Microwave Hydration Monitoring: System Assessment Using Fasting Volunteers.

Hydration is an important aspect of human health, as water is a critical nutrient used in many physiological processes. However, there is currently no clinical gold standard for non-invasively assessing hydration status. Recent work has suggested that permittivity in the microwave frequency range provides a physiologically meaningful metric for hydration monitoring. Using a simple time of flight technique for estimating permittivity, this study investigates microwave-based hydration assessment using a population of volunteers fasting during Ramadan. Volunteers are measured throughout the day while fasting during Ramadan and while not fasting after Ramadan. Comparing the estimated changes in permittivity to changes in weight and the time s fails to establish a clear relationship between permittivity and hydration. Assessing the subtle changes in hydration found in a population of sedentary, healthy adults proves difficult and more work is required to determine approaches suitable for tracking subtle changes in hydration over time with microwave-based hydration assessment techniques.

Assessing Patient-Specific Microwave Breast Imaging in Clinical Case Studies.

Microwave breast imaging has seen increasing use in clinical investigations in the past decade with over eight systems having being trialled with patients. The majority of systems use radar-based algorithms to reconstruct the image shown to the clinician which requires an estimate of the dielectric properties of the breast to synthetically focus signals to reconstruct the image. Both simulated and experimental studies have shown that, even in simplified scenarios, misestimation of the dielectric properties can impair both the image quality and tumour detection. Many methods have been proposed to address the issue of the estimation of dielectric properties, but few have been tested with patient images. In this work, a leading approach for dielectric properties estimation based on the computation of many candidate images for microwave breast imaging is analysed with patient images for the first time. Using five clinical case studies of both healthy breasts and breasts with abnormalities, the advantages and disadvantages of computational patient-specific microwave breast image reconstruction are highlighted.

Conformal and Disposable Antenna-Based Sensor for Non-Invasive Sweat Monitoring.

This paper presents a feasibility study for a non-wearable, conformal, low cost, and disposable antenna-based sensor for non-invasive hydration monitoring using sweat. It is composed of a patch antenna implemented on a cellulose filter paper substrate and operating in the range 2⁻4 GHz. The paper substrate can absorb liquids, such as sweat on the skin, through two slots incorporated within the antenna structure. Thus, the substrate dielectric properties are altered according to the properties of the absorbed liquid. Changes in reflection-based measurements are used to analyze salt solutions and artificial sweat, specifically the amount of sampled solution and the sodium chloride (NaCl) concentration. Using the shift in resonant frequency and magnitude of the reflection coefficient, NaCl concentrations in the range of 8.5⁻200 mmol/L, representing different hydration states, are detected. The measurements demonstrate the feasibility of using microwave based measurements for hydration monitoring using sweat.

Adaptive Monostatic System for Measuring Microwave Reflections from the Breast.

A second-generation monostatic radar system to measure microwave reflections from the human breast is presented and analyzed. The present system can measure the outline of the breast with an accuracy of ±1 mm and precisely place the microwave sensor in an adaptive matter such that microwaves are normally incident on the skin. Microwave reflections are measured between 10 MHz to 12 GHz with sensitivity of 65 to 75 dB below the input power and a total scan time of 30 min for 140 locations. The time domain reflections measured from a volunteer show fidelity above 0.98 for signals in a single scan. Finally, multiple scans of a breast phantoms demonstrate the consistency of the system in terms of recorded reflection, outline measurement, and image reconstruction.

Evaluation of Image Reconstruction Algorithms for Confocal Microwave Imaging: Application to Patient Data.

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.

Surface Estimation for Microwave Imaging.

Biomedical imaging and sensing applications in many scenarios demand accurate surface estimation from a sparse set of noisy measurements. These measurements may arise from a variety of sensing modalities, including laser or electromagnetic samples of an object's surface. We describe a state-of-the-art microwave imaging prototype that has sensors to acquire both microwave and laser measurements. The approach developed to translate sparse samples of the breast surface into an accurate estimate of the region of interest is detailed. To evaluate the efficacy of the method, laser and electromagnetic samples are acquired by sensors from three realistic breast models with varying sizes and shapes. A set of metrics is developed to assist with the investigation and demonstrate that the algorithm is able to accurately estimate the shape of a realistic breast phantom when only a sparse set of data are available. Moreover, the algorithm is robust to the presence of measurement noise, and is effective when applied to measurement scans of patients acquired with the prototype.

