Radar-based Microwave Imaging Research Articles

Accurate blood glucose level monitoring using microwave imaging

Painless and non-invasive detection techniques are needed to replace finger-prick blood collection for people with diabetes. A first-of-its-kind, noninvasive, and continuous blood glucose level (BGL) detection method based on microwave imaging is introduced in this paper. This method avoids the complex task of frequency choice for the design of electromagnetic sensors. A radar-based microwave imaging technology combined with an improved very-deep super-resolution (VDSR-BL) method is presented to obtain high-resolution (HR) microwave images. After image super-resolution reconstruction by VDSR-BL, the peak signal-to-noise ratio and structural similarity index of HR images reach 35.4461 dB and 0.9761, respectively. Then, an ensemble learning strategy based on support vector regression and random forest algorithms is proposed to identify HR microwave images for BGL estimation. The developed detection system has been verified on the medium under tests with different glucose solutions. The final detection results obtain a root mean squared error of 0.1394 mg ml−1 and a mean absolute relative difference of 8.02%, which show good accuracy with clinical acceptance. Meanwhile, we also conducted human trials. A high correlation coefficient (R) of 0.9254 was achieved between the results of microwave imaging and invasive BGL. Together, these results show that microwave imaging offers a promising new approach for noninvasive BGL monitoring.

Enhanced Physics Modelling in Radar-Based Microwave Imaging for Breast Cancer Detection

Enhanced Physics Modelling in Radar-Based Microwave Imaging for Breast Cancer Detection

Design and analysis of a super compact UWB antenna for accurate detection of breast tumors using monostatic radar‐based microwave imaging technique

AbstractThis article proposes a low‐profile ultra‐wideband (UWB) antenna for breast tumor detection using the monostatic radar‐based microwave imaging (RBMI) technique. The proposed UWB antenna consists of a circular patch and ground plane along with a modified tapered feed line having a total size of 13 × 9 × 1.6 mm3. The designed antenna operates from 4 to 11 GHz based on a −10 dB reflection coefficient with a peak gain of 9.5 dBi at 4.8 GHz. Experimental validation is carried out after fabricating the prototype of the UWB antenna and the breast mimic. The measured backscattered signals are captured using the vector network analyzer (E‐5063A) by keeping the antenna at 10 mm from the breast phantom. After that, post‐processing of the measured data is done to detect the exact depth and location of the malignant tissue. The proposed antenna uses UWB band frequency of operation because it has some unique features, that is, it provides immunity to multipath fading, has a simple design, has a low cost of fabrication, and has biological friendliness. Moreover, due to the proposed antenna's resonance at a lower resonant frequency better depth of penetration is achieved in human body phantoms and the proposed antenna provides specific absorption rates (SAR) of 0.877 W/kg over 1 g of tissue at 4.8 GHz, which is an acceptable limit for SAR as per FCC guidelines. Therefore, the proposed antenna and monostatic RBMI technique are good candidates for breast tumor detection.

Radar-Based Microwave Breast Imaging Using Neurocomputational Models

In this study, neurocomputational models are proposed for the acquisition of radar-based microwave images of breast tumors using deep neural networks (DNNs) and convolutional neural networks (CNNs). The circular synthetic aperture radar (CSAR) technique for radar-based microwave imaging (MWI) was utilized to generate 1000 numerical simulations for randomly generated scenarios. The scenarios contain information such as the number, size, and location of tumors for each simulation. Then, a dataset of 1000 distinct simulations with complex values based on the scenarios was built. Consequently, a real-valued DNN (RV-DNN) with five hidden layers, a real-valued CNN (RV-CNN) with seven convolutional layers, and a real-valued combined model (RV-MWINet) consisting of CNN and U-Net sub-models were built and trained to generate the radar-based microwave images. While the proposed RV-DNN, RV-CNN, and RV-MWINet models are real-valued, the MWINet model is restructured with complex-valued layers (CV-MWINet), resulting in a total of four models. For the RV-DNN model, the training and test errors in terms of mean squared error (MSE) are found to be 103.400 and 96.395, respectively, whereas for the RV-CNN model, the training and test errors are obtained to be 45.283 and 153.818. Due to the fact that the RV-MWINet model is a combined U-Net model, the accuracy metric is analyzed. The proposed RV-MWINet model has training and testing accuracy of 0.9135 and 0.8635, whereas the CV-MWINet model has training and testing accuracy of 0.991 and 1.000, respectively. The peak signal-to-noise ratio (PSNR), universal quality index (UQI), and structural similarity index (SSIM) metrics were also evaluated for the images generated by the proposed neurocomputational models. The generated images demonstrate that the proposed neurocomputational models can be successfully utilized for radar-based microwave imaging, especially for breast imaging.

