Head Imaging System Research Articles

FLEXIBLE WIDE BAND TRAPEZOIDAL ANTENNA FOR ELECTROMAGNETIC HEAD IMAGING SYSTEM

This work presents a method for developing and fabricating a compact, flat, wideband antenna that can be used in electromagnetic (EM) head imaging systems. The trapezoidal antenna is constructed using a Taconic TLY-3 dielectric substrate. The antenna is designed to function within the frequency range of 700 MHz to 3 GHz. Microwave imaging (MI) devices used in biological applications provide crucial attributes, including broad bandwidth, compact dimensions, and adaptability. A comparison is made between the modeling and measurement data for the reflection coefficient and radiation pattern.

Experimental Verification of Microwave Head Imaging System Using Phantoms Fabricated from Artificial Tissue-Mimicking Materials

Experimental Verification of Microwave Head Imaging System Using Phantoms Fabricated from Artificial Tissue-Mimicking Materials

Design of Compact Fragment-Type Antenna Array for Microwave-Based Head Imaging Application

Specifically designed antennas are needed to meet the needs of head imaging applications where an antenna array is typically used. In this article, the design of a compact fragment-type antenna using a genetic algorithm is proposed. The proposed antenna is fabricated by a low-cost FR4 substrate and covers a 72.4% fractional bandwidth (1.92 to 4.1 GHz), with an average gain of more than 2.02 dBi. The overall structure of the proposed antenna is equivalent to 0.29 λ0 × 0.19 λ0, where λ0 is the corresponding wavelength at 1.92 GHz. The results indicate that the proposed antenna is compact and suitable for microwave-based head imaging. An effective microwave head imaging system with an array of 8 compact fragment-type antennas is designed, with one antenna working as a transmitter and the others working as a receiver in turn. The imaging performance is investigated with blood layer inside the head model using the Microwave Radar- Based Imaging Toolbox open-source software. The detection of the presence and location of stroke is done by analyzing the compact fragment-type antennas’ backscattered signal.

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.

Design and Modelling of Graphene-Based Flexible 5G Antenna for Next-Generation Wearable Head Imaging Systems.

Arguably, 5G and next-generation technology with its key features (specifically, supporting high data rates and high mobility platforms) make it valuable for coping with the emerging needs of medical healthcare. A 5G-enabled portable device receives the sensitive detection signals from the head imaging system and transmits them over the 5G network for real-time monitoring, analysis, and storage purposes. In terms of material, graphene-based flexible electronics have become very popular for wearable and healthcare devices due to their exceptional mechanical strength, thermal stability, high electrical conductivity, and biocompatibility. A graphene-based flexible antenna for data communication from wearable head imaging devices over a 5G network was designed and modelled. The antenna operated at the 34.5 GHz range and was designed using an 18 µm thin graphene film for the conductive radiative patch and ground with electric conductivity of 3.5 × 105 S/m. The radiative patch was designed in a fractal fashion to provide sufficient antenna flexibility for wearable uses. The patch was designed over a 1.5 mm thick flexible polyamide substrate that made the design suitable for wearable applications. This paper presented the 3D modelling and analysis of the 5G flexible antenna for communication in a digital care-home model. The analyses were carried out based on the antenna's reflection coefficient, gain, radiation pattern, and power balance. The time-domain signal analysis was carried out between the two antennas to mimic real-time communication in wearable devices.

An On-Body Matched Differentially Fed Magnetoelectric Dipole Antenna for Head Imaging Systems

An on-body matched differentially fed magnetoelectric (ME) dipole antenna is proposed for the head imaging system to detect intracranial hemorrhage (ICH). The ME dipole with its differential feed achieves a wide impedance bandwidth of 137% from 0.45 to 2.4 GHz. The antenna exhibits unidirectional and symmetrical radiation patterns in the near-field region, leading to a stable main beam toward the 2-D imaging domain inside the human head. The high front-to-back ratio (FBR) above 18 dB is realized in the whole frequency band by this antenna. The values of specific absorption rate (SAR) always remain safe level under 0.82 W/kg in the entire band with an input power of 10 dBm. To validate the detection capability of the proposed antenna, a head imaging system composed of eight antenna elements in a co-polarization state is designed and fabricated. The reconstructed 2-D images of the head interior using a confocal imaging algorithm demonstrate the successful detection and localization of the circular bleeding area as small as 3 mm in radius numerically and 5 mm in radius experimentally, and it has the capability to detect multiple bleeding areas of the human brain, which indicates the head imaging system has the potential to be used in clinical trials.

