Fast 3-d tomographic microwave imaging for breast cancer detection

Experimental Validation of Realistic Measurement Setup for Quantitative UWB-Guided Hyperthermia Temperature Monitoring

Hyperthermia induces slight temperature increase of 4-8 °C inside the tumor, making it more responsive to radiation and drugs, thereby improving the outcome of the oncological treatment. To verify the level of heat in the tumor and to avoid damage of the healthy tissue, methods for non-invasive temperature monitoring are needed. Temperature estimation by means of microwave imaging is of great interest among the scientific community. In this paper, we present the results of experiments based on ultra-wideband (UWB) M-sequence technology. Our temperature estimation approach uses temperature dependency of tissue dielectric properties and relation of UWB images to the reflection coefficient on the boundary between tissue types. The realistic measurement setup for neck cancer hyperthermia considers three antenna arrangements. Data are processed with Delay and Sum beamforming and Truncated Singular Value Decomposition. Two types of experiments are presented in this paper. In the first experiment, relative permittivity of subsequently replaced tumor mimicking material is estimated, and in the second experiment, real temperature change in the tumor imitate is monitored. The results showed that the presented approach allows for qualitative as well as quantitative permittivity and temperature estimation. The frequency range for temperature estimation, preferable antenna configurations, and limitations of the method are indicated.

Review of Microwave Near-Field Sensing and Imaging Devices in Medical Applications

Microwaves can safely and non-destructively illuminate and penetrate dielectric materials, making them an attractive solution for various medical tasks, including detection, diagnosis, classification, and monitoring. Their inherent electromagnetic properties, portability, cost-effectiveness, and the growth in computing capabilities have encouraged the development of numerous microwave sensing and imaging systems in the medical field, with the potential to complement or even replace current gold-standard methods. This review aims to provide a comprehensive update on the latest advances in medical applications of microwaves, particularly focusing on the near-field ones working within the 1-15 GHz frequency range. It specifically examines significant strides in the development of clinical devices for brain stroke diagnosis and classification, breast cancer screening, and continuous blood glucose monitoring. The technical implementation and algorithmic aspects of prototypes and devices are discussed in detail, including the transceiver systems, radiating elements (such as antennas and sensors), and the imaging algorithms. Additionally, it provides an overview of other promising cutting-edge microwave medical applications, such as knee injuries and colon polyps detection, torso scanning and image-based monitoring of thermal therapy intervention. Finally, the review discusses the challenges of achieving clinical engagement with microwave-based technologies and explores future perspectives.

Artificial Intelligence-Powered Mammography: Navigating the Landscape of Deep Learning for Breast Cancer Detection

Worldwide, breast cancer (BC) is one of the most commonly diagnosed malignancies in women. Early detection is key to improving survival rates and health outcomes. This literature review focuses on how artificial intelligence (AI), especially deep learning (DL), can enhance the ability of mammography, a key tool in BC detection, to yield more accurate results. Artificial intelligence has shown promise in reducing diagnostic errors and increasing early cancer detection chances. Nevertheless, significant challenges exist, including the requirement for large amounts of high-quality data and concerns over data privacy. Despite these hurdles, AI and DL are advancing the field of radiology, offering better ways to diagnose, detect, and treat diseases. The U.S. Food and Drug Administration (FDA) has approved several AI diagnostic tools. Yet, the full potential of these technologies, especially for more advanced screening methods like digital breast tomosynthesis (DBT), depends on further clinical studies and the development of larger databases. In summary, this review highlights the exciting potential of AI in BC screening. It calls for more research and validation to fully employ the power of AI in clinical practice, ensuring that these technologies can help save lives by improving diagnosis accuracy and efficiency.

UWB Antenna with Enhanced Directivity for Applications in Microwave Medical Imaging

Microwave medical imaging (MMI) is experiencing a surge in research interest, with antenna performance emerging as a key area for improvement. This work addresses this need by enhancing the directivity of a compact UWB antenna using a Yagi-Uda-inspired reflector antenna. The proposed reflector-loaded antenna (RLA) exhibited significant gain and directivity improvements compared to a non-directional reference antenna. When analyzed for MMI applications, the RLA showed a maximum increase of 4 dBi in the realized gain and of 14.26 dB in the transmitted field strength within a human breast model. Moreover, it preserved the shape of time-domain input signals with a high correlation factor of 94.86%. To further validate our approach, another non-directional antenna with proven head imaging capabilities was modified with a reflector, achieving similar directivity enhancements. The combined results demonstrate the feasibility of RLAs for improved performance in MMI systems.

Microwave Imaging and Sensing Techniques for Breast Cancer Detection

Medical imaging techniques, including X-ray mammography, ultrasound, and magnetic resonance imaging, play a crucial role in the timely identification and monitoring of breast cancer. However, these conventional imaging modalities have their limitations, and there is a need for a more accurate and sensitive alternative. Microwave imaging has emerged as a promising technique for breast cancer detection due to its non-ionizing, non-invasive, and cost-effective nature. Recent advancements in microwave imaging and sensing techniques have opened up new possibilities for the early diagnosis and treatment of breast cancer. By combining microwave sensing with machine learning techniques, microwave imaging approaches can rapidly and affordably identify and classify breast tumors. This manuscript provides a comprehensive overview of the latest developments in microwave imaging and sensing techniques for the early detection of breast cancer. It discusses the principles and applications of microwave imaging and highlights its advantages over conventional imaging modalities. The manuscript also delves into integrating machine learning algorithms to enhance the accuracy and efficiency of microwave imaging in breast cancer detection.