Textile monopole sensors for breast cancer detection

This paper presents two flexible monopole antennas implemented on cotton substrate as sensors designed for radar-based microwave imaging and particularly breast imaging systems. The flexible antennas are basic building block of a radar-based microwave imaging system that could be integrated in clothes. Thus, these low-cost textile sensors could be used by women for self-monitoring and early breast cancer detection at an affordable price. The ultra-wideband fully textile sensors are designed as rectangular and circular monopole antennas. Both sensors have an impedance bandwidth le − 10 dB from 2.2 to 8 GHz with an overall footprint of 50 × 50 mm2. Simulations for antenna in proximity to breast model with and without tumor as well as bending capacity are performed. The simulations are complemented with fabrication of a breast phantom and a tumor sample with parameters that mimic these of the human breast’s healthy and malignant tissue. Measurements are compared to simulation results as well as the performance of antenna before and after subjected to washing. Finally, the specific absorption rate is also calculated and measured to ensure safety for on-body deployment. The proposed work demonstrates the potential to develop wearable microwave imaging system using fully textile antennas.

A novel gain enhanced Vivaldi antenna for a breast phantom measurement system

ABSTRACT In this study, a breast phantom measurement system based on radar-based microwave imaging technique is proposed. For this purpose, a small-size Vivaldi antenna with high gain and ultra-wide band is designed. The antenna bandwidth is in the range of 3–10.6 GHz, and the maximum realized gain is 8.5 dB. The size of the antenna is 36 × 36 mm2, and FR4 was used as dielectric material. Bandwidth and gain are increased by using edge slots. After the antenna hardware had been manufactured with PCB printed circuit technology, a mechanism was developed to measure breast phantom. After the measurement process is completed, a computer program converts the collected signals into images. For the testing of the system, canola oil was used as a phantom and the object in the oil was successfully imaged.

Reduced Graphene Oxide UWB Array Sensor: High Performance for Brain Tumor Imaging and Detection.

A low cost, with high performance, reduced graphene oxide (RGO) Ultra-wide Band (UWB) array sensor is presented to be applied with a technique of confocal radar-based microwave imaging to recognize a tumor in a human brain. RGO is used to form its patches on a Taconic substrate. The sensor functioned in a range of 1.2 to 10.8 GHz under UWB frequency. The sensor demonstrates high gain of 5.2 to 14.5 dB, with the small size of 90 mm × 45 mm2, which can be easily integrated into microwave imaging systems and allow the best functionality. Moreover, the novel UWB RGO array sensor is established as a detector with a phantom of the human head. The layers' structure represents liquid-imitating tissues that consist of skin, fat, skull, and brain. The sensor will scan nine different points to cover the whole one-sided head phantom to obtain equally distributed reflected signals under two different situations, namely the existence and absence of the tumor. In order to accurately detect the tumor by producing sharper and clearer microwave image, the Matrix Laboratory software is used to improve the microwave imaging algorithm (delay and sum) including summing the imaging algorithm and recording the scattering parameters. The existence of a tumor will produce images with an error that is lower than 2 cm.

A low cost and efficient breast cancer detection method with a staircase shaped ultrawide band dielectric resonator antenna using monostatic radar based microwave imaging technique

In this article, a staircase-shaped ultra-wide band dielectric resonator antenna (DRA) has been used as a sensor for the detection of breast tumor by monostatic radar-based microwave imaging (MRMWI). The proposed DRA has fractional bandwidth (BW) 98.5% and a high peak gain 5.98 dB along with dual polarization behaviour from 5.12 to 8.2 GHz and 11.02-13.8 GHz. In the MRMWI setup, DRA is placed over the breast phantom at a distance of 7 mm and provides a safe exposure of radiation (<1.6 W/Kg). For simulation, it rotates around the phantom at a fixed interval in elevation (0-180°) and azimuthal (0-360°) planes. It works as a radiate and receives the reflected signals towards and from the scanned area simultaneously. To validate the results, fabricated DRA is connected to a vector network analyzer and rotates (as done for simulation) around the artificial breast phantom. That is a replica of the human breast made from gelatin+sugar, Vaseline and wheat flour+water equivalent to skin, fat and tumor respectively. Afterward S11 responses are recorded in the presence and absence of tumor inside the phantom. A significant variation in recorded values leads to the detection of tumors that are processed further in beam-forming algorithms; delay and sum (DAS) and delay-multiply and sum (DMAS) to reform the 2-dimensional image of tumor in MATLAB.

