Portable Microwave Head Imaging System Using Software-Defined Radio and Switching Network

Stancombe, Anthony E.; Bialkowski, Konstanty; Abbosh, Amin

doi:10.1109/jerm.2019.2901360

Cited by 50 publications

(29 citation statements)

References 34 publications

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“…The team from Queensland University, Australia proposed a low-cost reconfigurable microwave transceiver using Software-Defined Radio (SDR) technology as a substitute for VNA [116]. In 2019, the team developed a novel combination of the SDR with a solid-state switching network and a static antenna array to develop a portable multistatic microwave head imaging system [117]. Casu et al fabricated an FPGA (Field Programmable Gate Array) based circuitry [118] that executed the imaging algorithm 20 times faster than a multicore CPU.…”

Section: Signal Acquisitionmentioning

confidence: 99%

An Overview of Microwave Imaging for Breast Tumor Detection

Benny¹,

Anjit²,

Mythili³

2020

PIER B

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Microwave imaging (MWI) is a non-ionizing, non-invasive and an upcoming affordable medical imaging modality. Over the last few decades, MWI has invited active research towards biomedical imaging, with special focus on breast tumor detection. After long years of intense research and clinical trials, a breast tumour monitoring unit based on MWI is finally entering clinical imaging scenarios. In this manuscript, the vast literature in MWI to date has been consolidated, and an indetail study of the state-of-the-art for breast tumor detection has been presented. The hurdles faced during clinical trials are discussed, and their possible solutions and future directions for a fast transition into clinical imaging have been presented. It is hoped that this paper can serve as a guide for MWI researchers and practitioners, especially those new to the field to comprehend the potential of MWI as a viable imaging tool for breast imaging.

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Section: Signal Acquisitionmentioning

confidence: 99%

An Overview of Microwave Imaging for Breast Tumor Detection

Benny¹,

Anjit²,

Mythili³

2020

PIER B

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“…Anthony E. Stancombe proposes a portable ECPS imaging system that can capture input signals over a range of approximately 106 dB, which is suitable for detecting realistic brain injuries. It is proved that the system is capable of locating targets with dielectric properties similar to brain tumors and bleeds [28]. The ECPS technology developed by Rubinsky et al has received U.S. Food and Drug Administration (FDA) approval and is currently used in a variety of clinical studies such as for the development of classifiers to detect edema and hematoma [29], as a tool to study cerebrovascular reactivity [30], or to detect fluid shifts in the brain during dialysis [31].…”

Section: Discussionmentioning

confidence: 99%

Noninvasive real-time assessment of intracranial pressure after traumatic brain injury based on electromagnetic coupling phase sensing technology

Zhao

et al. 2021

BMC Neurol

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Background To investigate the feasibility of intracranial pressure (ICP) monitoring after traumatic brain injury (TBI) by electromagnetic coupling phase sensing, we established a portable electromagnetic coupling phase shift (ECPS) test system and conducted a comparison with invasive ICP. Methods TBI rabbits’ model were all synchronously monitored for 24 h by ECPS testing and invasive ICP. We investigated the abilities of the ECPS to detect targeted ICP by feature extraction and traditional classification decision algorithms. Results The ECPS showed an overall downward trend with a variation range of − 13.370 ± 2.245° as ICP rose from 11.450 ± 0.510 mmHg to 38.750 ± 4.064 mmHg, but its change rate gradually declined. It was greater than 1.5°/h during the first 6 h, then decreased to 0.5°/h and finally reached the minimum of 0.14°/h. Nonlinear regression analysis results illustrated that both the ECPS and its change rate decrease with increasing ICP post-TBI. When used as a recognition feature, the ability (area under the receiver operating characteristic curve, AUCs) of the ECPS to detect ICP ≥ 20 mmHg was 0.88 ± 0.01 based on the optimized adaptive boosting model, reaching the advanced level of current noninvasive ICP assessment methods. Conclusions The ECPS has the potential to be used for noninvasive continuous monitoring of elevated ICP post-TBI.

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“…Classification performance on this 34-subject data (without back-scatter) is also given and compared to the 15-subject data. The goal of Dataset C is to test the classification accuracy for the case of incomplete measurement of the S-parameter matrix which can be used to simplify future versions of the experimental setup [18].…”

Section: Dataset C: Human Participants With Incomplete S-parametermentioning

confidence: 99%

Machine Learning Classification of S-Band Microwave Scattering Measurements From the Forearm as a Novel Biometric Technique

Nabulsi

Al-Shaikhli

Kettlewell

et al. 2020

IEEE Open J. Antennas Propag.

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Biometrics use an individual's biological traits for personal identification. Various sensors have been used to obtain these measurements. Microwave biometric scans have recently gained traction as a non-contact technique due to their robustness to environmental lighting and unobtrusiveness. To evaluate microwave signature of human forearm as a biometric modality, an 8-antenna (Wi-Fi) data collection setup was developed and initially tested with foil-wrapped tubes of different geometric cross sections. The system was later evaluated by collecting microwave samples from human volunteers' forearms and classifying the data, from different antenna subsets, using Support Vector Machines and Naive Bayesian classifiers. Our results show that human identification via microwave signals is possible even with a subset of the above mentioned 8-antenna configuration.

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Portable Microwave Head Imaging System Using Software-Defined Radio and Switching Network

Cited by 50 publications

References 34 publications

An Overview of Microwave Imaging for Breast Tumor Detection

An Overview of Microwave Imaging for Breast Tumor Detection

Noninvasive real-time assessment of intracranial pressure after traumatic brain injury based on electromagnetic coupling phase sensing technology

Machine Learning Classification of S-Band Microwave Scattering Measurements From the Forearm as a Novel Biometric Technique

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