Reducing Non-Through Body Energy Transfer in Microwave Imaging Systems

On-body antennas for use in microwave imaging (MI) systems can direct energy around the body instead of through the body, thus degrading the overall signal-to-noise ratio (SNR) of the system. This work introduces and quantifies the usage of modern metal-backed RF absorbing foam in conjunction with on-body antennas to dampen energy flowing around the body, using both simulations and experiments. A head imaging system is demonstrated herein but the principle can be applied to any part of the body including the torso or extremities. A computational model was simulated numerically using Ansys HFSS. A physical prototype in the form of a helmet with embedded antennas was built to compare simulations with measured data. Simulations and measurements demonstrate that usage of such metal-backed RF-absorbing foams can significantly reduce around-body coupling from Transmit (Tx) and Receive (Rx) antennas by approximately 10dB. Thus, the overall SNR of the MI system can be substantially improved using this low-cost and affordable method.

Review and Analysis of Tumour Detection and Image Quality Analysis in Experimental Breast Microwave Sensing

This review evaluates the methods used for image quality analysis and tumour detection in experimental breast microwave sensing (BMS), a developing technology being investigated for breast cancer detection. This article examines the methods used for image quality analysis and the estimated diagnostic performance of BMS for image-based and machine-learning tumour detection approaches. The majority of image analysis performed in BMS has been qualitative and existing quantitative image quality metrics aim to describe image contrast-other aspects of image quality have not been addressed. Image-based diagnostic sensitivities between 63 and 100% have been achieved in eleven trials, but only four articles have estimated the specificity of BMS. The estimates range from 20 to 65%, and do not demonstrate the clinical utility of the modality. Despite over two decades of research in BMS, significant challenges remain that limit the development of this modality as a clinical tool. The BMS community should utilize consistent image quality metric definitions and include image resolution, noise, and artifacts in their analyses. Future work should include more robust metrics, estimates of the diagnostic specificity of the modality, and machine-learning applications should be used with more diverse datasets and with robust methodologies to further enhance BMS as a viable clinical technique.

A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave Tomography

In this paper, a deep learning technique for tumor detection in a microwave tomography framework is proposed. Providing an easy and effective imaging technique for breast cancer detection is one of the main focuses for biomedical researchers. Recently, microwave tomography gained a great attention due to its ability to reconstruct the electric properties maps of the inner breast tissues, exploiting nonionizing radiations. A major drawback of tomographic approaches is related to the inversion algorithms, since the problem at hand is nonlinear and ill-posed. In recent decades, numerous studies focused on image reconstruction techniques, in same cases exploiting deep learning. In this study, deep learning is exploited to provide information about the presence of tumors based on tomographic measures. The proposed approach has been tested with a simulated database showing interesting performances, in particular for scenarios where the tumor mass is particularly small. In these cases, conventional reconstruction techniques fail in identifying the presence of suspicious tissues, while our approach correctly identifies these profiles as potentially pathological. Therefore, the proposed method can be exploited for early diagnosis purposes, where the mass to be detected can be particularly small.

A compact diamond shaped ultra-wide band antenna system for diagnosing breast cancer

BACKGROUND

Antennas for the microwave imaging system are large which results in higher radiation, manufacturing cost, poor radiation characteristics and it will be difficult to locate on breast tissues.

OBJECTIVE

We propose a wearable ultra-wide band antenna for use in the diagnosis of breast cancer bio-medical applications.

METHODS

The antenna has been fabricated on 1.6 mm FR4 substrate with a dimension of 28 × 14.4 mm2 and can operate between 2 GHz-12 GHz with S11<-10 dB with best radiation characteristics. The prototype of the proposed antenna was fabricated and practically tested and the results were found to be consistent with the simulated results. The proposed UWB antenna is intended to radiate and receive information covering the entire spectrum from 3 GHz to 13 GHz. For good impendence matching throughout the larger spectrum, the defected ground structure (diamond shape) was exploited. All the dimensions of the proposed design are confirmed by parametric study and optimization.

RESULTS

The maximum simulated efficiency was ranging from 80 to 84% in the desired operating frequency. The maximum Specific Absorption Rate of the proposed antenna was 0.98 W/Kg. Therefore, the proposed UWB antenna could be the right structure for breast cancer diagnosis in terms of SAR. The antenna was found to have a substantial radiation efficiency of around 78%-84% in the desired operating bandwidth. The overall realized gain of the proposed UWB antenna was seen ranging from 1.8-4.2 dB which is sufficient for bio-medical applications.

CONCLUSION

The breast phantom was modeled for the validation of the performance of the antenna and SAR was analyzed. The value of SAR of the designed antenna was observed at about 0.98 W/Kg, which is suitable for medical applications.

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.

A Novel Discretization Procedure in the CSI-FEM Algorithm for Brain Stroke Microwave Imaging

In this work, the contrast source inversion method is combined with a finite element method to solve microwave imaging problems. The paper's major contribution is the development of a novel contrast source variable discretization that leads to simplify the algorithm implementation and, at the same time, to improve the accuracy of the discretized quantities. Moreover, the imaging problem is recreated in a synthetic environment, where the antennas, and their corresponding coaxial port, are modeled. The implemented algorithm is applied to reconstruct the tissues' dielectric properties inside the head for brain stroke microwave imaging. The proposed implementation is compared with the standard one to evaluate the impact of the variables' discretization on the algorithm's accuracy. Furthermore, the paper shows the obtained performances with the proposed and the standard implementations of the contrast source inversion method in the same realistic 3D scenario. The exploited numerical example shows that the proposed discretization can reach a better focus on the stroke region in comparison with the standard one. However, the variation is within a limited range of permittivity values, which is reflected in similar averages.

An End-to-End Deep Learning Approach for Quantitative Microwave Breast Imaging in Real-Time Applications

(1) Background: In this paper, an artificial neural network approach for effective and real-time quantitative microwave breast imaging is proposed. It proposes some numerical analyses for the optimization of the network architecture and the improvement of recovery performance and processing time in the microwave breast imaging framework, which represents a fundamental preliminary step for future diagnostic applications. (2) Methods: The methodological analysis of the proposed approach is based on two main aspects: firstly, the definition and generation of a proper database adopted for the training of the neural networks and, secondly, the design and analysis of different neural network architectures. (3) Results: The methodology was tested in noisy numerical scenarios with different values of SNR showing good robustness against noise. The results seem very promising in comparison with conventional nonlinear inverse scattering approaches from a qualitative as well as a quantitative point of view. (4) Conclusion: The use of quantitative microwave imaging and neural networks can represent a valid alternative to (or completion of) modern conventional medical imaging techniques since it is cheaper, safer, fast, and quantitative, thus suitable to assist medical decisions.