“C” shaped dual polarized dielectric resonator antenna for the microwave imaging of breast tumor using beam‐forming algorithms

In this article, an ultra-wideband (UWB) monostatic radar based microwave imaging (MWI) approach is proposed to detect the presence and localize the position of breast tumor in women (usually greater than 45 years of age). For this purpose, a “C” shaped elliptically polarized DRA (dielectric resonator antenna) with an impedance bandwidth of 7.3 GHz (i.e., 3.4–10.7 GHz) covering the UWB has been proposed as a sensor to record the backscattered S parameter signals from the breast tissues. This is done by rotating the proposed DRA in monostatic radar configuration around the breast phantom with an interval of 30° in elevation (0–180) and azimuthal planes (0–360), respectively. The recorded “S” parameter data is then processed using beam-forming algorithms namely delay and sum (DAS), improved delay and sum (IDAS), delay multiply and sum (DMAS) to form 2D images of the breast tumor and locate its position (using MATLAB R 2013a). The design and simulation of the entire set up that includes the C shaped UWB DRA & Bio media (breast phantom) is done using CST MWS V'17. The fabrication of the proposed DRA is carried out using wet etching and water jet cutting process. The physical model of the Bio media is prepared using gelatin (skin), petroleum-jelly (fat), and wheat-flour (tumor). The validation of antenna radiation parameters is done using a VNA (vector network analyzer) model no. E5063A and good agreement between the simulated and measured results is observed. The proposed C shaped DRA is used in a monostatic radar based configuration to collect “S” parameters and the data is processed in a GPU to reconstruct a 2D image of the scanned breast phantom area. The size and detected location of tumor using beam-forming is also close to the actual physical location of tumor with an error of 6.2% from the actual tumor location.

Monostatic Radar-Based Microwave Imaging of Breast Tumor Using an Ultra-wideband Dielectric Resonator Antenna (DRA) with a Sierpinski Fractal Defected Ground Structure

Monostatic Radar-Based Microwave Imaging of Breast Tumor Using an Ultra-wideband Dielectric Resonator Antenna (DRA) with a Sierpinski Fractal Defected Ground Structure

Experimental Evaluation of an Axillary Microwave Imaging System to Aid Breast Cancer Staging

The number of metastasised Axillary Lymph Nodes (ALNs) is a key indicator for breast cancer staging. Its correct assessment affects subsequent therapeutic decisions. Common ALN screening modalities lack high enough sensitivity and specificity. Level I ALNs produce detectable backscattering of microwaves, opening the way for Microwave Imaging (MWI) as a complementary screening modality. Radar-based MWI is a low-cost, non-invasive technique, widely studied for breast cancer and brain stroke detection. However, new specific challenges arise for ALN detection, which deter a simple extension of existing MWI methods. The geometry of the axillary region is more complex, limiting the antenna travel range required for maximum resolution. Additionally, unlike breast MWI setups, it is impractical to use liquid immersion to enhance energy coupling to the body; therefore, higher skin reflection masks ALNs response. We present a complete study that proposes dedicated imaging algorithms to detect ALNs dealing with the above constraints, and evaluate their effectiveness experimentally. We describe the developed setup based on a 3D-printed anthropomorphic phantom, and the antenna-positioning configuration. To the authors’ knowledge, this is the first ALN-MWI study involving a fully functional anatomically compliant setup. A Vivaldi antenna, operating in a monostatic radar mode at 2-5 GHz, scans the axillary region. Pre-clinical assessment in different representative scenarios shows Signal-to-Clutter Ratio higher than 2.8 dB and Location Error lower than 15 mm, which is smaller than considered ALN dimensions. Our study shows promising level I ALN detection results despite the new challenges, confirming MWI potential to aid breast cancer staging.

Microstrip Antenna for Radar-Based Microwave Imaging of Breast Cancer: Simulation Analysis

The paper proposes a radar-based microwave imaging system for breast cancer detection by using a simple and effective design of a wideband microstrip patch antenna. Breast cancer is the second-highest cause of death among women worldwide and the only way to provide an effective treatment is to detect it at an early stage. Microwave imaging (MI) provides a safer and cheaper detection method as it uses low-level non-ionizing radiation. A compact wideband microstrip patch antenna working in the frequency range of 3.6 to 9.2 GHz frequency is used for the MI system. The antenna has a minimum return loss of -48 dB and a maximum gain of 4.5 dBi. Antenna is scanned over the breast tissue and a significant contrast in the reflected signals in the presence of the tumor is reported. The reflected signals at different positions of the antenna are then used to create a 2D image of the breast tissue showing the position of the tumor inside it. Specific absorption rate is also calculated in the tissue and is found well below the safety levels.

