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

Emerging non-invasive microwave and millimeter-wave imaging technologies for food inspection

The microwave and millimeter-wave (MMW) imaging technology is gaining increasing interest for food inspection. It allows for noninvasive, contactless, and fast scanning capabilities, while being cost-efficient and safe to human. This review paper introduces the fundamentals in the interaction of electromagnetic wave with food materials and the current MMW sensing and imaging systems used for foods. Then we present emerging technologies in MMW imaging for inspecting food quality and safety, aiming to meet the modern food industry's demand. According to the most recent technological advancements, it is expected that high-performance antenna, ultrawide bandwidth signal generation, nano-scale semiconductor technologies, radio frequency identification with inductance-capacitance resonator, and machine learning could significantly enhance the capabilities of MMW imaging systems for food inspection.

Cross-Correlation of Confocal Images for Excised Breast Tissues of Total Mastectomy

OBJECT

The purpose of this study is to develop an image artifact removal method for radar-based microwave breast imaging and demonstrates the detectability on excised breast tissues of total mastectomy.

METHODS

A cross-correlation method was proposed and measurements were conducted. A hand-held radar-based breast cancer detector was utilized to measure a breast at different orientations. Images were generated by multiplying the confocal image data from two scans after cross-correlation. The optimum reconstruction permittivity values were extracted by the local maxima of the confocal image intensity as a function of reconstruction permittivity.

RESULTS

With the proposed cross-correlation method, the contrast of the imaging result was enhanced and the clutters were removed. The proposed method was applied to 50 cases of excised breast tissues and the detection sensitivity of 72% was achieved. With the limited number of samples, the dependency of detection sensitivity on the breast size, breast density, and tumor size were examined.

CONCLUSION AND SIGNIFICANCE

The detection sensitivity was strongly influenced by the breast density. The sensitivity was high for fatty breasts, whereas the sensitivity was low for heterogeneously dense breasts. In addition, it was observed that the sensitivity was high for extremely dense breast. This is the first detailed report on the excised breast tissues.

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.

Interconnect for Dense Electronically Scanned Antenna Array Using High-Speed Vertical Connector

We present the design and the performance evaluation of a new interconnect for large-scale densely packed electronically scanned antenna arrays that utilize a high-speed digital board-to-board vertical connector. The application targets microwave tissue, imaging in the frequency range from 3 GHz to 8 GHz. The tissue-imaging arrays consist of hundreds of active antenna elements, which require low-reflection, low-loss, and low-crosstalk connections to their respective receiving and transmitting circuits. The small antenna size and the high array density preclude the use of coaxial connectors, which are also expensive and mechanically unreliable. Modern board-to-board high-speed connectors promise bandwidths as high as 12 GHz, along with high pin density, mechanical robustness, and low cost. However, their compatibility with the various transmission lines leading to/from the miniature printed antenna elements and microwave circuitry is not well studied. Here, we focus on the design of the transitions from coplanar waveguide transmission lines to/from a high-speed vertical connector. The performance of the interconnect is examined through electromagnetic simulations and measurements. Comparison is carried out with the expensive sub-miniature push-on sub-micro coaxial connectors commonly used in miniature radio-frequency electronics. The results demonstrate that high-speed vertical connectors can provide comparable performance in the UWB frequency range.

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.

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.

Microwave Imaging Approach for Breast Cancer Detection Using a Tapered Slot Antenna Loaded with Parasitic Components

In this paper, a wideband antenna is proposed for ultra-wideband microwave imaging applications. The antenna is comprised of a tapered slot ground, a rectangular slotted patch and four star-shaped parasitic components. The added slotted patch is shown to be effective in improving the bandwidth and gain. The proposed antenna system provides a realized gain of 6 dBi, an efficiency of around 80% on the radiation bandwidth, and a wide impedance bandwidth (S11 < -10 dB) of 6.3 GHz (from 3.8 to 10.1 GHz). This supports a true wideband operation. Furthermore, the fidelity factor for face-to-face (FtF) direction is 91.6%, and for side by side (SbS) is 91.2%. This proves the excellent directionality and less signal distortion of the designed antenna. These high figures establish the potential use of the proposed antenna for imaging. A heterogeneous breast phantom with dielectric characteristics identical to actual breast tissue with the presence of tumors was constructed for experimental validation. An antenna array of the proposed antenna element was situated over an artificial breast to collect reflected and transmitted waves for tumor characterization. Finally, an imaging algorithm was used to process the retrieved data to recreate the image in order to detect the undesirable tumor object inside the breast phantom.

