Microwave imaging of human forearms: pilot study and image enhancement

In-Vivo Electrical Properties Estimation of Biological Tissues by Means of a Multi-Step Microwave Tomography Approach

The accurate quantitative estimation of the electromagnetic properties of tissues can serve important diagnostic and therapeutic medical purposes. Quantitative microwave tomography is an imaging modality that can provide maps of the in-vivo electromagnetic properties of the imaged tissues, i.e. both the permittivity and the electric conductivity. A multi-step microwave tomography approach is proposed for the accurate retrieval of such spatial maps of biological tissues. The underlying idea behind the new imaging approach is to progressively add details to the maps in a step-wise fashion starting from single-frequency qualitative reconstructions. Multi-frequency microwave data is utilized strategically in the final stage. The approach results in improved accuracy of the reconstructions compared to inversion of the data in a single step. As a case study, the proposed workflow was tested on an experimental microwave data set collected for the imaging of the human forearm. The human forearm is a good test case as it contains several soft tissues as well as bone, exhibiting a wide range of values for the electrical properties.

UWB-Modulated Microwave Imaging for Human Brain Functional Monitoring

Morphological microwave imaging has shown interesting results on reconstructing biological objects inside the human body, and these parameters represent their actual biological condition, but not their biological activity. In this paper, we propose a novel microwave technique to locate the low-frequency (f≃1 kHz) -modulated signals produced by a microtag mimicking an action potential and proved it in a cylindrical phantom of the brain region. A set of two combined UWB microwave applicators, operating in the 0.5 to 2.5 GHz frequency band and producing a nsec interrogation pulse, is able to focus its radiated field into a small region of the brain containing the microtag with a modulated photodiode. The illuminating UWB microwave field was first modulated by the low-frequency (f≃1 kHz) electrical signal produced by the photodiode, inducing modulated microwave currents into the microtag that reradiating back towards the focusing applicators. At the receiving end, the low-frequency (f≃1 kHz) -modulated signal was first extracted from the full set of the backscattered signals, then focused into the region of interest and spatially represented in the corresponding region of the brain, resulting in a spatial resolution of the images in the order of 10 mm.

Real-Time 3D Microwave Medical Imaging With Enhanced Variational Born Iterative Method

In this paper, we present a new variational Born iterative method (VBIM) for real-time microwave imaging (MWI) applications. The S-parameter volume integral equation and waveport vector Green's function are implemented to utilize the measured signal of the MWI system. Meanwhile, the real and imaginary separation (RIS) approach is used at each iterative step to simultaneously reconstruct the dielectric permittivity and conductivity of unknown objects. Compared with the Born iterative method and distorted Born iterative method, VBIM requires less computational time to reach the convergence threshold. The graphics processing unit based acceleration technique is implemented for real-time imaging. To demonstrate the efficiency and accuracy of this VBIM-RIS method, synthetic analysis of a complex multi-layer spherical phantom is first conducted. Then, the algorithm is tested with measured data using our new MWI system prototype. Finally, a synthetic brain-tumor phantom model under a thermal therapy procedure is monitored to exemplify the real-time imaging with about 5 seconds per reconstruction frame.

Multi-Element UWB Probe Optimization for Medical Microwave Imaging

The need for non-ionizing techniques for medical imaging applications has led to the use of microwave signals. Several systems have been introduced in recent years based on increasing the number of antennas and frequency bandwidth to obtain high resolution and good accuracy in locating objects. A novel microwave imaging system that reduces the number of required antennas for precise target location appropriate for medical applications is presented. The proposed system consists of four UWB extended gap ridge horn (EGRH) antennas covering the frequency band from 0.5 GHz to 1.5 GHz mounted on a cylindrical phantom that mimics the brain in an orthogonal set of two EGRH probes. This configuration has the ability to control both the longitudinal and transversal dimensions of the reconstructed target's image, rather than controlling the spatial resolution, by increasing the frequency band that can be easily affected by medium losses. The system is tested numerically and experimentally by the detection of a cylindrical target within a human brain model.