Average dielectric property analysis of complex breast tissue with microwave transmission measurements.

Prior information about the average dielectric properties of breast tissue can be implemented in microwave breast imaging techniques to improve the results. Rapidly providing this information relies on acquiring a limited number of measurements and processing these measurement with efficient algorithms. Previously, systems were developed to measure the transmission of microwave signals through breast tissue, and simplifications were applied to estimate the average properties. These methods provided reasonable estimates, but they were sensitive to multipath. In this paper, a new technique to analyze the average properties of breast tissues while addressing multipath is presented. Three steps are used to process transmission measurements. First, the effects of multipath were removed. In cases where multipath is present, multiple peaks were observed in the time domain. A Tukey window was used to time-gate a single peak and, therefore, select a single path through the breast. Second, the antenna response was deconvolved from the transmission coefficient to isolate the response from the tissue in the breast interior. The antenna response was determined through simulations. Finally, the complex permittivity was estimated using an iterative approach. This technique was validated using simulated and physical homogeneous breast models and tested with results taken from a recent patient study.

Microwave imaging for monitoring breast cancer treatment: A pilot study.

BACKGROUND: Microwave imaging has been proposed for medical applications, creating maps related to water content of tissues. Breast imaging has emerged as a key application because the signals can be coupled directly into the breast and experience limited attenuation in fatty tissues. While the literature contains reports of tumor detection with microwave approaches, there is limited exploration of treatment monitoring. PURPOSE: This study aims to detect treatment-related changes in breast tissue with a low-resolution microwave scanner. METHODS: Microwave scans of 15 patients undergoing treatment for early-stage breast cancer are collected at up to 4 time points: after surgery (baseline), 6 weeks after accelerated partial breast radiation, as well as 1 and 2 years post-treatment. Both the treated and untreated breast are scanned at each time point. The microwave scanner consists of planar transmit and receive arrays and uses signals from 0.1 to 10 GHz. The average microwave frequency properties (permittivity) are calculated for each scan to enable quantitative comparison. Baseline and 6-week results are analyzed with a two-way ANOVA with blocking. RESULTS: Consistent properties are observed for the untreated breast over time, similar to a previous study. Comparison of the scans of the treated and untreated breast suggests increased properties related to treatment, particularly at baseline and 6-weeks following radiotherapy. Analysis of the average properties of the scans with ANOVA indicates statistically significant differences ( p < 0.05 $p < 0.05$ ) in the treated and untreated breast at these time points. CONCLUSIONS: Microwave imaging has the potential to track treatment-related changes in breast tissues.

Safety assessment of ultra-wideband antennas for microwave breast imaging.

This article deals with the safety assessment of several ultra-wideband (UWB) antenna designs for use in prototype microwave breast imaging systems. First, the performances of the antennas are validated by comparison of measured and simulated data collected for a simple test case. An efficient approach to estimating the specific energy absorption (SA) is introduced and validated. Next, SA produced by the UWB antennas inside more realistic breast models is computed. In particular, the power levels and pulse repetition periods adopted for the SA evaluation follow the measurement protocol employed by a tissue sensing adaptive radar (TSAR) prototype system. Results indicate that the SA for the antennas examined is below limits prescribed in standards for exposure of the general population; however, the difficulties inherent in applying such standards to UWB exposures are discussed. The results also suggest that effective tools for the rapid evaluation of new sensors have been developed.

Breast tissue mimicking phantoms for combined ultrasound and microwave imaging.

We present a new formulation for a breast tissue-mimicking phantom for combined microwave and ultrasound imaging to assist breast cancer detection. Formulations based on coconut oil, canola oil, agar and glass beads were used to mimic skin and fat tissues. First, 36 recipes were fabricated, and properties were measured to determine the relationship and possible interaction between ingredients with the ultrasound and microwave properties. Based on these results, the formulae were developed to mimic different tissues found in breast, including skin, fat, fibroglandular, and tumour tissues. All phantoms contained a base of agar and glass beads at different proportions depending on the tissue mimicked. Tumour and fibroglandular tissues were best mimicked by adding polyvinylpyrrolidone (PVP), while using coconut oil for skin and canola oil for fat produced the best results. Five final phantoms with different internal structures were fabricated and imaged using B-mode ultrasound and a microwave transmission system. Microwave permittivity maps were obtained from the microwave system and compared to ultrasound images. The structure and composition of the phantoms were all confirmed through this microwave and ultrasound imaging.