Non-Invasive Microwave-Based Imaging System for Early Detection of Breast Tumours

This work introduces a microwave-based system able to detect tumours in breast phantoms in a non-invasive way. The data acquisition system is composed of a hardware system which involves high-frequency components (antennas, switches and cables), a microcontroller, a vector network analyser used as measurement instrument and a computer devoted to the control and automation of the operation of the system. Concerning the software system, the computer runs a Python script which is in charge of mastering and automatising all the required stages for the data acquisition, from initialisation of the hardware system to performing and saving the measurements. We also report on the design of the high-performance broadband antenna used to carry out the measurements, as well as on the algorithm employed to build the final medical images, based on an adapted version of the so-called Improved Delay-and-Sum (IDAS) algorithm improved by a Hamming window filter and averaging preprocessing. The calibration and start-up of the system are also described. The experimental validation includes the use of different tumour models with different dielectric properties inside the breast phantom. The results show promising tumour detection capabilities, even when there is low dielectric contrast between the tumoural and healthy tissues, as is the usual case for dense breasts in young women.

On the dielectric and mechanical characterization of tissue‐mimicking breast phantoms

Objective. In this paper, we focus on the dielectric and mechanical characterization of tissue-mimicking breast phantoms. Approach. Starting from recipes previously proposed by our research group, based on easy-to-handle, cheap and safe components (i.e. sunflower oil, deionized water, dishwashing liquid and gelatin), we produced and tested, both dielectrically and mechanically, more than 100 samples. The dielectric properties were measured from 500 MHz to 14 GHz, the Cole–Cole parameters were derived to describe the dielectric behaviour in a broader frequency range, and the results were compared with dielectric properties of human breast ex vivo tissues up to 50 GHz. The macroscale mechanical properties were measured by means of unconfined compression tests, and the impact of the experimental conditions (i.e. preload and test speed) on the measured Young’s moduli was analysed. In addition, the mechanical contrast between healthy- and malignant-tissue-like phantoms was evaluated. Main results. The results agree with the literature in the cases in which the experimental conditions are known, demonstrating the possibility to fabricate phantoms able to mimic both dielectric and mechanical properties of breast tissues. Significance. In this work, for the first time, a range of materials reproducing all the categories of breast tissues were experimentally characterized, both from a dielectric and mechanical point of view. A large range of frequency were considered for the dielectric measurements and several combinations of experimental conditions were investigated in the context of the mechanical characterization. The proposed results can be useful in the design and testing of complementary or supplementary techniques for breast cancer detection based on micro/millimetre-waves, possibly in connection with other imaging modalities.

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.

Millimeter-wave breast cancer imaging by means of a dual-step approach combining radar and tomographic techniques: preliminary results

Breast cancer is one of the most diagnosed forms of cancer among women worldwide. However, the survival rate is very high when the tumor is diagnosed early. The search for diagnostic techniques increasingly able to detect lesions of the order of a few millimeters and to overcome the limitations of current diagnostic techniques (e.g., the X-ray mammography, currently used as standard for screening campaigns) is always active. Among the main emerging techniques, microwave and millimeter-wave imaging systems have been proposed, using either radar or tomographic approaches. In this paper, a novel dual-step millimeter-wave imaging which combines the advantages of tomographic and radar approaches is proposed. The goal of this work is to reconstruct the dielectric profile of suspicious regions by exploiting the morphological information from the radar maps as a priori information within quantitative tomographic techniques. Promising preliminary dielectric reconstruction results against simulated data are shown in both single- and dual-target scenarios, in which high-density healthy and tumor tissues are present. The reconstruction results were compared to the dielectric characteristics of human breast exvivo tissues used in the simulated models. The proposed dual-step approach allows to distinguish the nature of the targets also in the most challenging case represented by the co-presence of high-density healthy tissues and a malignant lesion, thus paving the way for a deeper investigation of this approach in experimental scenarios. Clinical Relevance-The proposed dual-step approach in the millimeter-wave regime allows to improve the reliability of the diagnostic technique, increasing its specificity.

An Adaptive Finite Element/Finite Difference Domain Decomposition Method for Applications in Microwave Imaging

A new domain decomposition method for Maxwell’s equations in conductive media is presented. Using this method, reconstruction algorithms are developed for the determination of the dielectric permittivity function using time-dependent scattered data of an electric field. All reconstruction algorithms are based on an optimization approach to find the stationary point of the Lagrangian. Adaptive reconstruction algorithms and space-mesh refinement indicators are also presented. Our computational tests show the qualitative reconstruction of the dielectric permittivity function using an anatomically realistic breast phantom.

Microwave Imaging for Early Breast Cancer Detection: Current State, Challenges, and Future Directions

Breast cancer is the most commonly diagnosed cancer type and is the leading cause of cancer-related death among females worldwide. Breast screening and early detection are currently the most successful approaches for the management and treatment of this disease. Several imaging modalities are currently utilized for detecting breast cancer, of which microwave imaging (MWI) is gaining quite a lot of attention as a promising diagnostic tool for early breast cancer detection. MWI is a noninvasive, relatively inexpensive, fast, convenient, and safe screening tool. The purpose of this paper is to provide an up-to-date survey of the principles, developments, and current research status of MWI for breast cancer detection. This paper is structured into two sections; the first is an overview of current MWI techniques used for detecting breast cancer, followed by an explanation of the working principle behind MWI and its various types, namely, microwave tomography and radar-based imaging. In the second section, a review of the initial experiments along with more recent studies on the use of MWI for breast cancer detection is presented. Furthermore, the paper summarizes the challenges facing MWI as a breast cancer detection tool and provides future research directions. On the whole, MWI has proven its potential as a screening tool for breast cancer detection, both as a standalone or complementary technique. However, there are a few challenges that need to be addressed to unlock the full potential of this imaging modality and translate it to clinical settings.

