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

Islam, Mohammad Shahidul; Hoque, Abu Sayed Md. Latiful; Islam, Tarikul; Amin, Nowshad; Chowdhury, Muhammad E. H.

doi:10.1109/access.2021.3069712

Cited by 25 publications

(15 citation statements)

References 40 publications

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“…Currently used imaging systems widely used in medical healthcare such as CT scans, MRI, ultrasound, and X-rays are very bulky, costly, and not accessible in rural areas. These bulky systems are not movable to specific locations for diagnosis purposes [ 45 ]. Portable and wearable health-monitoring devices are the only solution to minimize the distance between the patient and the physician.…”

Section: Flexible Electronics and Portable Systemsmentioning

confidence: 99%

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

2023

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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.

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Section: Flexible Electronics and Portable Systemsmentioning

confidence: 99%

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

2023

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“…In this research, an experimental sensor-based microwave brain imaging (SMBI) system has been developed to generate microwave brain images and analyze the system’s performance. The SMBI system framework has been implemented by our research group, as reported in [ 23 , 46 ]. It is worth mentioning here that a wideband antenna sensor with a high gain and unidirectional characteristics is required with a frequency band of 1 to 4 GHz for the SMBI system [ 11 , 12 , 15 , 24 , 25 ].…”

Section: Experimental Setup Of a Sensor-based Microwave Brain Imaging...mentioning

confidence: 99%

“…The collected signals from the PNA were preprocessed by the MATLAB programming language. Thereafter, an image reconstruction algorithm, M-DMAS (modified delay-multiply-and-sum) [ 23 ] was utilized to produce the desired RMW brain images of the head regions. The frequency range of 1.43 GHz to 3.71 was considered for image reconstruction.…”

Section: Experimental Setup Of a Sensor-based Microwave Brain Imaging...mentioning

confidence: 99%

“…The image reconstruction algorithm is then applied to post-process the data to generate reconstructed images. Different image reconstruction algorithms have been used in microwave head imaging modalities to detect brain tumors [ 11 , 12 , 15 , 17 , 18 , 20 , 21 , 22 , 23 , 24 , 25 ]. However, the main limitations of the developed MWHI modalities are that they are (i) noisy, blurry, and the images created by the system are of a low resolution, (ii) the identification of the tumor with its location is complicated for a non-expert physician and radiologist, and (iii) there is difficulty in detecting tumor regions by automatic detection.…”

Section: Introductionmentioning

confidence: 99%

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Brain Tumor Segmentation and Classification from Sensor-Based Portable Microwave Brain Imaging System Using Lightweight Deep Learning Models

et al. 2023

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Automated brain tumor segmentation from reconstructed microwave (RMW) brain images and image classification is essential for the investigation and monitoring of the progression of brain disease. The manual detection, classification, and segmentation of tumors are extremely time-consuming but crucial tasks due to the tumor’s pattern. In this paper, we propose a new lightweight segmentation model called MicrowaveSegNet (MSegNet), which segments the brain tumor, and a new classifier called the BrainImageNet (BINet) model to classify the RMW images. Initially, three hundred (300) RMW brain image samples were obtained from our sensors-based microwave brain imaging (SMBI) system to create an original dataset. Then, image preprocessing and augmentation techniques were applied to make 6000 training images per fold for a 5-fold cross-validation. Later, the MSegNet and BINet were compared to state-of-the-art segmentation and classification models to verify their performance. The MSegNet has achieved an Intersection-over-Union (IoU) and Dice score of 86.92% and 93.10%, respectively, for tumor segmentation. The BINet has achieved an accuracy, precision, recall, F1-score, and specificity of 89.33%, 88.74%, 88.67%, 88.61%, and 94.33%, respectively, for three-class classification using raw RMW images, whereas it achieved 98.33%, 98.35%, 98.33%, 98.33%, and 99.17%, respectively, for segmented RMW images. Therefore, the proposed cascaded model can be used in the SMBI system.

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“…The brain stroke, which occurs due to the presence of blood clots in the head, blocks the transportation of oxygen, thus interrupting the biological activity of the human body and possibly inducing paralysis. Hence, a fast diagnosis is required for this circumstance because it may endanger the life of a patient [1], [2]. Therefore, head imaging is crucial due to its role in performing early diagnosis and detection.…”

Section: Introductionmentioning

confidence: 99%

A Sierpinski Arrowhead Curve Slot Vivaldi Antenna for Microwave Head Imaging System

et al. 2023

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The development of antipodal Vivaldi antenna with a fractal slot is presented in this paper due to its promising application as an element of possible multisensory systems of head stroke imaging. The fractal slot is applied to improve the directivity, bandwidth and gain of the antenna. Hence, this research mainly aims to create and optimise a compact Vivaldi antenna through the fractal slot for the detection of stroke in the human head. The antenna is designed with an overall dimension of 65 × 65 × 1.6 mm 3 using Roger RO4350B as a substrate through the CST Studio Suite 2019 software. The proposed antenna will also be compared with an antipodal Vivaldi antenna without the fractal slot to observe differences in the result. The proposed antenna can produce a wide bandwidth of 1.44 GHz ranging from 2.35 GHz to 3.79 GHz at the maximum gain of 7.35 dBi. Furthermore, the prototype antenna has a directivity characteristic, which produces the unidirectional radiation necessary for microwave imaging procedures. The proposed antenna is also evaluated with the CST studio head model, thus providing satisfactory penetration results through the head tissue. A value of 0.0013 W/kg per 10 g is achieved for the maximum SAR. Thus, the proposed antenna met most of the requirements for the imaging process, which will affect its effectiveness during scanning of head stroke. Fractal slot, Vivaldi antenna, wideband, unidirectional, head imaging. INDEX TERMS

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

Cited by 25 publications

References 40 publications

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

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

Brain Tumor Segmentation and Classification from Sensor-Based Portable Microwave Brain Imaging System Using Lightweight Deep Learning Models

A Sierpinski Arrowhead Curve Slot Vivaldi Antenna for Microwave Head Imaging System

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