CMOS Gaussian Monocycle Pulse Transceiver for Radar-Based Microwave Imaging

A single-chip Gaussian monocycle pulse (GMP) transceiver was developed for radar-based microwave imaging by the use of 65-nm complementary metal oxide semiconductor (CMOS) technology. A transmitter (TX) generates GMP signals, whose pulse widths and -3 dB bandwidths are 192 ps and 5.9 GHz, respectively. A 102.4 GS/s equivalent time sampling receiver (RX) performs the minimum jitter, input referred noise, signal-to-nose-ratio (SNR), signal-to-noise and distortion ratio (SNDR) effective number of bits (ENOB) of 0.58 ps, 0.24 mVrms, 28.4 dB, 26.6dB and 4.1 bits, respectively. The SNR for the bandwidth of 3.6 GHz is 36.3 dB. The power dissipations of transmitter and receiver circuits are 19.79 mW and 48.87 mW, respectively. The GMP transceiver module can differentiate two phantom targets with the size of 1 cm and the spacing of 1 cm by confocal imaging.

Near-Field Radar-Based Microwave Imaging for Breast Cancer Detection: A Study on Resolution and Image Quality

Radar-based microwave imaging (MWI) systems have shown encouraging results in early detection of breast cancer; however, there exist remaining challenges. Resolution is one of the most important issues not being addressed with clear theoretical relations in medical MWI systems. In this analytical study, we thoroughly clarify the relation of resolution to the most fundamental aspects of an MWI system, namely, limited-view versus full-view array geometry, monostatic versus multistatic configuration, single-frequency versus wideband operation, and near-field versus far-field imaging. This goal is achieved by expressing the inverse scattering problem in the spatial frequency domain and utilizing K-space representation. Besides, to analyze the MWI system more accurately, the sidelobe level (SLL) of its point-spread function (PSF) is taken into account. We show that generally, the resolution limits of a multistatic configuration are the same as its monostatic counterpart and, hence, have no superiority in terms of resolution. In addition, in imaging with a full-view array, the signal bandwidth has no effect on the resolution. Thus, the single-frequency operation provides the same resolution as the wideband operation. We also demonstrate that the effect of using multistatic configuration or wideband operation is to reduce the SLL of the PSF and, hence, improve the image quality.

A Two-Stage Rotational Surface Clutter Suppression Method for Microwave Breast Imaging With Multistatic Impulse-Radar Detector

Radar-based microwave imaging is a promising technique for multiple medical applications, such as breast health monitoring, breast cancer detection, ablation monitoring, and chemotherapy monitoring. In our previous work, a hand-held multistatic CMOS impulse-radar detector was developed. It was designed to be placed directly on the patient in a supine position without a coupling medium. In the radar-based microwave breast imaging, the surface clutters, which are generated from skin reflection, imperfect contacts, and antenna coupling, deteriorate the imaging quality. This article proposes a two-stage rotational clutter suppression method for the multistatic rotational configuration of the radar detector. It consists of a signal selection stage and an adaptive filtering stage. The novelty of the proposed method is that a prior constraint using L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> regularization is introduced to facilitate the filtering. Meanwhile, the antenna and channel variations are considered in the signal selection. The performance of the proposed method is verified in experiments with different surface conditions and patient trial. The results demonstrated that the proposed method is effective in suppressing the surface clutters and improving the image quality.

The impact of the inverse chirp z-transform on breast microwave radar image reconstruction

This work examines the impact of the inverse chirp z-transform (ICZT) for frequency-to-time-domain conversion during image reconstruction of a pre-clinical radar-based breast microwave imaging system operating over 1–8 GHz. Two anthropomorphic breast phantoms were scanned with this system, and the delay-multiply-and-sum beamformer was used to reconstruct images of the phantoms, after using either the ICZT or the inverse discrete Fourier transform (IDFT) for frequency-to-time domain conversion. The contrast, localization error, and presence of artifacts in the reconstructions were compared. The use of the IDFT resulted in prominent ring artifacts that were not present when using the ICZT, and the use of the ICZT resulted in higher contrast between the tumor and clutter responses. In one of the phantoms, the tumor response was only visible in reconstructions that used the ICZT. The use of the ICZT evaluated with a time-step size of 11 ps resulted in the reduction of prominent artifacts present when using the IDFT and the successful identification of the tumor response in the reconstructed images.