Evaluating Performance of Microwave Image Reconstruction Algorithms: Extracting Tissue Types with Segmentation Using Machine Learning.

Evaluating the quality of reconstructed images requires consistent approaches to extracting information and applying metrics. Partitioning medical images into tissue types permits the quantitative assessment of regions that contain a specific tissue. The assessment facilitates the evaluation of an imaging algorithm in terms of its ability to reconstruct the properties of various tissue types and identify anomalies. Microwave tomography is an imaging modality that is model-based and reconstructs an approximation of the actual internal spatial distribution of the dielectric properties of a breast over a reconstruction model consisting of discrete elements. The breast tissue types are characterized by their dielectric properties, so the complex permittivity profile that is reconstructed may be used to distinguish different tissue types. This manuscript presents a robust and flexible medical image segmentation technique to partition microwave breast images into tissue types in order to facilitate the evaluation of image quality. The approach combines an unsupervised machine learning method with statistical techniques. The key advantage for using the algorithm over other approaches, such as a threshold-based segmentation method, is that it supports this quantitative analysis without prior assumptions such as knowledge of the expected dielectric property values that characterize each tissue type. Moreover, it can be used for scenarios where there is a scarcity of data available for supervised learning. Microwave images are formed by solving an inverse scattering problem that is severely ill-posed, which has a significant impact on image quality. A number of strategies have been developed to alleviate the ill-posedness of the inverse scattering problem. The degree of success of each strategy varies, leading to reconstructions that have a wide range of image quality. A requirement for the segmentation technique is the ability to partition tissue types over a range of image qualities, which is demonstrated in the first part of the paper. The segmentation of images into regions of interest corresponding to various tissue types leads to the decomposition of the breast interior into disjoint tissue masks. An array of region and distance-based metrics are applied to compare masks extracted from reconstructed images and ground truth models. The quantitative results reveal the accuracy with which the geometric and dielectric properties are reconstructed. The incorporation of the segmentation that results in a framework that effectively furnishes the quantitative assessment of regions that contain a specific tissue is also demonstrated. The algorithm is applied to reconstructed microwave images derived from breasts with various densities and tissue distributions to demonstrate the flexibility of the algorithm and that it is not data-specific. The potential for using the algorithm to assist in diagnosis is exhibited with a tumor tracking example. This example also establishes the usefulness of the approach in evaluating the performance of the reconstruction algorithm in terms of its sensitivity and specificity to malignant tissue and its ability to accurately reconstruct malignant tissue.

Study of the Dielectric Properties of Artificial Sweat Mixtures at Microwave Frequencies.

Analysis of sweat is of interest for a variety of diagnosis and monitoring applications in healthcare. In this work, detailed measurements of the dielectric properties of solutions representing the major components of sweat are presented. The measurements include aqueous solutions of sodium chloride (NaCl), potassium chloride (KCl), urea, and lactic acid, as well as their mixtures. Moreover, mixtures of NaCl, KCl, urea, and lactic acid, mimicking artificial sweat at different hydration states, are characterized, and the data are fitted to a Cole-Cole model. The complex dielectric permittivity for all prepared solutions and mixtures is studied in the range of 1-20 GHz, at temperature of 23 °C, with ionic concentrations in the range of 0.01-1.7 mol/L.

Breast surface estimation for radar-based breast imaging systems.

Radar-based microwave breast-imaging techniques typically require the antennas to be placed at a certain distance from or on the breast surface. This requires prior knowledge of the breast location, shape, and size. The method proposed in this paper for obtaining this information is based on a modified tissue sensing adaptive radar algorithm. First, a breast surface detection scan is performed. Data from this scan are used to localize the breast by creating an estimate of the breast surface. If required, the antennas may then be placed at specified distances from the breast surface for a second tumor-sensing scan. This paper introduces the breast surface estimation and antenna placement algorithms. Surface estimation and antenna placement results are demonstrated on three-dimensional breast models derived from magnetic resonance images.

Anthropomorphic breast model repository for research and development of microwave breast imaging technologies.

