to monitor the evolution of intracerebral hemorrhage in real-world scenarios.

Gradient-Boosting Algorithm for Microwave Breast Lesion Classification-SAFE Clinical Investigation.

(1) Background: Microwave breast imaging (MBI) is a promising breast-imaging technology that uses harmless electromagnetic waves to radiate the breast and assess its internal structure. It utilizes the difference in dielectric properties of healthy and cancerous tissue, as well as the dielectric difference between different cancerous tissue types to identify anomalies inside the breast and make further clinical predictions. In this study, we evaluate the capability of our upgraded MBI device to provide breast tissue pathology. (2) Methods: Only patients who were due to undergo biopsy were included in the study. A machine learning (ML) approach, namely Gradient Boosting, was used to understand information from the frequency spectrum, collected via SAFE, and provide breast tissue pathology. (3) Results: A total of 54 patients were involved in the study: 29 of them had benign and 25 had malignant findings. SAFE acquired 20 true-positive, 24 true-negative, 4 false-positive and 4 false-negative findings, achieving the sensitivity, specificity and accuracy of 80%, 83% and 81%, respectively. (4) Conclusions: The use of harmless tissue radiation indicates that SAFE can be used to provide the breast pathology of women of any age without safety restrictions. Results indicate that SAFE is capable of providing breast pathology at a high rate, encouraging further clinical investigations.

Rotationally Adjustable Hyperthermia Applicators: A Computational Comparative Study of Circular and Linear Array Applicators.

Microwave breast hyperthermia (MH) aims to increase the temperature at the tumor location with minimal change in the healthy tissue. To this end, the specific absorption rate (SAR) inside the breast is optimized. The choice of the MH applicator design is important for a superior energy focus on the target. Although hyperthermia treatment planning (HTP) changes for every patient, the MH applicator is required to be effective for different breast models and tumor types. The linear applicator (LA) is one of the previously proposed applicator designs with linearly arranged antennas; however, it suffers from low focusing ability in certain breast regions due to its unsymmetrical geometrical features. In this paper, we propose to radially adjust the LA to obtain alternative excitation schemes without actually changing the applicator. Antipodal Vivaldi antennas were utilized, and the antenna excitations were optimized with particle swarm optimization (PSO). The comparison of the rotated and the fixed linear applicator, between 12-antenna circular and linear applicators, and finally, between a 24-antenna circular applicator are provided. Within the 12 rotation angles and two target locations that were analyzed, the 135° axially rotated linear applicator gave a 35% to 84% higher target-to-breast SAR ratio (TBRS) and a 21% to 28% higher target-to-breast temperature ratio (TBRT) than the fixed linear applicator. For the deep-seated target, the 135° rotated linear applicator had an 80% higher TBRS and a 59% higher TBRT than the 12-antenna circular applicator, while the results were comparable to the 24-antenna circular applicator.

Antenna Excitation Optimization with Deep Learning for Microwave Breast Cancer Hyperthermia.

Microwave hyperthermia (MH) requires the effective calibration of antenna excitations for the selective focusing of the microwave energy on the target region, with a nominal effect on the surrounding tissue. To this end, many different antenna calibration methods, such as optimization techniques and look-up tables, have been proposed in the literature. These optimization procedures, however, do not consider the whole nature of the electric field, which is a complex vector field; instead, it is simplified to a real and scalar field component. Furthermore, most of the approaches in the literature are system-specific, limiting the applicability of the proposed methods to specific configurations. In this paper, we propose an antenna excitation optimization scheme applicable to a variety of configurations and present the results of a convolutional neural network (CNN)-based approach for two different configurations. The data set for CNN training is collected by superposing the information obtained from individual antenna elements. The results of the CNN models outperform the look-up table results. The proposed approach is promising, as the phase-only optimization and phase-power-combined optimization show a 27% and 4% lower hotspot-to-target energy ratio, respectively, than the look-up table results for the linear MH applicator. The proposed deep-learning-based optimization technique can be utilized as a protocol to be applied on any MH applicator for the optimization of the antenna excitations, as well as for a comparison of MH applicators.

Single-slice microwave imaging of breast cancer by reverse time migration.