Near-Field Microwave Tomography of Biological Tissues: Future Perspectives

This overview shows the mapping of specific visualization techniques, depth assessment of the structure of the underlying tissues and used wavelengths of radiation. Medical imaging is currently one of the most dynamically developing areas of medical science. The main aim of the review is a systematization of information on the current status of the microwave imaging of biological objects, primarily of body tissues. The main options of microwave sensing of biological objects are analyzed. Two basic techniques for sensing differing evaluation parameters are characterized. They are microwave thermometry (passive) and near-field resonance imaging. The physical principles of microwave sensing application are discussed. It is shown that the resonant near-field microwave tomography allows visualization of the structure of biological tissues on the basis of the spatial distribution of their electrodynamic characteristics - permittivity and conductivity. Potential areas for this method in dermatology, including dermatooncology, are shown. The known results of applying the method to patients with dermatoses are given. The informativeness of the technology in the early diagnosis of melanoma is shown. The prospects of microwave diagnostics in combustiology, reconstructive and plastic surgery are demonstrated. Thus, microwave sensing is a modern, dynamically developing method of biophysical assessment of body tissues. There is a strong indication of the feasibility of application of microwave sensing in combustiology (in different periods of burn disease), as well as in reconstructive surgery. Further research in this and other areas of biomedicine will significantly expand the range of possibilities of modern technologies of visualization.

Analytical chemistry toward on-site diagnostics

Analytical Chemistry, through quantitative and/or qualitative analysis (identification), is a discipline that involves the development of methodologies and the exploration of new principles to obtain answers to given problems. In situ analysis techniques have attracted attention for its ability to elucidate phenomena occurring and to evaluate amount of a certain component in substances at real time and biological samples as applications of such analysis technology. Lots of techniques have been performed to understand the fundamental phenomena in varied fields such as X-ray, vibrational, and electrochemical impedance spectroscopies and also analytical reagents that enable to semi-quantitative analysis just observation. In fact, applying various in situ techniques in analytical chemistry expands to the medical diagnosis, which leads to be able to detect early diseases. Here, we describe some of previous researches in many fields such as electrochemical device for energy storage, biology, environment, and pathology and briefly introduce our recent challenges to analytical chemistry toward the on-site diagnosis.

Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model

Mammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer mortality. However, mammography has limitations and potential harms, such as the use of ionizing radiation. To overcome the ionizing radiation exposure issues, a novel device (i.e. MammoWave) based on low-power radio-frequency signals has been developed for breast lesion detection. The MammoWave is a microwave device and is under clinical validation phase in several hospitals across Europe. The device transmits non-invasive microwave signals through the breast and accumulates the backscattered (returned) signatures, commonly denoted as the S21 signals in engineering terminology. Backscattered (complex) S21 signals exploit the contrast in dielectric properties of breasts with and without lesions. The proposed research is aimed to automatically segregate these two types of signal responses by applying appropriate supervised machine learning (ML) algorithm for the data emerging from this research. The support vector machine with radial basis function has been employed here. The proposed algorithm has been trained and tested using microwave breast response data collected at one of the clinical validation centres. Statistical evaluation indicates that the proposed ML model can recognise the MammoWave breasts signal with no radiological finding (NF) and with radiological findings (WF), i.e., may be the presence of benign or malignant lesions. A sensitivity of 84.40% and a specificity of 95.50% have been achieved in NF/WF recognition using the proposed ML model.

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.

Near-Field Microwave Scattering Formulation by A Deep Learning Method

A deep learning method is applied to modelling electromagnetic (EM) scattering for microwave breast imaging (MBI). The neural network (NN) accepts 2D dielectric breast maps at 3 GHz and produces scattered-field data on an antenna array composed of 24 transmitters and 24 receivers. The NN was trained by 18,000 synthetic digital breast phantoms generated by generative adversarial network (GAN), and the scattered-field data pre-calculated by method of moments (MOM). Validation was performed by comparing the 2,000 NN-produced datasets isolated from the training data with the data computed by MOM. Finally, data generated by NN and MOM were used for image reconstruction. The reconstruction demonstrated that errors caused by NN would not significantly affect the image result. But the computational speed of NN was nearly 104 times faster than the MOM, indicating that deep learning has the potential to be considered as a fast tool for EM scattering computation.