A repository of anthropomorphic numerical breast models is made available for the scientific community to support research and development of microwave imaging technologies for diagnostic and therapeutic applications. These models are constructed from magnetic resonance imaging (MRI) scans acquired at our university hospital. Our 3D breast modelling method is used to translate the MRI scans into 3D models representing the geometry and microwave-frequency properties of tissues in the breast. The reconstructed models demonstrate anatomical realism, reconfigurable complexity, and flexibility to adapt to simulations of various microwave imaging techniques and prototype systems. With these models, realistic and rigorous test scenarios can be defined in simulations to support feasibility analysis, performance verification and design improvements of developing microwave imaging techniques, prior to testing on experimental systems. A repository of breast models is created which includes breasts of varying classification - fatty, scattered, heterogeneous, and dense. In addition, the models include brief documentation to facilitate researchers in selecting a model by matching its features with their requirements.

Automated 3D method for the construction of flexible and reconfigurable numerical breast models from MRI scans.

Anatomically realistic numerical breast models are essential tools for microwave breast imaging, supporting feasibility analysis, performance verification, and design improvements. Patient-specific models also assist in interpreting the results of the patient studies conducted on microwave imaging prototype systems. The proposed method employs automated and robust 3D processing techniques to construct flexible and reconfigurable breast models. These techniques include noise and artifact suppression with a principal component analysis (PCA) approach, and oversampling of the magnetic resonance imaging (MRI) data to enhance the intensity continuity. The k-means clustering segmentation identifies fatty and fibroglandular tissues and further segments these regions into a selected number of tissues, providing reconfigurable models. A peak Gaussian fitting technique maps the model clusters to the dielectric properties. The robustness of the proposed method is verified by applying it to both 1.5- and 3-T MRI scans as well as to scans of varying breast densities.

Metrics for Assessing the Similarity of Microwave Breast Imaging Scans of Healthy Volunteers.

Microwave radar imaging is promising as a complementary medical imaging modality. However, the unique nature of the images means interpretation can be difficult. As a result, it is important to understand the sources of image differences, and how much variability is inherent in the imaging system itself. To address this issue, we compare the effectiveness of six different measures of image similarity for quantifying the similarity (or difference) between two microwave radar images. The structural similarity index has become the de facto standard for image comparison, but we propose that useful information can be acquired from a measure known as the Modified Hausdorff Distance. We apply each measure to image pairs from sequential scans of both phantoms and volunteers. We find that rather than using a single value to quantify the image similarity, by computing a number of values that are designed to capture different image aspects, we can better assess the ways in which the images differ.

Engineering Approaches to Assessing Hydration Status.

Dehydration is a common condition characterized by a decrease in total body water. Acute dehydration can cause physical and cognitive impairment, heat stroke and exhaustion, and, if severe and uncorrected, even death. The health effects of chronic mild dehydration are less well studied with urolithiasis (kidney stones) the only condition consistently associated with it. Aside from infants and those with particular medical conditions, athletes, military personnel, manual workers, and older adults are at particular risk of dehydration due to their physical activity, environmental exposure, and/or challenges in maintaining fluid homeostasis. This review describes the different approaches that have been explored for hydration assessment in adults. These include clinical indicators perceived by the patient or detected by a practitioner and routine laboratory analyses of blood and urine. These techniques have variable accuracy and practicality outside of controlled environments, creating a need for simple, portable, and rapid hydration monitoring devices. We review the wide array of devices proposed for hydration assessment based on optical, electromagnetic, chemical, and acoustical properties of tissue and bodily fluids. However, none of these approaches has yet emerged as a reliable indicator in diverse populations across various settings, motivating efforts to develop new methods of hydration assessment.

Integrating prior information into microwave tomography Part 1: Impact of detail on image quality.