Development of a 3D Anthropomorphic Phantom Generator for Microwave Imaging Applications of the Head and Neck Region.

The development of 3D anthropomorphic head and neck phantoms is of crucial and timely importance to explore novel imaging techniques, such as radar-based MicroWave Imaging (MWI), which have the potential to accurately diagnose Cervical Lymph Nodes (CLNs) in a neoadjuvant and non-invasive manner. We are motivated by a significant diagnostic blind-spot regarding mass screening of LNs in the case of head and neck cancer. The timely detection and selective removal of metastatic CLNs will prevent tumor cells from entering the lymphatic and blood systems and metastasizing to other body regions. The present paper describes the developed phantom generator which allows the anthropomorphic modelling of the main biological tissues of the cervical region, including CLNs, as well as their dielectric properties, for a frequency range from 1 to 10 GHz, based on Magnetic Resonance images. The resulting phantoms of varying complexity are well-suited to contribute to all stages of the development of a radar-based MWI device capable of detecting CLNs. Simpler models are essential since complexity could hinder the initial development stages of MWI devices. Besides, the diversity of anthropomorphic phantoms resulting from the developed phantom generator can be explored in other scientific contexts and may be useful to other medical imaging modalities.

Microwave Imaging for Breast Cancer Detection: The Discrimination of Breast Lesion Morphology

Radar based microwave imaging system is proposed and used for breast cancer detection. Tumour location can be detected based on high dielectric differences between tumour and surrounding tissues. In clinical medicine, malignant tumour usually has more irregular morphology than benign tumour. However, previous work has ignored the discrimination between benign and malignant tumours, and this issue is solved in our work. In this paper, two major novel contributions are presented. Firstly, a complex natural responses (CNRs) method is proposed for discrimination between the benign and malignant tumours. The CNRs of the tumour show a unique feature that the CNRs only depend on the morphological appearance of the tumour. Simulation results show that the use of the CNRs can successfully discriminate between a 5 mm radius spherical benign tumour and a 5 mm average radius malignant tumour with a spiculated periphery. Secondly, microwave images of the benign and malignant tumours are created by a proposed mono-static imaging system which uses a single Vivaldi antenna to scan the breast. Both benign and malignant tumour positions can be correctly indicated from the images.

Microwave Breast Imaging Using a Dry Setup

This article demonstrates for the first time, both numerically and experimentally, the feasibility of radar-based microwave imaging of anthropomorphic heterogeneously dense breasts in prone position, requiring no immersion liquid. The dry, contactless approach greatly simplifies the setup, favors patient comfort, and further avoids lengthy sanitation procedures after each exam. We use a radar-type technique with the antennas distributed in cylindrical configuration around the breast phantom. The reflectivity map is reconstructed using a wave-migration algorithm in the frequency domain. This article presents new developed strategies to cope with the challenges of a dry setup, namely increased skin artifact due to the concomitant absence of matching liquid and nonuniform breast shape. We propose an iterative and adaptive algorithm based on singular value decomposition that effectively removes the skin backscattering under the abovementioned conditions. It is compatible with automatic processing, and computationally fast. One of its inputs is the breast three-dimensional surface information, and its distance to the antennas, all obtained automatically from a proposed low-cost procedure based on a webcam. The imaging method is reasonably resilient to the presence of fibroglandular tissues, and to uncertainties of tissue permittivity. Another tackled challenge is the miniaturization of the antenna in air, which is achieved with an optimized balanced antipodal Vivaldi of the same size as counterparts used in dense immersion liquids. Finally, all the building blocks are combined to demonstrate experimentally the overall dry system performance, with very good detection of the tumor at three different positions in the breast, even in low-contrast scenarios.

A Phase Shift and Sum Method for UWB Radar Imaging in Dispersive Media

A phase shift and sum (PSAS) algorithm to image objects in dispersive media is presented. The algorithm compensates the phase shift of the scattered field from the receiver to the source for each frequency component in an ultrawideband (UWB) and then integrates all the frequency responses. This method resolves the multispeed and multipath issue when UWB signals propagate in dispersive media. In addition, a multipath effect due to refraction on a curved boundary is also explored. By collecting data using a customized microwave measurement system of two different objects placed in a plastic graduated cylinder filled with glycerin, along the measured dielectric parameters of glycerin (a dispersive medium), highquality reconstructed images are formed using PSAS. Quantitative and qualitative comparisons with two other traditional time-shift radar-based microwave imaging algorithms for the same objects under test demonstrate the advantages of PSAS.