Non-Contact VITAL Signs Monitoring of a Patient Lying on Surgical Bed Using Beamforming FMCW Radar

Respiration and heartrates are important information for surgery. When the vital signs of the patient lying prone are monitored using radar installed on the back of the surgical bed, the surgeon's movements reduce the accuracy of these monitored vital signs. This study proposes a method for enhancing the monitored vital sign accuracies of a patient lying on a surgical bed using a 60 GHz frequency modulated continuous wave (FMCW) radar system with beamforming. The vital sign accuracies were enhanced by applying a fast Fourier transform (FFT) for range and beamforming which suppress the noise generated at different ranges and angles from the patient's position. The experiment was performed for a patient lying on a surgical bed with or without surgeon. Comparing a continuous-wave (CW) Doppler radar, the FMCW radar with beamforming improved almost 22 dB of signal-to-interference and noise ratio (SINR) for vital signals. More than 90% accuracy of monitoring respiration and heartrates was achieved even though the surgeon was located next to the patient as an interferer. It was analyzed using a proposed vital signal model included in the radar IF equation.

Dynamic Microwave Imaging of the Cardiovascular System Using Ultra-Wideband Radar-on-Chip Devices

OBJECTIVE

Microwave imaging has been investigated for medical applications such as stroke and breast imaging. Current systems typically rely on bench-top equipment to scan at a variety of antenna positions. For dynamic imaging of moving structures, such as the cardiovascular system, much higher imaging speeds are required than what has thus far been reported. Recent innovations in radar-on-chip technology allow for simultaneous high speed data collection at multiple antenna positions at a fraction of the cost of conventional microwave equipment, in a small and potentially portable system. The objective of the current work is to provide proof of concept of dynamic microwave imaging in the body, using radar-on-chip technology.

METHODS

Arrays of body-coupled antennas were used with nine simultaneously operated coherent ultra-wideband radar chips. Data were collected from the chest and thigh of a volunteer, with the objective of imaging the femoral artery and beating heart. In addition, data were collected from a phantom to validate system performance. Video data were constructed using beamforming.

RESULTS

The location of the femoral artery could successfully be resolved, and a distinct arterial pulse wave was discernable. Cardiac activity was imaged at locations corresponding to the heart, but image quality was insufficient to identify individual anatomical structures. Static and differential imaging of the femur bone proved unsuccessful.

CONCLUSION

Using radar chip technology and an imaging approach, cardiovascular activity was detected in the body, demonstrating first steps towards biomedical dynamic microwave imaging. The current portable and modular system design was found unsuitable for static in-body imaging.

SIGNIFICANCE

This first proof of concept demonstrates that radar-on-chip could enable cardiovascular imaging in a low-cost, small and portable system. Such a system could make medical imaging more accessible, particularly in ambulatory or long-term monitoring settings.

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.

Feasibility of Portable Microwave Imaging Device for Breast Cancer Detection

Purpose: Microwave radar-based breast imaging technology utilizes the principle of radar, in which radio waves reflect at the interface between target and normal tissues, which have different permittivities. This study aims to investigate the feasibility and safety of a portable microwave breast imaging device in clinical practice. Materials and methods: We retrospectively collected the imaging data of ten breast cancers in nine women (median age: 66.0 years; range: 37–78 years) who had undergone microwave imaging examination before surgery. All were Japanese and the tumor sizes were from 4 to 10 cm. Using a five-point scale (1 = very poor; 2 = poor; 3 = fair; 4 = good; and 5 = excellent), a radiologist specialized in breast imaging evaluated the ability of microwave imaging to detect breast cancer and delineate its location and size in comparison with conventional mammography and the pathological findings. Results: Microwave imaging detected 10/10 pathologically proven breast cancers, including non-invasive ductal carcinoma in situ (DCIS) and micro-invasive carcinoma, whereas mammography failed to detect 2/10 breast cancers due to dense breast tissue. In the five-point evaluation, median score of location and size were 4.5 and 4.0, respectively. Conclusion: The results of the evaluation suggest that the microwave imaging device is a safe examination that can be used repeatedly and has the potential to be useful in detecting breast cancer.