Ultra-Wideband Antennas for Biomedical Imaging Applications: A Survey

Microwave imaging is an active area of research that has garnered interest over the past few years. The main desired improvements to microwave imaging are related to the performances of radiating systems and identification algorithms. To achieve these improvements, antennas suitable to guarantee demanding requirements are needed. In particular, they must operate in close proximity to the objects under examination, ensure an adequate bandwidth, as well as reduced dimensions and low production costs. In addition, in near-field microwave imaging systems, the antenna should provide an ultra-wideband (UWB) response. Given the relevance of the foreseen applications, many UWB antenna designs for microwave imaging applications have been proposed in the literature. In this paper, a comprehensive review of different UWB antenna designs for near-field microwave imaging is presented. The antennas are classified according to the manufacturing technology and radiative performances. Particular attention is also paid to the radiation mechanisms as well as the techniques used to reduce the size and improve the bandwidth.

Three-Dimensional Microwave Head Imaging with GPU-Based FDTD and the DBIM Method

We present a preliminary study of microwave head imaging using a three-dimensional (3-D) implementation of the distorted Born iterative method (DBIM). Our aim is to examine the benefits of using the more computationally intensive 3-D implementation in scenarios where limited prior information is available, or when the target occupies an area that is not covered by the imaging array’s transverse planes. We show that, in some cases, the 3-D implementation outperforms its two-dimensional (2-D) counterpart despite the increased number of unknowns for the linear problem at each DBIM iteration. We also discuss how the 3-D algorithm can be implemented efficiently using graphic processing units (GPUs) and validate this implementation with experimental data from a simplified brain phantom. In this work, we have implemented a non-linear microwave imaging approach using DBIM with GPU-accelerated FDTD. Moreover, the paper offers a direct comparison of 2-D and 3-D microwave tomography implementations for head imaging and stroke detection in inhomogenous anatomically complex numerical head phantoms.

Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review

Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges.

Discrete Dipole Approximation-Based Microwave Tomography for Fast Breast Cancer Imaging

This paper describes a fast microwave tomography reconstruction algorithm based on the two-dimensional discrete dipole approximation. Synthetic data from a finite-element based solver and experimental data from a microwave imaging system are used to reconstruct images and to validate the algorithm. The microwave measurement system consists of 16 monopole antennas immersed in a tank filled with lossy coupling liquid and a vector network analyzer. The low-profile antennas and lossy nature of system make the discrete dipole approximation an ideal forward solver in the image reconstructions. The results show that the algorithm can readily reconstruct a 2D plane of a cylindrical phantom. The proposed forward solver combined with the nodal adjoint method for computing the Jacobian matrix enables the algorithm to reconstruct an image within 6 seconds. This implementation provides a significant time savings and reduced memory requirements and is a dramatic improvement over previous implementations.

Lowering latency and processing burden in computational imaging through dimensionality reduction of the sensing matrix

Recent demonstrations have shown that frequency-diverse computational imaging systems can greatly simplify conventional architectures developed for imaging by transferring constraints into the digital layer. Here, in order to limit the latency and processing burden involved in image reconstruction, we propose to truncate insignificant principal components of the sensing matrix that links the measurements to the scene to be imaged. In contrast to recent work using principle component analysis to synthesize scene illuminations, our generic approach is fully unsupervised and is applied directly to the sensing matrix. We impose no restrictions on the type of imageable scene, no training data is required, and no actively reconfigurable radiating apertures are employed. This paper paves the way to the constitution of a new degree of freedom in image reconstructions, allowing one to place the performance emphasis either on image quality or latency and computational burden. The application of such relaxations will be essential for widespread deployment of computational microwave and millimeter wave imagers in scenarios such as security screening. We show in this specific context that it is possible to reduce both the processing time and memory consumption with a minor impact on the quality of the reconstructed images.

An Active and Low-Cost Microwave Imaging System design for Detection of Breast Cancer Using Back Scattered Signal

A defected ground antenna with dielectric reflector is designed and investigated for breast tumour diagnosis. Ultra-wide band resonance (3.1 to 10.6 GHz) is achieved by etching two slots and adding a narrow vertical strip in a patch antenna. A high dielectric constant substrate is added below the antenna, which shows remarkable effect on performance. Antenna performance is verified experimentally on an artificially fabricated breast tissue and tumour. Malignant tissue has different dielectric properties than the normal tissue, that causes deviation in the scattered antenna power. Average value of backscattered signal variation and ground penetrating radar (GPR) algorithm is used to localize the tumour of radius 4mm in breast tissue.

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.

Experimental Validation on Tissue-Mimicking Phantoms of Millimeter-Wave Imaging for Breast Cancer Detection

Breast cancer is one of the leading causes of cancer death among women; to decrease the death rate for this disease, early detection plays a key role. Recently, microwave imaging systems have been proposed as an alternative to the current techniques, but they suffer from poor resolution due to the low frequencies involved. In this paper, for the first time, an innovative millimeter-wave imaging system for early-stage breast cancer detection is proposed and experimentally verified on different breast phantoms. This has the potential to achieve superior resolution for breasts with a high volumetric percentage of adipose tissue, and the merit to overcome the common misconception that millimeter-waves cannot achieve useful penetration depths for biological applications. Three phantoms were prepared according to the dielectric properties of human breast ex vivo tissues in the frequency range [0.5–50] GHz. Two cylindrical inclusions made by water and gelatin or agar, mimicking dielectric properties of neoplastic tissues, were embedded in the phantom at different depths up to 3 cm. Two double ridge waveguides, with mono-modal frequency band equal to [18–40] GHz, were used to synthetize a linear array of 24 elements in 28 positions, acquiring signals with a Vector Network Analyzer. The images were reconstructed by applying the Delay and Sum algorithm to calibrated data. The feasibility of a new imaging system with a central working frequency of about 30 GHz is experimentally demonstrated for the first time, and a target detection capability up to 3 cm within the phantom is shown. The presented results pave the way for a possible use of millimeter-waves to image non-superficial neoplasms in the breast.