Preparation of new flexible antenna based on sol–gel synthesized MgxCa(0.9-x)Zn0.10Fe2O4 nanoparticle for microwave imaging applications

This article prepares, MgxCa(0.90-x)Zn0.10Fe2O4 nanoparticle-based new flexible microwave substrate materials to build flexible antennas. The MgxCa(0.90-x)Zn0.10Fe2O4 nanoparticles are synthesized using the sol–gel chemical method. There are three different weight percentages are chosen for “X” values i.e., X = 25%, 50%, and 75%. X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and transmission electron microscopy (TEM) studies are used to evaluate the structural and morphological features of the produced nanoparticles. Dielectric Assessment Kit is also used to evaluate the nanoparticles' tunable dielectric characteristics (DAK). Loss tangents range from 0.00275 to 0.00675 while dielectric permittivity values range from 3.25 to 4.75. The magnetic characteristics of the produced samples are also investigated using the vibrating sample magnetometer (VSM). The Agilent 8501E module is used to calculate the adjustable magnetic permeability and magnetic loss tangent values. The magnetic permeability and magnetic loss tangent values obtained are in the range of 1.00–1.15 and 0.0035–0.0065, respectively. Later, a flexible antenna is designed on the prepared flexible substrate that operates within the frequency range of 1.60 GHz–3.00 GHz with a maximum gain of 5.15 dBi. After that, this antenna is incorporated with a nine-antenna array-based portable microwave head imaging system. Finally, successful brain tumor detection is observed by post-processing the collected scattering parameters with an image reconstruction algorithm. The overall results ensure that the MgxCa(0.90-x)Zn0.10Fe2O4 nanoparticle-based new flexible microwave substrate materials can be a potential candidate for microwave head imaging and are suitable to fit with microwave devices.

A portable non-invasive microwave based head imaging system using compact metamaterial loaded 3D unidirectional antenna for stroke detection

A metamaterial (MTM) loaded compact three-dimensional antenna is presented for the portable, low-cost, non-invasive microwave head imaging system. The antenna has two slotted dipole elements with finite arrays of MTM unit cell and a folded parasitic patch that attains directional radiation patterns with 80% of fractional bandwidth. The operating frequency of the antenna is 1.95–4.5 GHz. The optimization of MTM unit cell is performed to increase the operational bandwidth, realized gain, and efficiency of the antenna within the frequency regime. It is also explored to improve radiation efficiency and gain when placed to head proximity. One-dimensional mathematical modelling is analyzed to precisely estimate the power distribution that validates the performance of the proposed antenna. To verify the imaging capability of the proposed system, an array of 9 antennas and a realistic three-dimensional tissue-emulating experimental semi-solid head phantom are fabricated and measured. The backscattered signal is collected from different antenna positions and processed by the updated Iterative Correction of Coherence Factor Delay-Multiply-and-Sum beamforming algorithm to reconstruct the hemorrhage images. The reconstructed images in simulation and experimental environment demonstrate the feasibility of the proposed system as a portable platform to successfully detect and locate the hemorrhages inside the brain.

A deep learning model to classify and detect brain abnormalities in portable microwave based imaging system

Automated classification and detection of brain abnormalities like a tumor(s) in reconstructed microwave (RMW) brain images are essential for medical application investigation and monitoring disease progression. This paper presents the automatic classification and detection of human brain abnormalities through the deep learning-based YOLOv5 object detection model in a portable microwave head imaging system (MWHI). Initially, four hundred RMW image samples, including non-tumor and tumor(s) in different locations are collected from the implemented MWHI system. The RMW image dimension is 640 × 640 pixels. After that, image pre-processing and augmentation techniques are applied to generate the training dataset, consisting of 4400 images. Later, 80% of images are used to train the models, and 20% are used for testing. Later, from the 80% training dataset, 20% are utilized to validate the models. The detection and classification performances are evaluated by three variations of the YOLOv5 model: YOLOv5s, YOLOv5m, and YOLOv5l. It is investigated that the YOLOv5l model performed better compared to YOLOv5s, YOLOv5m, and state-of-the-art object detection models. The achieved accuracy, precision, sensitivity, specificity, F1-score, mean average precision (mAP), and classification loss are 96.32%, 95.17%, 94.98%, 95.28%, 95.53%, 96.12%, and 0.0130, respectively for the YOLOv5l model. The YOLOv5l model automatically detected tumor(s) accurately with a predicted bounding box including objectness score in RMW images and classified the tumors into benign and malignant classes. So, the YOLOv5l object detection model can be reliable for automatic tumor(s) detection and classification in a portable microwave brain imaging system as a real-time application.