An Optimization-Based Approach to Radar Image Reconstruction in Breast Microwave Sensing

Breast microwave sensing (BMS) has been studied as a potential technique for cancer detection due to the observed microwave properties of malignant and healthy breast tissues. This work presents a novel radar-based image reconstruction algorithm for use in BMS that reframes the radar image reconstruction process as an optimization problem. A gradient descent optimizer was used to create an optimization-based radar reconstruction (ORR) algorithm. Two hundred scans of MRI-derived breast phantoms were performed with a preclinical BMS system. These scans were reconstructed using the ORR, delay-and-sum (DAS), and delay-multiply-and-sum (DMAS) beamformers. The ORR was observed to improve both sensitivity and specificity compared to DAS and DMAS. The estimated sensitivity and specificity of the DAS beamformer were 19% and 44%, respectively, while for ORR, they were 27% and 56%, representing a relative increase of 42% and 27%. The DAS reconstructions also exhibited a hot-spot image artifact, where a localized region of high intensity that did not correspond to any physical phantom feature would be present in an image. This artifact appeared like a tumour response within the image and contributed to the lower specificity of the DAS beamformer. This artifact was not observed in the ORR reconstructions. This work demonstrates the potential of an optimization-based conceptualization of the radar image reconstruction problem in BMS. The ORR algorithm implemented in this work showed improved diagnostic performance and fewer image artifacts compared to the widely employed DAS algorithm.

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.

Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data

Recently, a novel microwave apparatus for breast lesion detection (MammoWave), uniquely able to function in air with 2 antennas rotating in the azimuth plane and operating within the band 1-9 GHz has been developed. Machine learning (ML) has been implemented to understand information from the frequency spectrum collected through MammoWave in response to the stimulus, segregating breasts with and without lesions. The study comprises 61 breasts (from 35 patients), each one with the correspondent output of the radiologist's conclusion (i.e., gold standard) obtained from echography and/or mammography and/or MRI, plus pathology or 1-year clinical follow-up when required. The MammoWave examinations are performed, recording the frequency spectrum, where the magnitudes show substantial discrepancy and reveals dissimilar behaviours when reflected from tissues with/without lesions. Principal component analysis is implemented to extract the unique quantitative response from the frequency response for automated breast lesion identification, engaging the support vector machine (SVM) with a radial basis function kernel. In-vivo feasibility validation (now ended) of MammoWave was approved in 2015 by the Ethical Committee of Umbria, Italy (N. 6845/15/AV/DM of 14 October 2015, N. 10352/17/NCAV of 16 March 2017, N 13203/18/NCAV of 17 April 2018). Here, we used a set of 35 patients. According to the radiologists conclusions, 25 breasts without lesions and 36 breasts with lesions underwent a MammoWave examination. The proposed SVM model achieved the accuracy, sensitivity, and specificity of 91%, 84.40%, and 97.20%. The proposed ML augmented MammoWave can identify breast lesions with high accuracy.

Analysis of the Mechanism of Breast Metastasis Based on Image Recognition and Ultrasound Diagnosis