Three-Dimensional Microwave Imaging: Fast and Accurate Computations with Block Resolution Algorithms

This paper considers the microwave imaging reconstruction problem, based on additive penalization and gradient-based optimization. Each evaluation of the cost function and of its gradient requires the resolution of as many high-dimensional linear systems as the number of incident fields, which represents a large amount of computations. Since all such systems involve the same matrix, we propose a block inversion strategy, based on the block-biconjugate gradient stabilized (BiCGStab) algorithm, with efficient implementations specific to the microwave imaging context. Numerical experiments performed on synthetic data and on real measurements show that savings in computing time can reach a factor of two compared to the standard, sequential, BiCGStab implementation. Improvements brought by the block approach are even more important for the most difficult reconstruction problems, that is, with high-frequency illuminations and/or highly contrasted objects. The proposed reconstruction strategy is shown to achieve satisfactory estimates for objects of the Fresnel database, even on the most contrasted ones.

Combining Millimeter-Wave Imaging, Ultrasound and Elastography in a New Multimodal Approach for Breast Cancer Detection: Initial Experimental Results

In this paper, for the first time, a triple-mode scan using electromagnetic waves, in the shape of millimeter waves, and ultrasound waves, to obtain B-mode and quasistatic elastography images of a phantom of human breast tissues is shown. A homogeneous phantom composed of nontoxic, low-cost and easy-to-handle materials (i.e. water, oil, gelatin and dishwashing liquid) was produced, with an inclusion made of water and agar. These are intended to mimic, in terms of dielectric properties, healthy adipose tissues and neoplastic tissues, respectively. A millimeter-wave imaging prototype was used to scan the phantom, by implementing a linear synthetic array of 24 antennas with a central working frequency of 30 GHz. The phantom was then scanned using an ultrasound research system and a linear-array probe at 7 MHz, acquiring both B-mode and quasi-static elastography images. The millimeter-wave system showed an excellent ability to detect the target placed at about 1.4 cm depth. Also in the ultrasound case the inclusion was correctly detected as a hypoechoic, stiff mass. This first experimental findings show that millimeter-wave, ultrasound and elasticity imaging can be used jointly to detect tumor-like targets into phantoms mimicking healthy breast tissues. Thus, they provide promising preliminary results to further study the application of this multimodal approach in all those critical cases in which such complementary imaging techniques could be exploited for an enhanced tumor detection, based on tissues dielectric, acoustic and elastic properties.

Multimodal Breast Phantoms for Microwave, Ultrasound, Mammography, Magnetic Resonance and Computed Tomography Imaging

The aim of this work was to develop multimodal anthropomorphic breast phantoms suitable for evaluating the imaging performance of a recently-introduced Microwave Imaging (MWI) technique in comparison to the established diagnostic imaging modalities of Magnetic Resonance Imaging (MRI), Ultrasound (US), mammography and Computed Tomography (CT). MWI is an emerging technique with significant potential to supplement established imaging techniques to improve diagnostic confidence for breast cancer detection. To date, numerical simulations have been used to assess the different MWI scanning and image reconstruction algorithms in current use, while only a few clinical trials have been conducted. To bridge the gap between the numerical simulation environment and a more realistic diagnostic scenario, anthropomorphic phantoms which mimic breast tissues in terms of their heterogeneity, anatomy, morphology, and mechanical and dielectric characteristics, may be used. Key in this regard is achieving realism in the imaging appearance of the different healthy and pathologic tissue types for each of the modalities, taking into consideration the differing imaging and contrast mechanisms for each modality. Suitable phantoms can thus be used by radiologists to correlate image findings between the emerging MWI technique and the more familiar images generated by the conventional modalities. Two phantoms were developed in this study, representing difficult-to-image and easy-to-image patients: the former contained a complex boundary between the mammary fat and fibroglandular tissues, extracted from real patient MRI datasets, while the latter contained a simpler and less morphologically accurate interface. Both phantoms were otherwise identical, with tissue-mimicking materials (TMMs) developed to mimic skin, subcutaneous fat, fibroglandular tissue, tumor and pectoral muscle. The phantoms’ construction used non-toxic materials, and they were inexpensive and relatively easy to manufacture. Both phantoms were scanned using conventional modalities (MRI, US, mammography and CT) and a recently introduced MWI radar detection procedure called in-coherent Multiple Signal Classification (I-MUSIC). Clinically realistic artifact-free images of the anthropomorphic breast phantoms were obtained using the conventional imaging techniques as well as the emerging technique of MWI.

Review of Microwaves Techniques for Breast Cancer Detection

Conventional breast cancer detection techniques including X-ray mammography, magnetic resonance imaging, and ultrasound scanning suffer from shortcomings such as excessive cost, harmful radiation, and inconveniences to the patients. These challenges motivated researchers to investigate alternative methods including the use of microwaves. This article focuses on reviewing the background of microwave techniques for breast tumour detection. In particular, this study reviews the recent advancements in active microwave imaging, namely microwave tomography and radar-based techniques. The main objective of this paper is to provide researchers and physicians with an overview of the principles, techniques, and fundamental challenges associated with microwave imaging for breast cancer detection. Furthermore, this study aims to shed light on the fact that until today, there are very few commercially available and cost-effective microwave-based systems for breast cancer imaging or detection. This conclusion is not intended to imply the inefficacy of microwaves for breast cancer detection, but rather to encourage a healthy debate on why a commercially available system has yet to be made available despite almost 30 years of intensive research.