PURPOSE: Microwave imaging of breast cancer is considered and a new microwave imaging prototype including the imaging algorithm, the antenna array, and the measurement configuration is presented. The prototype aims to project the geometrical features of the anomalies inside the breast to a single-slice image at the coronal plane depending on the complex dielectric permittivity variation among the tissues to aid the diagnosis. METHODS: The imaging prototype uses a solid cylindrical dielectric platform, where a total of 24 optimized Vivaldi antennas are embedded inside to form a uniform circular antenna array. The center of the platform is carved to create a hollow part for placement of the breast and the multistatic, microwave scattering parameters are collected with the antenna array around the hollow center. The dielectric platform further enhances the microwave impedance matching against the breast fat tissue and preserves the vertical polarization during the measurements. In the imaging phase, a computationally efficient inverse electromagnetic scattering method-reverse time migration (RTM)-is considered and adapted in terms of scattering parameters to comply with the actual measurements. RESULTS: The prototype system is experimentally tested against tissue-mimicking breast phantoms with realistic dielectric permittivity profiles. The reconstructed single-slice images accurately determined the locations and the geometrical extents of the tumor phantoms. These experiments not only verified the microwave imaging prototype but also provided the first experimental results of the imaging algorithm. CONCLUSIONS: The presented prototype system implementing the RTM method is capable of reconstructing single-slice, nonanatomical images, where the hotspots correspond to the geometrical projections of the anomalies inside the breast.

Characterization of Open-Ended Coaxial Probe Sensing Depth with Respect to Aperture Size for Dielectric Property Measurement of Heterogeneous Tissues.

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.

Classification of rat mammary carcinoma with large scale in vivo microwave measurements.

Mammary carcinoma, breast cancer, is the most commonly diagnosed cancer type among women. Therefore, potential new technologies for the diagnosis and treatment of the disease are being investigated. One promising technique is microwave applications designed to exploit the inherent dielectric property discrepancy between the malignant and normal tissues. In theory, the anomalies can be characterized by simply measuring the dielectric properties. However, the current measurement technique is error-prone and a single measurement is not accurate enough to detect anomalies with high confidence. This work proposes to classify the rat mammary carcinoma, based on collected large-scale in vivo S[Formula: see text] measurements and corresponding tissue dielectric properties with a circular diffraction antenna. The tissues were classified with high accuracy in a reproducible way by leveraging a learning-based linear classifier. Moreover, the most discriminative S[Formula: see text] measurement was identified, and to our surprise, using the discriminative measurement along with a linear classifier an 86.92% accuracy was achieved. These findings suggest that a narrow band microwave circuitry can support the antenna enabling a low-cost automated microwave diagnostic system.

A Novel Approach on Microwave Hyperthermia.

Microwave hyperthermia (MH) requires the selective focusing of microwave energy on the targeted region while minimally affecting the healthy tissue. Emerging from the simple nature of the linear antenna arrays, this work demonstrates focusing maps as an application guide for MH focusing by adjusting the antenna phase values. The focusing of the heating potential (HP) on different density breast models is performed via the proposed method using Vivaldi antennas. The effect of the tumor conductivity on the focusing is discussed. As a straightforward approach and utilizing the Vivaldi antennas, the system can be further combined with MH monitoring application.

SAFE: A Novel Microwave Imaging System Design for Breast Cancer Screening and Early Detection-Clinical Evaluation.

SAFE (Scan and Find Early) is a novel microwave imaging device intended for breast cancer screening and early detection. SAFE is based on the use of harmless electromagnetic waves and can provide relevant initial diagnostic information without resorting to X-rays. Because of SAFE's harmless effect on organic tissue, imaging can be performed repeatedly. In addition, the scanning process itself is not painful since breast compression is not required. Because of the absence of physical compression, SAFE can also detect tumors that are close to the thoracic wall. A total number of 115 patients underwent the SAFE scanning procedure, and the resultant images were compared with available magnetic resonance (MR), ultrasound, and mammography images in order to determine the correct detection rate. A sensitivity of 63% was achieved. Breast size influenced overall sensitivity, as sensitivity was lower in smaller breasts (51%) compared to larger ones (74%). Even though this is only a preliminary study, the results show promising concordance with clinical reports, thus encouraging further SAFE clinical studies.

Microwave dielectric property based classification of renal calculi: Application of a kNN algorithm.

The proper management of renal lithiasis presents a challenge, with the recurrence rate of the disease being as high as 46%. To prevent recurrence, the first step is the accurate categorization of the discarded renal calculi. Currently, the discarded renal calculi type is determined with the X-ray powder diffraction method which requires a cumbersome sample preparation. This work presents a new approach that can enable fast and accurate classification of discarded renal calculi with minimal sample preparation requirements. To do so, first, the measurements of the dielectric properties of naturally formed renal calculi are collected with the open-ended contact probe technique between 500 MHz and 6 GHz with 100 MHz intervals. Cole-Cole parameters are fitted to the measured dielectric properties with the generalized Newton-Raphson method. The renal calculi types are classified based on their Cole-Cole parameters as calcium oxalate, cystine, or struvite. The classification is performed using k-nearest neighbors (kNN) machine learning algorithm with the 10 nearest neighbors, where accuracy as high as 98.17% is achieved.