Breast cancer is one of the cancers with the highest incidence among women. In the late stage, cancer cells may metastasize to a distance, causing multiple organ diseases, threatening the lives of patients. The detection of lymph node metastasis based on pathological images is a key indicator for the diagnosis and staging of breast cancer, and correct staging decisions are the prerequisite and basis for targeted treatment. At present, the detection of lymph node metastasis mainly relies on manual screening by pathologists, which is time-consuming and labor-intensive, and the diagnosis results are variable and subjective. The automatic staging method based on the panoramic image calculation of the sentinel lymph node of the breast proposed in this paper can provide a set of standardized, high-accuracy, and repeatable objective diagnosis results. However, it is very difficult to automatically detect and locate cancer metastasis areas in highly complex panoramic images of lymph nodes. This paper proposes a novel deep network training strategy based on the sliding window to train an automatic localization model of cancer metastasis area. The training strategy first trains the initial convolutional network in a small amount of data, extracts false-positive and false-negative image blocks, and uses manual screening combined with automatic network screening to reclassify the false-positive blocks to improve the class of negative categories. Using mammography, ultrasound, MRI, and 18F-FDG PET-CT examinations, the detection rate and diagnostic accuracy of primary cancers in the breast of patients with axillary lymph node metastasis as the first diagnosis were obtained. The detection rate and diagnostic accuracy of breast MRI for primary cancers in the breast are much higher than those of X-ray, ultrasound, and 18F-FDG PET-CT (all P values <0.001). Mammography, ultrasound, and PET-CT examinations showed no difference in the detection rate and diagnostic accuracy of primary cancers in the breast of patients with axillary lymph node metastasis as the first diagnosis. Breast MRI should be used as a routine examination for patients with axillary lymph node metastasis as the first diagnosis. The primary breast cancer in the first diagnosed patients with axillary lymph node metastasis is often presented as localized asymmetric compactness or calcification on X-ray; it often appears as small focal mass lesions and ductal lesions without three-dimensional space-occupying effect on ultrasound.

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.

Enhancement of Penetration of Millimeter Waves by Field Focusing Applied to Breast Cancer Detection

OBJECTIVE

The potentialities of improving the penetration of millimeter waves for breast cancer imaging are here explored.

METHODS

A field focusing technique based on a convex optimization method is proposed, capable of increasing the field level inside a breast-emulating stratification.

RESULTS

The theoretical results are numerically validated via the design and simulation of two circularly polarized antennas. The experimental validation of the designed antennas, using tissue-mimicking phantoms, is provided, being in good agreement with the theoretical predictions.

CONCLUSION

The possibility of focusing, within a lossy medium, the electromagnetic power at millimeter-wave frequencies is demonstrated.

SIGNIFICANCE

Field focusing can be a key for using millimeter waves for breast cancer detection.

The Application of an Iterative Structure to the Delay-and-Sum and the Delay-Multiply-and-Sum Beamformers in Breast Microwave Imaging

Breast microwave imaging (BMI) is a potential breast cancer screening method. This manuscript presents a novel iterative delay-and-sum (DAS) based reconstruction algorithm for BMI. This iterative-DAS (itDAS) algorithm uses a forward radar model to iteratively update an image estimate. A variation of the itDAS reconstruction algorithm that uses the delay-multiply-and-sum (DMAS) beamformer was also implemented (the itDMAS algorithm). Both algorithms were used to reconstruct images from experimental scans of an array of 3D-printed MRI-based breast phantoms performed with a clinical BMI system. The signal-to-clutter ratio (SCR) and signal-to-mean ratio (SMR) were used to compare the performance of the itDAS and itDMAS methods to the DAS and DMAS beamformers. While no significant difference between the itDAS and itDMAS methods was observed in most images, the itDAS algorithm produced reconstructions that had significantly higher SMR than the non-iterative methods, increasing contrast by as much as 19 dB over DAS and 13 dB over DMAS. The itDAS algorithm also increased the SCR of reconstructions by up to 5 dB over DAS and 4 dB over DMAS, indicating that both high-intensity and background clutter are reduced in images reconstructed by the itDAS algorithm.

Advances in Microwave Near-Field Imaging: Prototypes, Systems, and Applications

Microwave imaging employs detection techniques to evaluate hidden or embedded objects in a structure or media using electro-magnetic (EM) waves in the microwave range, 300 MHz-300 GHz. Microwave imaging is often associated with radar detection such as target location and tracking, weather-pattern recognition, and underground surveillance, which are far-field applications. In recent years, due to microwaves' ability to penetrate optically opaque media, short-range applications, including medical imaging, nondestructive testing (NDT) and quality evaluation, through-the-wall imaging, and security screening, have been developed. Microwave near-field imaging most often occurs when detecting the profile of an object within the short range (when the distance from the sensor to the object is less than one wavelength to several wave-lengths) and depends on the electrical size of the antenna(s) and target.

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.