Microwave Antenna System for Muscle Rupture Imaging with a Lossy Gel to Reduce Multipath Interference

Injuries to the hamstring muscles are an increasing problem in sports. Imaging plays a key role in diagnosing and managing athletes with muscle injuries, but there are several problems with conventional imaging modalities with respect to cost and availability. We hypothesized that microwave imaging could provide improved availability and lower costs and lead to improved and more accurate diagnostics. In this paper, a semicircular microwave imaging array with eight antennae was investigated. A key component in this system is the novel antenna design, which is based on a monopole antenna and a lossy gel. The purpose of the gel is to reduce the effects of multipath signals and improve the imaging quality. Several different gels have been manufactured and evaluated in imaging experiments. For comparison, corresponding simulations were performed. The results showed that the gels can effectively reduce the multipath signals and the imaging experiments resulted in significantly more stable and repeatable reconstructions when a lossy gel was used compared to when an almost non-lossy gel was used.

A Versatile and Shelf-Stable Dielectric Coupling Medium for Microwave Imaging

OBJECTIVE

To develop a new class of emulsions using a protein-based emulsifier as the coupling fluid for microwave imaging systems.

METHODS

In this paper, we provide a theoretical basis for engineering shelf-stable dielectric fluids, a step-by-step formulation method, and measurements of complex dielectric properties in the frequency range of 0.5-3 GHz, which can be applicable for many of the recent microwave imaging systems.

RESULTS

This medium was primarily designed for long-term stability while providing a controllable range of complex dielectric permittivities given different fractions of its constituents. Consequently, this emulsion shows dielectric stability in open air throughout a 7-day experiment and temperature insensitivity over the range of 0 to 60 Celsius degree.

CONCLUSIONS

This control over dielectric permittivity enables formulations that tune the background-to-target contrast to the linearizable regime of iterative inverse scattering algorithms. Accordingly, the emulsion conductivity can also be controlled and reduced to maintain the required signal-to-noise ratio within the dynamic range of the imaging system. The new formulation overcomes the practical challenges of engineering coupling fluids for microwave imaging systems, e.g., temporal stability, non-toxic, low sensitivity to temperature variation, and easy formulation from readily available and inexpensive materials.

SIGNIFICANCE

The achieved properties associated with this new fluid are of particular benefit to microwave imaging systems used in thermal therapy monitoring.

Monitoring Bone Density Using Microwave Tomography of Human Legs: A Numerical Feasibility Study

A major cause of bone mass loss worldwide is osteoporosis. X-ray is considered to be the gold-standard technique to diagnose this disease. However, there is currently a need for an alternative modality due to the ionizing radiations used in X-rays. In this vein, we conducted a numerical study herein to investigate the feasibility of using microwave tomography (MWT) to detect bone density variations that are correlated to variations in the complex relative permittivity within the reconstructed images. This study was performed using an in-house finite-element method contrast source inversion algorithm (FEM-CSI). Three anatomically-realistic human leg models based on magnetic resonance imaging reconstructions were created. Each model represents a leg with a distinct fat layer thickness; thus, the three models are for legs with thin, medium, and thick fat layers. In addition to using conventional matching media in the numerical study, the use of commercially available and cheap ultrasound gel was evaluated prior to bone image analysis. The inversion algorithm successfully localized bones in the thin and medium fat scenarios. In addition, bone volume variations were found to be inversely proportional to their relative permittivity in the reconstructed images with the root mean square error as low as 2.54. The observations found in this study suggest MWT as a promising bone imaging modality owing to its safe and non-ionizing radiations used in imaging objects with high quality.

Enhancing image quality of single-frequency microwave imaging with a multistatic full-view array based on sidelobe reduction

Single-frequency microwave imaging can be effectively realized with multistatic full-view arrays, offering great potential in various sensing applications. In this paper, we address the problem of forming high quality images with the focus on multistatic full-view arrays. We aim to enhance its image quality by means of reducing the side-lobe level (SLL) of the imaging array. K-space representation and PSF analysis are presented to get an insight into the effect of low spatial frequency samples collected by the array on the side-lobe response of the array. Based on this understanding, a novel SLL reduction method is proposed based on weakening the effect of low spatial frequency samples. A modified back-projection algorithm is suggested to apply the proposed SLL reduction method in image reconstruction. Numerical simulations confirm a reduction of about 5 dB in side-lobe level. The functionality of the proposed method is verified by using the experimental measurement data of two different targets. Image quality is enhanced by 3.5 and 4.5 dB in terms of signal-to-mean ratio (SMR) for the two studied targets. This considerable improvement has resulted in avoiding appearance of artifacts and wrong interpretations of the target under imaging. The proposed method can be beneficial for existing imaging systems that utilize a full-view multistatic array, from medical to industrial applications.