PURPOSE: The authors investigate the impact that incremental increases in the level of detail of patient-specific prior information have on image quality and the convergence behavior of an inversion algorithm in the context of near-field microwave breast imaging. A methodology is presented that uses image quality measures to characterize the ability of the algorithm to reconstruct both internal structures and lesions embedded in fibroglandular tissue. The approach permits key aspects that impact the quality of reconstruction of these structures to be identified and quantified. This provides insight into opportunities to improve image reconstruction performance. METHODS: Patient-specific information is acquired using radar-based methods that form a regional map of the breast. This map is then incorporated into a microwave tomography algorithm. Previous investigations have demonstrated the effectiveness of this approach to improve image quality when applied to data generated with two-dimensional (2D) numerical models. The present study extends this work by generating prior information that is customized to vary the degree of structural detail to facilitate the investigation of the role of prior information in image formation. Numerical 2D breast models constructed from magnetic resonance (MR) scans, and reconstructions formed with a three-dimensional (3D) numerical breast model are used to assess if trends observed for the 2D results can be extended to 3D scenarios. RESULTS: For the blind reconstruction scenario (i.e., no prior information), the breast surface is not accurately identified and internal structures are not clearly resolved. A substantial improvement in image quality is achieved by incorporating the skin surface map and constraining the imaging domain to the breast. Internal features within the breast appear in the reconstructed image. However, it is challenging to discriminate between adipose and glandular regions and there are inaccuracies in both the structural properties of the glandular region and the dielectric properties reconstructed within this structure. Using a regional map with a skin layer only marginally improves this situation. Increasing the structural detail in the prior information to include internal features leads to reconstructions for which the interface that delineates the fat and gland regions can be inferred. Different features within the glandular region corresponding to tissues with varying relative permittivity values, such as a lesion embedded within glandular structure, emerge in the reconstructed images. CONCLUSION: Including knowledge of the breast surface and skin layer leads to a substantial improvement in image quality compared to the blind case, but the images have limited diagnostic utility for applications such as tumor response tracking. The diagnostic utility of the reconstruction technique is improved considerably when patient-specific structural information is used. This qualitative observation is supported quantitatively with image metrics.

Integrating prior information into microwave tomography part 2: Impact of errors in prior information on microwave tomography image quality.

PURPOSE: The authors have developed a method to combine a patient-specific map of tissue structure and average dielectric properties with microwave tomography. The patient-specific map is acquired with radar-based techniques and serves as prior information for microwave tomography. The impact that the degree of structural detail included in this prior information has on image quality was reported in a previous investigation. The aim of the present study is to extend this previous work by identifying and quantifying the impact that errors in the prior information have on image quality, including the reconstruction of internal structures and lesions embedded in fibroglandular tissue. This study also extends the work of others reported in literature by emulating a clinical setting with a set of experiments that incorporate heterogeneity into both the breast interior and glandular region, as well as prior information related to both fat and glandular structures. METHODS: Patient-specific structural information is acquired using radar-based methods that form a regional map of the breast. Errors are introduced to create a discrepancy in the geometry and electrical properties between the regional map and the model used to generate the data. This permits the impact that errors in the prior information have on image quality to be evaluated. Image quality is quantitatively assessed by measuring the ability of the algorithm to reconstruct both internal structures and lesions embedded in fibroglandular tissue. The study is conducted using both 2D and 3D numerical breast models constructed from MRI scans. RESULTS: The reconstruction results demonstrate robustness of the method relative to errors in the dielectric properties of the background regional map, and to misalignment errors. These errors do not significantly influence the reconstruction accuracy of the underlying structures, or the ability of the algorithm to reconstruct malignant tissue. Although misalignment errors do not significantly impact the quality of the reconstructed fat and glandular structures for the 3D scenarios, the dielectric properties are reconstructed less accurately within the glandular structure for these cases relative to the 2D cases. However, general agreement between the 2D and 3D results was found. CONCLUSION: A key contribution of this paper is the detailed analysis of the impact of prior information errors on the reconstruction accuracy and ability to detect tumors. The results support the utility of acquiring patient-specific information with radar-based techniques and incorporating this information into MWT. The method is robust to errors in the dielectric properties of the background regional map, and to misalignment errors. Completion of this analysis is an important step toward developing the method into a practical diagnostic tool.

Estimating the Effective Permittivity for Reconstructing Accurate Microwave-Radar Images.

We present preliminary results from a method for estimating the optimal effective permittivity for reconstructing microwave-radar images. Using knowledge of how microwave-radar images are formed, we identify characteristics that are typical of good images, and define a fitness function to measure the relative image quality. We build a polynomial interpolant of the fitness function in order to identify the most likely permittivity values of the tissue. To make the estimation process more efficient, the polynomial interpolant is constructed using a locally and dimensionally adaptive sampling method that is a novel combination of stochastic collocation and polynomial chaos. Examples, using a series of simulated, experimental and patient data collected using the Tissue Sensing Adaptive Radar system, which is under development at the University of Calgary, are presented. These examples show how, using our method, accurate images can be reconstructed starting with only a broad estimate of the permittivity range.