Breast Cancer Detection-A Synopsis of Conventional Modalities and the Potential Role of Microwave Imaging

Global statistics have demonstrated that breast cancer is the most frequently diagnosed invasive cancer and the leading cause of cancer death among female patients. Survival following a diagnosis of breast cancer is grossly determined by the stage of the disease at the time of initial diagnosis, highlighting the importance of early detection. Improving early diagnosis will require a multi-faceted approach to optimizing the use of currently available imaging modalities and investigating new methods of detection. The application of microwave technologies in medical diagnostics is an emerging field of research, with breast cancer detection seeing the most significant progress in the last twenty years. In this review, the application of current conventional imaging modalities is discussed, and recurrent shortcomings highlighted. Microwave imaging is rapid and inexpensive. If the preliminary results of its diagnostic capacity are substantiated, microwave technology may offer a non-ionizing, non-invasive, and painless adjunct or stand-alone modality that could possibly be implemented in routine diagnostic breast care.

Measurement and Analysis of Complex Permittivity of Breast Cancer in Microwave Band

In order to obtain the basic data of breast cancer detection by microwave imaging, the complex permittivity of tissue excised from a breast cancer surgery was measured and analyzed. The relative permittivity and the conductivity of each tissue have strong linearity. In 80% of cases the relative dielectric constant and conductivity of the cancer tissue were higher than those in the breast tissue. However, in the remaining 20% case (scirrhous carcinoma) the dielectric constant and conductivity of the mammary gland were higher in those of cancer. We found that it is necessary to examine diagnostic approach of the reconstructed image by microwave imaging.

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.

Comparing Radar-Based Breast Imaging Algorithm Performance with Realistic Patient-Specific Permittivity Estimation

Radar-based breast imaging has shown promise as an imaging modality for early-stage cancer detection, and clinical investigations of two commercial imaging systems are ongoing. Many imaging algorithms have been proposed, which seek to improve the quality of the reconstructed microwave image to enhance the potential clinical decision. However, in many cases, the radar-based imaging algorithms have only been tested in limited numerical or experimental test cases or with simplifying assumptions such as using one estimate of permittivity for all patient test cases. In this work, the potential impact of patient-specific permittivity estimation on algorithm comparison is highlighted using representative experimental breast phantoms. In particular, the case studies presented help show that the permittivity estimate can impact the conclusions of the algorithm comparison. Overall, this work suggests that it is important that imaging algorithm comparisons use realistic test cases with and without breast abnormalities and with reconstruction permittivity estimation.

Coherent UWB Radar-on-Chip for In-Body Measurement of Cardiovascular Dynamics

Coherent ultra-wideband (UWB) radar-on-chip technology shows great promise for developing portable and low-cost medical imaging and monitoring devices. Particularly monitoring the mechanical functioning of the cardiovascular system is of interest, due to the ability of radar systems to track sub-mm motion inside the body at a high speed. For imaging applications, UWB radar systems are required, but there are still significant challenges with in-body sensing using low-power microwave equipment and wideband signals. Recently, it was shown for the first time, on a single subject, that the arterial pulse wave can be measured at various locations in the body, using a coherent UWB radar-on-chip technology. This paper provides more substantial evidence, in the form of new measurements and improved methods, to demonstrate that cardiovascular dynamics can be measured using radar-on-chip. Results across four participants were found to be robust and repeatable. Cardiovascular signals were recorded using radar-on-chip systems and electrocardiography (ECG). Through ECG-aligned averaging, the arterial pulse wave could be measured at a number of locations in the body. Pulse arrival time could be determined with high precision, and blood pressure pulse wave propagation through different arteries was demonstrated. In addition, cardiac dynamics were measured from the chest. This paper serves as a first step in developing a portable and low-cost device for long-term monitoring of the cardiovascular system and provides the fundamentals necessary for developing UWB radar-on-chip imaging systems.

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.

Qualitative microwave imaging of breast cancer with contrast agents

A microwave imaging (MWI) methodology for early diagnosis of breast cancer is presented. Instead of generating a tomographic image of the breast, the proposed technique aims to reconstruct a map of malignant tumours inside the breast by adopting an extended form of factorization method. The implementation of factorization method requires (i) two multi-static scattered field measurements around the breast, which correspond to two different states of the breast, and (ii) the inhomogeneous Green's function associated to the breast. For this purpose, the paper proposes the use of contrast agents, which selectively increase the dielectric properties of the malign tissues. Two multi-static field measurements are collected before and after the administration of contrast agents. Later, the inhomogeneous Green's function of the breast is estimated by back-propagating the scattered field measurements, which are taken before the contrast agent usage. The feasibility and efficiency of the proposed technique are demonstrated with numerical examples that are performed on a slice of the realistic breast phantoms, which are derived from real three dimensional magnetic resonance imaging (3D-MRI) measurements.