Operational Learning-Based Boundary Estimation in Electromagnetic Medical Imaging

Incorporating boundaries of the imaging object as <i>a priori</i> information to imaging algorithms can significantly improve the performance of EM medical imaging systems. To avoid overly complicating the system by using different sensors and the adverse effect of the subject&#x2019;s movement, a learning-based method is proposed to estimate the boundary (external contour) of the imaged object using the same EM imaging data. While imaging techniques may discard the reflection coefficients for being dominant and uninformative for imaging, these parameters are made use of for boundary detection. The learned model is verified through independent clinical human trials by using a head imaging system with a 16-element antenna array that works across the band 0.7&#x2013;1.6 GHz. The evaluation demonstrated that the model achieves average dissimilarity of 0.012 in Hu-moment while detecting head boundary. The model enables fast scan and image creation while eliminating the need for additional devices for accurate boundary estimation.

Stable and Lifelong Head Phantoms Using Polymer Composition Mimicking Materials to Test Electromagnetic Medical Imaging Systems

Phantoms are critical items for testing and evaluating new prototypes of electromagnetic medical imaging systems. Realistic tissues distribution, size, shape, and dielectric properties of tissue-mimicking materials across the desired frequency band; in addition, long-term stability is required. In this study, a polymer composition that use to develop head tissues-mimicking materials that satisfy the aforementioned requirements. Polyepoxides (Epoxy), and assorted types of micro scale additive, including graphite, aluminium oxide, carbon black, and brass powders, are used to fabricate the phantom. Different mixing ratios are used to mimic four healthy head tissues; white matter, grey matter, skull, and skin. Blood-mimicking material is also included in the unhealthy phantom to represent stroke (hemorrhagic) at different locations and sizes. Also, water-based mimicking material is used to emulate cerebrospinal fluid (CSF) tissue. The measurements confirm close agreement to properties of actual head tissues across the frequency band 0.5–5 GHz, which has been used in the ongoing research activities of electromagnetic head imaging systems. Stability over time is investigated and compared with the widely used gelatin-in water-based mimicking materials. The results show the superiority of the developed phantom compared to currently using gelatin-based phantoms.

Measurement of whole-brain atrophy progression using microwave signal analysis

IntroductionWhole-brain atrophy is one of the key symptoms of neurodegenerative diseases such as Alzheimer and Parkinson. Monitoring the whole-brain atrophy is an important step to predict the stage of these diseases. Current diagnostic systems are expensive, bulky, and sometimes uncomfortable for patients to use. Microwave imaging systems are getting attention in recent years for head diagnostics due to being low-cost, portable, and nonionizing. ObjectiveThe objective of this paper is to present a processing pipeline, composed of various steps including data acquisition, and an integrated image reconstruction algorithm for a microwave head imaging system that shows the level of atrophy as a way of monitoring neurodegenerative diseases. MethodImages for energy distributions are obtained by applying an integrated imaging algorithm, to backscattered signals obtained from an array of six fully-textile directional antennas placed in a hat-like structure on the head. Novel pre-processing techniques are applied to signals to remove clutter effects. The beamformer creates images of scattering signals in the head exposed to microwave radiation over 0 to 3 GHz of the frequency range. The algorithm is validated on real lamb brain phantoms that imitate whole brain atrophy. A measured amount of the lamb brain sample was removed uniformly around the outer layer and replaced with an artificial phantom that represents the Cerebrospinal fluid. Results and conclusionThe results show that the proposed method can detect and locate whole-brain atrophy with sensible accuracy. The proposed method can potentially be applied for real-time monitoring of neurodegenerative diseases.

Flexible Electromagnetic Cap for Three-Dimensional Electromagnetic Head Imaging.