Concept of a Radiofrequency Device for Osteopenia/Osteoporosis Screening

Osteoporosis represents a major health problem, resulting in substantial increases in health care costs. There is an unmet need for a cost-effective technique that can measure bone properties without the use of ionizing radiation. The present study reports design, construction, and testing of a safe, and easy to use radiofrequency device to detect osteoporotic bone conditions. The device uses novel on-body antennas contacting the human wrist under an applied, operator-controlled pressure. For the dichotomous diagnostic test, we selected 60 study participants (23-94 years old, 48 female, 12 male) who could be positively differentiated between healthy and osteopenic/osteoporotic states. The band-limited integral of the transmission coefficient averaged for both wrists, multiplied by age, and divided by BMI has been used as an index. For a 100 MHz frequency band centered about 890-920 MHz, the maximum Youden's J index is 81.5%. Both the sensitivity and specificity simultaneously reach 87% given the calibration device threshold tolerance of ±3%. Our approach correlates well with the available DXA measurements and has the potential for screening patients at risk for fragility fractures, given the ease of implementation and low costs associated with both the technique and the equipment. The inclusion of radiofrequency transmission data does add supplementary useful information to the available clinical risk factors.

Metasurface Loaded High Gain Antenna based Microwave Imaging using Iteratively Corrected Delay Multiply and Sum Algorithm

In this paper, the design consideration is investigated for a cylindrical system with low-cost and low-loss dielectric materials for the detection of breast tumor using iteratively corrected delay multiply and sum (IC- DMAS) algorithm. Anomaly in breast tissue is one of the most crucial health issues for women all over the world today. Emergency medical imaging diagnosis can be harmlessly managed by microwave-based analysis technology. Microwave Imaging (MI) has been proved to be a reliable health monitoring approach that can play a fundamental role in diagnosing anomaly in breast tissue. An array of 16 high gain microstrip antennas loaded by Index Near-Zero (INZ) metasurfaces (MS), having the impedance bandwidth of 8.5 GHz (2.70-11.20 GHz) are used as transceivers for the system. The MS is used to increase the electrical length of the signal that results in the gain enhancements. The antennas are mounted in a cylindrical arrangement on a mechanical rotating table along with a phantom mounting podium. A non-reflective positive control switching matrix is used for transmitting and receiving microwave signals. A set of lab-made realistic heterogeneous breast phantoms containing skin, fat, glandular, and tumor tissue dielectric properties in individual layers are used to verify the performance of the proposed technique. The control of the mechanical unit, data collection, and post-processing is conducted via MATLAB. The system can detect multiple tumor objects. The imaging results and numerical Signal to Mean Ratio (SMR) values of the experiment validate the system efficiency and performance that can be a viable solution for tumor detections.

3D Simulations of Intracerebral Hemorrhage Detection Using Broadband Microwave Technology

Early, preferably prehospital, detection of intracranial bleeding after trauma or stroke would dramatically improve the acute care of these large patient groups. In this paper, we use simulated microwave transmission data to investigate the performance of a machine learning classification algorithm based on subspace distances for the detection of intracranial bleeding. A computational model, consisting of realistic human head models of patients with bleeding, as well as healthy subjects, was inserted in an antenna array model. The Finite-Difference Time-Domain (FDTD) method was then used to generate simulated transmission coefficients between all possible combinations of antenna pairs. These transmission data were used both to train and evaluate the performance of the classification algorithm and to investigate its ability to distinguish patients with versus without intracranial bleeding. We studied how classification results were affected by the number of healthy subjects and patients used to train the algorithm, and in particular, we were interested in investigating how many samples were needed in the training dataset to obtain classification results better than chance. Our results indicated that at least 200 subjects, i.e., 100 each of the healthy subjects and bleeding patients, were needed to obtain classification results consistently better than chance (p < 0.05 using Student's t-test). The results also showed that classification results improved with the number of subjects in the training data. With a sample size that approached 1000 subjects, classifications results characterized as area under the receiver operating curve (AUC) approached 1.0, indicating very high sensitivity and specificity.

A Discrete Dipole Approximation Solver Based on the COCG-FFT Algorithm and Its Application to Microwave Breast Imaging

We introduce the discrete dipole approximation (DDA) for efficiently calculating the two-dimensional electric field distribution for our microwave tomographic breast imaging system. For iterative inverse problems such as microwave tomography, the forward field computation is the time limiting step. In this paper, the two-dimensional algorithm is derived and formulated such that the iterative conjugate orthogonal conjugate gradient (COCG) method can be used for efficiently solving the forward problem. We have also optimized the matrix-vector multiplication step by formulating the problem such that the nondiagonal portion of the matrix used to compute the dipole moments is block-Toeplitz. The computation costs for multiplying the block matrices times a vector can be dramatically accelerated by expanding each Toeplitz matrix to a circulant matrix for which the convolution theorem is applied for fast computation utilizing the fast Fourier transform (FFT). The results demonstrate that this formulation is accurate and efficient. In this work, the computation times for the direct solvers, the iterative solver (COCG), and the iterative solver using the fast Fourier transform (COCG-FFT) are compared with the best performance achieved using the iterative solver (COCG-FFT) in C++. Utilizing this formulation provides a computationally efficient building block for developing a low cost and fast breast imaging system to serve under-resourced populations.

Application of Two-Dimensional Discrete Dipole Approximation in Simulating Electric Field of a Microwave Breast Imaging System

The two-dimensional electric field distribution of the microwave imaging system is numerically simulated for a simplified breast tumour model. The proposed two-dimensional discrete dipole approximation (DDA) has the potential to improve computational speed compared to other numerical methods while retaining comparable accuracy. We have modeled the field distributions in COMSOL Multiphysics as baseline results to benchmark the DDA simulations. We have also investigated the adequate sampling size and the effect of inclusion size and property contrast on solution accuracy. In this way, we can utilize the 2D DDA as an alternative, fast and reliable forward solver for microwave tomography. From a mathematical perspective, the derivation of the 2D DDA and its application to microwave imaging is new and not previously implemented. The simulation results and the measurements show that the 2D DDA is a well-grounded forward solver for the specified microwave breast imaging system.