Biosensing-by-Learning Direct Targeting Strategy for Enhanced Tumor Sensitization

We propose a novel iterative-optimization-inspired direct targeting strategy (DTS) for smart nanosystems, which harness swarms of externally manipulable nanoswimmers assembled by magnetic nanoparticles (MNPs) for knowledge-aided tumor sensitization and targeting. We aim to demonstrate through computational experiments that the proposed DTS can significantly enhance the accumulation of MNPs in the tumor site, which serve as a contrast agent in various medical imaging modalities, by using the shortest possible physiological routes and with minimal systemic exposure. The epicenter of a tumor corresponds to the global maximum of an externally measurable objective function associated with an in vivo tumor-triggered biological gradient; the domain of the objective function is the tissue region at a high risk of malignancy; swarms of externally controllable magnetic nanoswimmers for tumor sensitization are modeled as the guess inputs. The objective function may be resulted from a passive phenomenon such as reduced blood flow or increased kurtosis of microvasculature due to tumor angiogenesis; otherwise, the objective function may involve an active phenomenon such as the fibrin formed during the coagulation cascade activated by tumor-targeted "activator" nanoparticles. Subsequently, the DTS can be interpreted from the iterative optimization perspective: guess inputs (i.e., swarms of nanoswimmers) are continuously updated according to the gradient of the objective function in order to find the optimum (i.e., tumor) by moving through the domain (i.e., tissue under screening). Along this line of thought, we propose the computational model based on the gradient descent (GD) iterative method to describe the GD-inspired DTS, which takes into account the realistic in vivo propagation scenario of nanoswimmers. By means of computational experiments, we show that the GD-inspired DTS yields higher probabilities of tumor sensitization and more significant dose accumulation compared to the "brute-force" search, which corresponds to the systemic targeting scenario where drug nanoparticles attempt to target a tumor by enumerating all possible pathways in the complex vascular network. The knowledge-aided DTS has potential to enhance the tumor sensitization and targeting performance remarkably by exploiting the externally measurable, tumor-triggered biological gradients. We believe that this work motivates a novel biosensing-by-learning framework facilitated by externally manipulable, smart nanosystems.

Detectability of Breast Tumors in Excised Breast Tissues of Total Mastectomy by IR-UWB-Radar-Based Breast Cancer Detector

The objective of this paper is to investigate the detectability of breast tumors having various histological types in excised breast tissues of total mastectomy. The tumor images measured by a portable impulse-radio-ultra-wideband (IR-UWB)-radar-based breast cancer detector are compared with both pathological images and images of dedicated breast positron emission tomography. It is found that the detector can detect invasive-ductal-carcinomas and extensive intraductal component in the dense breast. The density of the breast has a correlation to the effective permittivity derived from the reconstructed confocal images. The results show that the IR-UWB-radar-based breast cancer detector has a potential as a portable modality for early-stage breast cancer screening.

A Homogeneous Breast Phantom Measurement System with an Improved Modified Microwave Imaging Antenna Sensor

Microwave breast imaging has been reported as having the most potential to become an alternative or additional tool to the existing X-ray mammography technique for detecting breast tumors. Microwave antenna sensor performance plays a significant role in microwave imaging system applications because the image quality is mostly affected by the microwave antenna sensor array properties like the number of antenna sensors in the array and the size of the antenna sensors. In this paper, a new system for successful early detection of a breast tumor using a balanced slotted antipodal Vivaldi Antenna (BSAVA) sensor is presented. The designed antenna sensor has an overall dimension of 0.401λ × 0.401λ × 0.016λ at the first resonant frequency and operates between 3.01 to 11 GHz under 10 dB. The radiating fins are modified by etching three slots on both fins which increases the operating bandwidth, directionality of radiation pattern, gain and efficiency. The antenna sensor performance of both the frequency domain and time domain scenarios and high-fidelity factor with NFD is also investigated. The antenna sensor can send and receive short electromagnetic pulses in the near field with low loss, little distortion and highly directionality. A realistic homogenous breast phantom is fabricated, and a breast phantom measurement system is developed where a two antennas sensor is placed on the breast model rotated by a mechanical scanner. The tumor response was investigated by analyzing the backscattering signals and successful image construction proves that the proposed microwave antenna sensor can be a suitable candidate for a high-resolution microwave breast imaging system.