The timely treatment is the crucial element for the survival of patients with brain stroke. Thus, a fast, cost-effective, and portable device is needed for the early and on-the-spot diagnosis of stroke patients. A 3D electromagnetic head imaging system for rapid brain stroke diagnosis with a wearable and lightweight platform is presented. The platform comprises a custom-built flexible cap with a 24-element planar antenna array, and a flexible matching medium layer. The custom-built cap is made out of an engineered polymer-ceramic composite substrate of RTV silicone rubber and aluminum oxide (Al2O3) for enhanced dielectric properties and mechanical flexibility and robustness. The array is arranged into two elliptical rings that are entirely incorporated into the flexible cap. The employed antenna elements within the system are compact with low SAR values over the utilized frequency range of 0.9-2.5 GHz. Moreover, a flexible matching medium layer is introduced on the front of the apertures of the antenna array to enhance the impedance matching with the skin. The detection capability of the system is experimentally verified on 3D realistic head phantoms at multiple imaging scenarios and different types of strokes. The reconstructed 3D and 2D multi-slice images using the beamforming and polar sensitivity encoding (PSE) image processing algorithms indicate the applicability and potential of the system for onsite brain imaging.

Compact ellipse shaped patch with ground slotted broadband monopole patch antenna for head imaging applications

This paper represents an ellipse-shaped patch with a ground slotted broadband patch antenna for microwave head imaging systems. The proposed antenna constructs with a simple ellipse shaped square patch and modified slotted plane. The proposed design is very simple to fabricate and is enclosed in a microwave imaging system. The slotted patch, and the partial ground plane improves the antenna's efficiency, operating frequency range, and gain. The size of the proposed antenna is 70 × 60 × 1.5 mm3 with the electrical dimension being 0.277λ × 0.238λ × 0.006λ at a lower frequency of 1.19 GHz and connected to a 50Ω microstrip feeding line. This antenna is printed onto a low-cost FR-4 substrate whose relative permittivity is 4.4, and whose thickness is 1.5 mm. CST and HFSS software have been used for simulation and thereafter successful completion of the measurements and the fabrication. The comprehensive simulation exhibits that this design provides a bandwidth of 2.37 GHz (1.19 – 3.56 GHz) and 100% of the fractional bandwidths (% BW) with the reflection coefficient of <-10 dB. This antenna on FR-4 can produce an average gain of around 3.63 dBi with 5.95 dBi peak gain at whole operation frequencies. The prototype has a peak radiation efficiency of approximately 97% across the active frequency spectrum with 93% of average. The antenna does have an improved fidelity-factor (> 90 %) with a shorter group-delay. Several design modifications have been performed to get perfect, effective, and suitable results for microwave imaging applications. A 3D-realist Hugo head model is fitted with a single antenna and a 9-antenna array component to verify the performance of both the single antenna, and the configured array antenna. The antenna penetrates the brain human tissues satisfactorily. Across the operational range, the specific absorption rate (SAR) attains a limit of <1 W/kg. The analysis of both numeric and experimental evidence clearly indicates that the suggested antenna is ideal for microwave head-imaging implementations.

A Portable Electromagnetic Head Imaging System Using Metamaterial Loaded Compact Directional 3D Antenna

A non-invasive, low-powered, and portable electromagnetic (EM) head imaging system is presented using metamaterial (MTM) loaded compact directional 3D antenna. The antenna consists of two slotted dipole elements with $2\times 3$ and $3\times 3$ finite MTM array elements in top and ground, respectively, and folded parasitic elements that operate within the frequency range of 1.12 GHz to 2.5 GHz. The MTM array elements are optimized to enhance the overall performance regarding antenna bandwidth, realized gain, efficiency, and directionality in both free space and proximity to the head model. The mathematical modelling is also analyzed to justify the integration of MTM unit cells to the top and ground side of the antenna. The impact of MTM on SAR analysis is also performed. A tissue-mimicking 3D head phantom is fabricated and measured to validate the antenna performance. A nine-antenna portable setup is used with the fabricated phantom to measure and collect the scattering parameters that are later analyzed to detect and reconstruct the haemorrhage images by applying the updated IC-CF-DMAS algorithm. The overall performance demonstrates the feasibility of the proposed system as a portable platform to successfully detect, locate and monitor the haemorrhages inside the head in EM imaging system.