Tissue-mimicking materials for breast phantoms up to 50 GHz

Millimeter (mm)-wave imaging has been recently proposed as a new technique for breast cancer detection, based on the significant dielectric contrast between healthy and tumor tissues. Here we propose a procedure to fabricate, electromagnetically characterize and preserve realistic breast tissue-mimicking phantoms for testing mm-wave imaging prototypes. Low-cost, non-toxic and easy-to-produce mixtures made of sunflower oil, water and gelatin were prepared and their dielectric properties were for the first time measured in the (0.5-50) GHz frequency range using a coaxial probe kit. Different oil and gelatin percentages were tested. An alternative recipe based on a waste-oil hardener was also proposed. Finally, water and sunflower oil were investigated as preservation media. The mixtures electromagnetic properties were in good agreement with those of human breast ex vivo samples. By changing the ingredient concentrations or using different solidifying agents it was possible to mimic different tissue types. Besides, we show that sunflower oil represents an effective preservation medium for the developed materials. The first breast phantom mimicking a tumor mass into healthy tissues up to 50 GHz was also successfully fabricated. Results demonstrated the potential of the designed recipes to mimic breast tissues with different biological characteristics, preserving dielectric properties over time. Thus, this study represents a fundamental step towards the development of heterogeneous breast phantoms able to mimic the electromagnetic behavior of healthy and tumor tissues for mm-wave imaging applications.

Full-Vectorial 3D Microwave Imaging of Sparse Scatterers through a Multi-Task Bayesian Compressive Sensing Approach

In this paper, the full-vectorial three-dimensional (3D) microwave imaging (MI) of sparse scatterers is dealt with. Towards this end, the inverse scattering (IS) problem is formulated within the contrast source inversion (CSI) framework and it is aimed at retrieving the sparsest and most probable distribution of the contrast source within the imaged volume. A customized multi-task Bayesian compressive sensing (MT-BCS) method is used to yield regularized solutions of the 3D-IS problem with a remarkable computational efficiency. Selected numerical results on representative benchmarks are presented and discussed to assess the effectiveness and the reliability of the proposed MT-BCS strategy in comparison with other competitive state-of-the-art approaches, as well.

Attributes, Performance, and Gaps in Current & Emerging Breast Cancer Screening Technologies

Background:

Early detection of breast cancer, combined with effective treatment, can reduce mortality. Millions of women are diagnosed with breast cancer and many die every year globally. Numerous early detection screening tests have been employed. A wide range of current breast cancer screening methods are reviewed based on a series of searchers focused on clinical testing and performance. </P><P> Discussion: The key factors evaluated centre around the trade-offs between accuracy (sensitivity and specificity), operator dependence of results, invasiveness, comfort, time required, and cost. All of these factors affect the quality of the screen, access/eligibility, and/or compliance to screening programs by eligible women. This survey article provides an overview of the working principles, benefits, limitations, performance, and cost of current breast cancer detection techniques. It is based on an extensive literature review focusing on published works reporting the main performance, cost, and comfort/compliance metrics considered.

Conclusion:

Due to limitations and drawbacks of existing breast cancer screening methods there is a need for better screening methods. Emerging, non-invasive methods offer promise to mitigate the issues particularly around comfort/pain and radiation dose, which would improve compliance and enable all ages to be screened regularly. However, these methods must still undergo significant validation testing to prove they can provide realistic screening alternatives to the current accepted standards.

On the Use of Focused Incident Near-Field Beams in Microwave Imaging

We consider the use of focused incident near-field (NF) beams to interrogate the object of interest (OI) in NF microwave imaging (MWI). To this end, we first discuss how focused NF beams can be advantageously utilized to suppress scattering effects from the neighbouring objects whose unknown dielectric properties are not of interest (i.e., undesired scatterers). We then discuss how this approach can also be helpful in reducing the required measured data points to perform imaging. Driven by the relation between the electromagnetic inverse source and inverse scattering problems, our approach emphasizes the importance of tailoring the induced contrast sources in the imaging domain through the utilized incident NF beams. To demonstrate this idea, we consider two recently-proposed NF beams, and simulate them for imaging applications. The first one is a subwavelength focused NF beam generated by a passive NF plate, and the other is a Bessel beam generated by a leaky radial waveguide. Simple imaging examples are considered to explore the potential advantages of this approach, in particular, toward mainly seeing the object of interest, and not the unknown undesired scatterers. The scope of this paper is limited to homogeneous dielectric objects for which the induced total field distributions in the interrogated objects are similar to the incident field distributions (e.g., those that satisfy the Born approximation). Simple inversion results for focused and non-focused beams are presented accompanied by discussions comparing the achieved reconstructed values.

A Multithreshold Iterative DBIM-Based Algorithm for the Imaging of Heterogeneous Breast Tissues

OBJECTIVE

This paper proposes a novel microwave imaging (MWI) multifrequency technique, which combines compressive sensing (CS) with the well-known distorted Born iterative method. CS strategies are emerging as a promising tool in MWI applications, which can improve reconstruction quality and/or reduce the number of data samples.

METHODS

The proposed approach is based on iterative shrinkage thresholding algorithm (ISTA), which has been modified to include an automatic and adaptive selection of multithreshold values.

RESULTS

This adaptive multithreshold ISTA implementation is applied in reconstruction of two-dimensional (2-D) numerical heterogeneous breast phantoms, where it outperforms the standard thresholding implementation. We show that our approach is also successful in 3-D simulations of a realistic imaging experiment, despite the mismatch between the data and our algorithm's forward model.

CONCLUSION

These results suggest that the proposed algorithm is a promising tool for medical MWI applications.