A Machine Learning Workflow for Tumour Detection in Breasts Using 3D Microwave Imaging

A two-stage workflow for detecting and monitoring tumors in the human breast with an inverse scattering-based technique is presented. Stage 1 involves a phaseless bulk-parameter inference neural network that recovers the geometry and permittivity of the breast fibroglandular region. The bulk parameters are used for calibration and as prior information for Stage 2, a full phase contrast source inversion of the measurement data, to detect regions of high relative complex-valued permittivity in the breast based on an assumed known overall tissue geometry. We demonstrate the ability of the workflow to recover the geometry and bulk permittivity of the different sized fibroglandular regions, and to detect and localize tumors of various sizes and locations within the breast model. Preliminary results show promise for a synthetically trained Stage 1 network to be applied to experimental data and provide quality prior information in practical imaging situations.

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.

Microwave Bone Imaging: A Preliminary Investigation on Numerical Bone Phantoms for Bone Health Monitoring

Microwave tomography (MWT) can be used as an alternative modality for monitoring human bone health. Studies have found a significant dielectric contrast between healthy and diseased human trabecular bones. A set of diverse bone phantoms were developed based on single-pole Debye parameters of osteoporotic and osteoarthritis human trabecular bones. The bone phantoms were designed as a two-layered circular structure, where the outer layer mimics the dielectric properties of the cortical bone and the inner layer mimics the dielectric properties of the trabecular bone. The electromagnetic (EM) inverse scattering problem was solved using a distorted Born iterative method (DBIM). A compressed sensing-based linear inversion approach referred to as iterative method with adaptive thresholding for compressed sensing (IMATCS) has been employed for solving the underdetermined set of linear equations at each DBIM iteration. To overcome the challenges posed by the ill-posedness of the EM inverse scattering problem, the L2-based regularization approach was adopted in the amalgamation of the IMATCS approach. The simulation results showed that osteoporotic and osteoarthritis bones can be differentiated based on the reconstructed dielectric properties even for low values of the signal-to-noise ratio. These results show that the adopted approach can be used to monitor bone health based on the reconstructed dielectric properties.

A Tomograph Prototype for Quantitative Microwave Imaging: Preliminary Experimental Results

A new prototype of a tomographic system for microwave imaging is presented in this paper. The target being tested is surrounded by an ad-hoc 3D-printed structure, which supports sixteen custom antenna elements. The transmission measurements between each pair of antennas are acquired through a vector network analyzer connected to a modular switching matrix. The collected data are inverted by a hybrid nonlinear procedure combining qualitative and quantitative reconstruction algorithms. Preliminary experimental results, showing the capabilities of the developed system, are reported.

Design and Experimental Validation of a Multiple-Frequency Microwave Tomography System Employing the DBIM-TwIST Algorithm

We present a first prototype of a wideband microwave tomography system with potential application to medical imaging. The system relies on a compact and robust printed monopole antenna which can operate in the 1.0–3.0 GHz range when fully immersed in commonly used coupling liquids, such as glycerine–water solutions. By simulating the proposed imaging setup in CST Microwave Studio, we study the signal transmission levels and array sensitivity for different target and coupling liquid media. We then present the experimental prototype design and data acquisition process, and show good agreement between experimentally measured data and results from the CST simulations. We assess imaging performance by applying our previously proposed two-dimensional (2-D) DBIM TwIST-algorithm to both simulated and experimental datasets, and demonstrate that the system can reconstruct simple cylindrical targets at multiple frequencies.

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.

A microwave imaging-based technique to localize an in-body RF source for biomedical applications

In some biomedical applications such as wireless capsule endoscopy, the localization of an in-body radio-frequency (RF) source is important for the positioning of any abnormality inside the gastrointestinal tract. With knowledge of the location, therapeutic operations can be performed precisely at the position of the abnormality. Electrical properties (relative permittivity and conductivity) of the tissues and their distribution are utilized to estimate the position. This paper presents a method for the localization of an in-body RF source based on microwave imaging. The electrical properties of the tissues and their distribution at 403.5 MHz are found from microwave imaging and the position of an RF source is then estimated based on the image. The method is applied on synthetic noisy data, obtained after the addition of white Gaussian noise to simulated data of a simple circular phantom, and a realistic phantom in a 2-D case. The root-mean-square of the error distance between the actual and the estimated position is found to be within 10 and 4 mm for the circular and the realistic phantom, respectively, showing the capability of the proposed algorithm to work with a good accuracy even in the presence of noise for the localization of the in-body RF source.