A YOLOv3 Deep Neural Network Model to Detect Brain Tumor in Portable Electromagnetic Imaging System

This paper presents the detection of brain tumors through the YOLOv3 deep neural network model in a portable electromagnetic (EM) imaging system. YOLOv3 is a popular object detection model with high accuracy and improved computational speed. Initially, the scattering parameters are collected from the nine-antenna array setup with a tissue-mimicking head phantom, where one antenna acts as a transmitter and the other eight antennas act as receivers. The images are then reconstructed from the post-processed scattering parameters by applying the modified delay-multiply-and-sum algorithm that contains 416×416 pixels. Fifty sample images are collected from the different head regions through the EM imaging system. The images are later augmented to generate a final image data set for training, validation, and testing, where the data set contains 1000 images, including fifty samples with a single and double tumor. 80% of the images are utilized for training the network, whereas 10% are used for validation, and the rest 10% are utilized for testing purposes. The detection performance is investigated with the different image data sets. The achieved detection accuracy and F1 scores are 95.62% and 94.50%, respectively, which ensure better detection accuracy. The training accuracy and validation losses are 96.74% and 9.21%, respectively. The tumor detection with its location in different cases from the testing images is evaluated through YOLOv3, which demonstrates its potential in the portable electromagnetic head imaging system.

Flexible Meander-Line Antenna Array for Wearable Electromagnetic Head Imaging

Portable lightweight electromagnetic (EM) head imaging systems for brain stroke detection are of great interest due to the need for fast and low-cost diagnosis tools. This communication presents a wideband and compact flexible meander-line antenna array for a wearable EM head imaging system. The antenna is designed and optimized on a 4 mm thick low-cost, flexible room-temperature-vulcanizing (RTV) silicone substrate in close vicinity to a multitissue head model. The antenna comprises two sections of meander-lines, a full ground plane, and a 50 Ω coaxial connector. The measured reflection coefficient |S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sub> | on a human head shows that the proposed antenna operates effectively across the frequency band of 0.45-3.6 GHz, attaining fractional bandwidth (FBW) of 155%. The antenna achieves unidirectional radiations with greater than 9 dB of front-to-rear ratio in the near-field region, and safe-level maximum specific-absorption rate (SAR) below 0.2 W/kg using an input power of 10 dBm. A 16 element antenna array was assembled and experimentally utilized in an EM head imaging system. The reconstructed images of abnormality-emulating targets using a confocal imaging algorithm demonstrate the feasibility of utilizing such flexible and lightweight antennas for wearable EM head imaging systems.

Flexible Electromagnetic Cap for Head Imaging.

A wideband wearable electromagnetic (EM) head imaging system for brain stroke detection is presented. The proposed system aims at overcoming the challenges of size, rigidity, and complex structures of existing systems. The proposed system is built into a light-weight and compact imaging platform, which integrates a 16-element antenna array into a highly flexible custom-made wearable cap made of a cost-effective and robust room-temperature-vulcanizing (RTV) silicone. The system mitigates the mismatch between the skin and antenna array by introducing a flexible high-permittivity matching layer. The utilized compact antenna demonstrates wideband operational frequency over 0.6-2.5GHz with a low signal distortion, safe values of SAR, and unidirectional radiations. The system is experimentally validated on realistic head phantoms. The polar sensitivity encoding (PSE) image processing algorithm is utilized to generate 2D images of different testing scenarios. The obtained images of a stroke-like target inside the head phantoms demonstrate the merits and feasibility of the system for preclinical trials.

Microwave Head Imaging System for Brain Tumor Detection: A Review

Microwave Head Imaging System for Brain Tumor Detection: A Review

Stroke Classification in Simulated Electromagnetic Imaging Using Graph Approaches

Identifying stroke subtypes from electromagnetic imaging systems is usually based on frequency domain using radar or tomography algorithms which is computationally expensive. This paper presents a novel graph degree mutual information (GDMI) approach to distinguish Intracranial Haemorrhage (ICH) from Ischemic Stroke (IS). A total of 50 ICH and 50 IS signals simulated using a 16-antenna electromagnetic head imaging system are analysed to evaluate GDMI. The data collected from each model consists of 256 reflected and received signal. Subsequently, noise is injected into the collected signals to generate three groups of signals with different signal-to-noise ratios (40 dB, 25 dB and 10 dB SNR), to emulate measurement noise and to test the algorithm's robustness. Each signal is converted into a graph to avoid the variable signal amplitudes. Then, the relationship between each pair of graph degrees is calculated by mutual information and forwarded to a support vector machine to identify stroke type. The results indicate that signals from ICH subjects exhibit a significantly higher GDMI compared to the IS group ( $p ). An accuracy of 88% is achieved in identifying ICH from IS without the need to use time- and resource-expensive brain image reconstruction algorithms even under 25 dB signal-to-noise levels. The execution time for graph feature extraction and classification is less than one minute on a PC. Such a short time is suitable for stroke emergency requirements.