SIGNIFICANCE

Important novelties of this approach are the use of multiple thresholds to recover the different unknowns in the Debye model as well as the adaptive selection of these thresholds. Moreover, we have shown that employing modified hard constraints inside the linear step of the inversion procedure can enhance reconstruction quality.

Dielectric properties of colon polyps, cancer, and normal mucosa: Ex vivo measurements from 0.5 to 20 GHz

PURPOSE

Colorectal cancer is highly preventable by detecting and removing polyps, which are the precursors. Currently, the most accurate test is colonoscopy, but still misses 22% of polyps due to visualization limitations. In this paper, we preliminary assess the potential of microwave imaging and dielectric properties (e.g., complex permittivity) as a complementary method for detecting polyps and cancer tissue in the colon. The dielectric properties of biological tissues have been used in a wide variety of applications, including safety assessment of wireless technologies and design of medical diagnostic or therapeutic techniques (microwave imaging, hyperthermia, and ablation). The main purpose of this work is to measure the complex permittivity of different types of colon polyps, cancer, and normal mucosa in ex vivo human samples to study if the dielectric properties are appropriate for classification purposes.

METHODS

The complex permittivity of freshly excised healthy colon tissue, cancer, and histological samples of different types of polyps from 23 patients was characterized using an open-ended coaxial probe between 0.5 and 20 GHz. The obtained measurements were classified into five tissue groups before applying a data reduction step with a frequency dispersive single-pole Debye model. The classification was finally compared with pathological analysis of tissue samples, which is the gold standard.

RESULTS

The complex permittivity progressively increases as the tissue degenerates from normal to cancer. When comparing to the gold-standard histological tissue analysis, the sensitivity and specificity of the proposed method is the following: 100% and 95% for cancer diagnosis; 91% and 62% for adenomas with high-grade dysplasia; 100% and 61% for adenomas with low-grade dysplasia; and 100% and 74% for hyperplastic polyps, respectively. In addition, complex permittivity measurements were independent of the lesion shape and size, which is also an interesting property comparing to current colonoscopy techniques.

CONCLUSIONS

The contrast in complex permittivities between normal and abnormal colon tissues presented here for the first time demonstrate the potential of these measurements for tissue classification. It also opens the door to the development of a microwave endoscopic device to complement the outcomes of colonoscopy with functional tissue information.

Addressing Multipath Signal Corruption in Microwave Tomography and the Influence on System Design and Algorithm Development

In developing a microwave tomography system, we started by examining the fundamental signal measurement challenges-i.e., how to interrogate the target while suppressing unwanted multi-path signals. Beginning with a lossy coupling bath to suppress unwanted surface waves, we have developed a robust and reliable system that is both simple and low profile. However, beyond the basic measurement configuration, the lossy coupling medium concept has also informed our choice of array antenna and imaging algorithms. The synergism of these concepts has produced a novel concept which is embodied in a system that has been successfully translated to the clinic.

Detectability of Breast Tumor by a Hand-held Impulse-Radar Detector: Performance Evaluation and Pilot Clinical Study

In this report, a hand-held impulse-radar breast cancer detector is presented and the detectability of malignant breast tumors is demonstrated in the clinical test at Hiroshima University Hospital, Hiroshima, Japan. The core functional parts of the detector consist of 65-nm technology complementary metal-oxide-semiconductor (CMOS) integrated circuits covering the ultrawideband width from 3.1 to 10.6 GHz, which enable the generation and transmission of Gaussian monocycle pulse (GMP) with the pulse width of 160 ps and single port eight throw (SP8T) switching matrices for controlling the combination of 4 × 4 cross-shaped dome antenna array. The detector is designed to be placed on the breast with the patient in the supine position. The detectability of malignant tumors is confirmed in excised breast tissues after total mastectomy surgery. The three-dimensional positions of the tumors in the imaging results are consistent with the results of histopathology analysis. The clinical tests are conducted by a clinical doctor for five patients at the hospital. The malignant tumors include invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). The final confocal imaging results are consistent with those of Magnetic Resonance Imaging (MRI), demonstrating the feasibility of the hand-held impulse-radar detector for malignant breast tumors.

Advanced Fast 3-D Electromagnetic Solver for Microwave Tomography Imaging

This paper describes a fast-forward electromagnetic solver (FFS) for the image reconstruction algorithm of our microwave tomography system. Our apparatus is a preclinical prototype of a biomedical imaging system, designed for the purpose of early breast cancer detection. It operates in the 3-6-GHz frequency band using a circular array of probe antennas immersed in a matching liquid; it produces image reconstructions of the permittivity and conductivity profiles of the breast under examination. Our reconstruction algorithm solves the electromagnetic (EM) inverse problem and takes into account the real EM properties of the probe antenna array as well as the influence of the patient's body and that of the upper metal screen sheet. This FFS algorithm is much faster than conventional EM simulation solvers. In comparison, in the same PC, the CST solver takes ~45 min, while the FFS takes ~1 s of effective simulation time for the same EM model of a numerical breast phantom.

A COTS-Based Microwave Imaging System for Breast-Cancer Detection

Microwave imaging is an emerging breast cancer diagnostic technique, which aims at complementing already established methods like mammography, magnetic resonance imaging, and ultrasound. It offers two striking advantages: no-risk for the patient and potential low-cost for national health systems. So far, however, the prototypes developed for validation in labs and clinics used costly lab instruments such as a vector network analyzer (VNA). Moreover, the CPU time required by complex image reconstruction algorithms may not be compatible with the duration of a medical examination. In this paper, both these issues are tackled. Indeed, we present a prototype system based on low-cost and off-the-shelf microwave components, custom-made antennas, and a small form-factor processing system with an embedded field-programmable gate array for accelerating the execution of the imaging algorithm. We show that our low-cost system can compete with an expensive VNA in terms of accuracy, and it is more than 20x faster than a high-performance server at image reconstruction.