Comparison of X-ray-Mammography and Planar UWB Microwave Imaging of the Breast: First Results from a Patient Study

Hemispherical and cylindrical antenna arrays are widely used in radar-based and tomography-based microwave breast imaging systems. Based on the dielectric contrast between healthy and malignant tissue, a three-dimensional image could be formed to locate the tumor. However, conventional X-ray mammography as the golden standard in breast cancer screening produces two-dimensional breast images so that a comparison between the 3D microwave image and the 2D mammogram could be difficult. In this paper, we present the design and realisation of a UWB breast imaging prototype for the frequency band from 1 to 9 GHz. We present a refined system design in light of the clinical usage by means of a planar scanning and compare microwave images with those obtained by X-ray mammography. Microwave transmission measurements were processed to create a two-dimensional image of the breast that can be compared directly with a two-dimensional mammogram. Preliminary results from a patient study are presented and discussed showing the ability of the proposed system to locate the tumor.

Effects of Interpatient Variance on Microwave Breast Images: Experimental Evaluation

Microwave breast imaging has seen significant developments in recent years, including new clinical trials and formation of a number of spin-out companies. Although many algorithms for microwave breast imaging have been developed, there are significant challenges in translating these algorithms to the clinic. For example, movement due to patient breathing can affect the scan, and both the breast and breast abnormalities vary significantly from patient to patient. As breast density is a known independent risk factor for cancer and cancerous tumours have different shapes and margins to benign tumours, the effect of interpatient variance on the microwave image is important. This work analyses the effect on image quality of tumour shape, size and breast density. Using the diverse and representative BRIGID experimental dataset, images of a variety of tumours are compared to images without tumours present. This work suggests that it is difficult to distinguish images with and without tumours present using existing metrics.

Portable impulse-radar detector for breast cancer: a pilot study

Microwave breast imaging is a painless and nonradiation method. This pilot study aimed to evaluate the detective capability and feasibility of a prototype of a portable breast cancer detector using a radar-based imaging system. Five patients with histologically confirmed breast cancers with a minimum diameter of 1 cm were enrolled in this study. The antenna array dome of the device was placed on the breast of the patient in a supine position for 15 min per single examination. The primary endpoint was a detection rate of breast cancers. The secondary endpoints were positional accuracy and adverse event. All five targeted breast tumors were detected and were visualized at the sites confirmed by other diagnostic modalities. Among five tumors, one was not detected via mammography because of heterogeneously dense breast and another was a microinvasive carcinoma of invasive tumor size 0.5 mm. No study-related adverse events occurred. The prototype of a portable breast cancer detector has sufficient detective capability, is safe for clinical use, and might detect an early stage breast cancer, such as noninvasive carcinoma. Future developments should focus on further decreasing the size of the machine and shortening inspection time.

Active Sensor for Microwave Tissue Imaging with Bias-Switched Arrays

A prototype of a bias-switched active sensor was developed and measured to establish the achievable dynamic range in a new generation of active arrays for microwave tissue imaging. The sensor integrates a printed slot antenna, a low-noise amplifier (LNA) and an active mixer in a single unit, which is sufficiently small to enable inter-sensor separation distance as small as 12 mm. The sensor’s input covers the bandwidth from 3 GHz to 7.5 GHz. Its output intermediate frequency (IF) is 30 MHz. The sensor is controlled by a simple bias-switching circuit, which switches ON and OFF the bias of the LNA and the mixer simultaneously. It was demonstrated experimentally that the dynamic range of the sensor, as determined by its ON and OFF states, is 109 dB and 118 dB at resolution bandwidths of 1 kHz and 100 Hz, respectively.