Hand-Held Volatilome Analyzer Based on Elastically Deformable Nanofibers.

This study reports on a hand-held volatilome analyzer for selective determination of clinically relevant biomarkers in exhaled breath. The sensing platform is based on electrospun polymer nanofiber-multiwalled carbon nanotube (MWCNT) sensing microchannels. Polymer nanofibers of poly(vinylidene fluoride) (PVDF), polystyrene (PS), and poly(methyl methacrylate) (PMMA) incorporated with MWCNT exhibits a stable response to interferences of humidity and CO2 and provides selective deformations upon exposure of exhaled breath target volatilomes acetone and toluene, exhibiting correlation to diabetes and lung cancer, respectively. The sensing microchannels "P1" (PVDF-MWCNT), "P2" (PS-MWCNT), and "P3" (PMMA-MWCNT) are integrated with a microfluidic cartridge (µ-card) that facilitates collection and concentration of exhaled breath. The volatilome analyzer consists of a conductivity monitoring unit, signal conditioning circuitries and a low energy display module. A combinatorial operation algorithm was developed for analyzing normalized resistivity changes of the sensing microchannels upon exposure to breath in the concentration ranges between 35 ppb and 3.0 ppm for acetone and 1 ppb and 10 ppm for toluene. Subsequently, responses of volatilomes from individuals in the different risk groups of diabetes were evaluated for validation of the proposed methodology. We foresee that proposed methodology provides an avenue for rapid detection of volatilomes thereby enabling point of care diagnosis in high-risk group individuals.

Machine learning aided diagnosis of hepatic malignancies through in vivo dielectric measurements with microwaves.

In the past decade, extensive research on dielectric properties of biological tissues led to characterization of dielectric property discrepancy between the malignant and healthy tissues. Such discrepancy enabled the development of microwave therapeutic and diagnostic technologies. Traditionally, dielectric property measurements of biological tissues is performed with the well-known contact probe (open-ended coaxial probe) technique. However, the technique suffers from limited accuracy and low loss resolution for permittivity and conductivity measurements, respectively. Therefore, despite the inherent dielectric property discrepancy, a rigorous measurement routine with open-ended coaxial probes is required for accurate differentiation of malignant and healthy tissues. In this paper, we propose to eliminate the need for multiple measurements with open-ended coaxial probe for malignant and healthy tissue differentiation by applying support vector machine (SVM) classification algorithm to the dielectric measurement data. To do so, first, in vivo malignant and healthy rat liver tissue dielectric property measurements are collected with open-ended coaxial probe technique between 500 MHz to 6 GHz. Cole-Cole functions are fitted to the measured dielectric properties and measurement data is verified with the literature. Malign tissue classification is realized by applying SVM to the open-ended coaxial probe measurements where as high as 99.2% accuracy (F1 Score) is obtained.

Realistic microwave breast models through T1-weighted 3-D MRI data.

In this paper we present an effective method for developing realistic numerical three-dimensional (3-D) microwave breast models of different shape, size, and tissue density. These models are especially convenient for microwave breast cancer imaging applications and numerical analysis of human breast-microwave interactions. As in the recent studies on this area, anatomical information of the breast tissue is collected from T1-weighted 3-D MRI data of different patients' in prone position. The method presented in this paper offers significant improvements including efficient noise reduction and tissue segmentation, nonlinear mapping of electromagnetic properties, realistically asymmetric phantom shape, and a realistic classification of breast phantoms. Our method contains a five-step approach where each MRI voxel is classified and mapped to the appropriate dielectric properties. In the first step, the MRI data are denoised by estimating and removing the bias field from each slice, after which the voxels are segmented into two main tissues as fibro-glandular and adipose. Using the distribution of the voxel intensities in MRI histogram, two nonlinear mapping functions are generated for dielectric permittivity and conductivity profiles, which allow each MRI voxel to map to its proper dielectric properties. Obtained dielectric profiles are then converted into 3-D numerical breast phantoms using several image processing techniques, including morphologic operations, filtering. Resultant phantoms are classified according to their adipose content, which is a critical parameter that affects penetration depth during microwave breast imaging.

Surface impedance based microwave imaging method for breast cancer screening: contrast-enhanced scenario.

A new microwave imaging method that uses microwave contrast agents is presented for the detection and localization of breast tumours. The method is based on the reconstruction of breast surface impedance through a measured scattered field. The surface impedance modelling allows for representing the electrical properties of the breasts in terms of impedance boundary conditions, which enable us to map the inner structure of the breasts into surface impedance functions. Later a simple quantitative method is proposed to screen breasts against malignant tumours where the detection procedure is based on weighted cross correlations among impedance functions. Numerical results demonstrate that the method is capable of detecting small malignancies and provides reasonable localization.