Microwave technology for detecting traumatic intracranial bleedings: tests on phantom of subdural hematoma and numerical simulations

An experimental system for detection and localization of hemorrhage using ultra-wideband microwaves with deep learning

Stroke is a leading cause of mortality and disability. Emergent diagnosis and intervention are critical, and predicated upon initial brain imaging; however, existing clinical imaging modalities are generally costly, immobile, and demand highly specialized operation and interpretation. Low-energy microwaves have been explored as a low-cost, small form factor, fast, and safe probe for tissue dielectric properties measurements, with both imaging and diagnostic potential. Nevertheless, challenges inherent to microwave reconstruction have impeded progress, hence conduction of microwave imaging remains an elusive scientific aim. Herein, we introduce a dedicated experimental framework comprising a robotic navigation system to translate blood-mimicking phantoms within a human head model. An 8-element ultra-wideband array of modified antipodal Vivaldi antennas was developed and driven by a two-port vector network analyzer spanning 0.6-9.0 GHz at an operating power of 1 mW. Complex scattering parameters were measured, and dielectric signatures of hemorrhage were learned using a dedicated deep neural network for prediction of hemorrhage classes and localization. An overall sensitivity and specificity for detection >0.99 was observed, with Rayleigh mean localization error of 1.65 mm. The study establishes the feasibility of a robust experimental model and deep learning solution for ultra-wideband microwave stroke detection.

Pre-Hospital Stroke Care beyond the MSU

PURPOSE OF REVIEW

Mobile stroke units (MSU) have established a new, evidence-based treatment in prehospital stroke care, endorsed by current international guidelines and can facilitate pre-hospital research efforts. In addition, other novel pre-hospital modalities beyond the MSU are emerging. In this review, we will summarize existing evidence and outline future trajectories of prehospital stroke care & research on and off MSUs.

RECENT FINDINGS

The proof of MSUs' positive effect on patient outcomes is leading to their increased adoption in emergency medical services of many countries. Nevertheless, prehospital stroke care worldwide largely consists of regular ambulances. Advancements in portable technology for detecting neurocardiovascular diseases, telemedicine, AI and large-scale ultra-early biobanking have the potential to transform prehospital stroke care also beyond the MSU concept. The increasing implementation of telemedicine in emergency medical services is demonstrating beneficial effects in the pre-hospital setting. In synergy with telemedicine the exponential growth of AI-technology is already changing and will likely further transform pre-hospital stroke care in the future. Other promising areas include the development and validation of miniaturized portable devices for the pre-hospital detection of acute stroke. MSUs are enabling large-scale screening for ultra-early blood-based biomarkers, facilitating the differentiation between ischemia, hemorrhage, and stroke mimics. The development of suitable point-of-care tests for such biomarkers holds the potential to advance pre-hospital stroke care outside the MSU-concept. A multimodal approach of AI-supported telemedicine, portable devices and blood-based biomarkers appears to be an increasingly realistic scenario for improving prehospital stroke care in regular ambulances in the future.

Cerebral Blood Flow Monitoring with a Portable Radio Frequency Sensing System

Cerebral blood flow (CBF) represents the rate at which blood circulates through the blood vessels in the brain. CBF is a crucial physiological parameter and is a precursor to diagnosing neurodegenerative diseases. CBF irregularities can contribute to an inadequate blood supply to the brain, which affects cerebral metabolism. This leads to a progressive loss of neurons and may result in cognitive impairment, vascular dysfunction, stroke or dementia. Regular monitoring of CBF is critical for proactive assessment of underlying anomalies in the neurovascular system. An innovative Radio Frequency (RF) based sensing system is proposed, which can effectively detect blood flow variations through backscattered RF signal strength. The portable RF sensing system includes a novel miniaturized U-shaped antenna sensor, which is designed to integrate with glasses for live monitoring. The sensor design is validated through microwave computational software and the fabricated sensing system is experimentally validated using artificial blood flow variation within a fabricated brain model. Results are measured on both static and variable flow, and the system can detect blood flow variations (10-90 mL/min) within the fabricated arterial network. The CBF variability is examined through standard deviation in reflected power (max: 7.12 dB, min: 3.10 dB) and analysis of variance test (F-value: 5.76, p-value: 0.038). The safety of the portable sensing system is confirmed through Specific Absorption Rate analysis (SAR < 0.94 W/kg at 100 mW) and thermal transient analysis (increase in tissue temperature < 0.14 °C). The promising results indicate the effectiveness of low-cost, portable systems in non-invasive CBF monitoring.

Microwave Imaging System Based on Signal Analysis in a Planar Environment for Detection of Abdominal Aortic Aneurysms

A proof-of-concept of a microwave imaging system for the fast detection of abdominal aortic aneurysms is shown. This experimental technology seeks to overcome the factors hampering the fast screening for these aneurysms with the usual equipment, such as high cost, long-time operation or hazardous exposure to chemical substances. The hardware system is composed of 16 twin antennas mastered by a microcontroller through a switching network, which connects the antennas to the measurement instrument for sequential measurement. The software system is run by a computer, mastering the whole system, automatizing the measurement process and running the signal processing and medical image generation algorithms. Two image generation algorithms are tested: Delay-and-Sum (DAS) and Improved Delay-and-Sum (IDAS). Own-modified versions of these algorithms adapted to the requirements of our system are proposed. The system is carefully calibrated and fine-tuned with known objects placed at known distances. An experimental proof-of-concept is shown with a human torso phantom, including an aorta phantom and an aneurysm phantom placed in different positions. The results show good imaging capabilities with the potential for detecting and locating possible abdominal aortic aneurysms and reporting acceptable errors.

Crescent Antennas as Sensors: Case of Sensing Brain Pathology

Microstrip crescent antennas offer compactness, conformability, low profile, high sensitivity, multi-band operability, cost-effectiveness and ease of fabrication in contrast to bulky, rigid horn, helical and Vivaldi antennas. This work presents crescent sensors for monitoring brain pathology associated with stroke and atrophy. Single- and multi-element crescent sensors are designed and validated by software simulations. The fabricated sensors are integrated with glasses and experimentally evaluated using a realistic brain phantom. The performance of the sensors is compared in terms of peak gain, directivity, radiation performance, flexibility and detection capability. The crescent sensors can detect the pathologies through the monitoring of backscattered electromagnetic signals that are triggered by dielectric variations in the affected tissues. The proposed sensors can effectively detect stroke and brain atrophy targets with a volume of 25 mm3 and 56 mm3, respectively. The safety of the sensors is examined through the evaluation of Specific Absorption Rate (peak SAR < 1.25 W/Kg, 100 mW), temperature increase within brain tissues (max: 0.155 °C, min: 0.115 °C) and electric field analysis. The results suggest that the crescent sensors can provide a flexible, portable and non-invasive solution to monitor degenerative brain pathology.

An experimental system for detection and localization of hemorrhage using ultra-wideband microwaves with deep learning

Stroke is a leading cause of mortality and disability. Emergent diagnosis and intervention are critical, and predicated upon initial brain imaging; however, existing clinical imaging modalities are generally costly, immobile, and demand highly specialized operation and interpretation. Low-energy microwaves have been explored as low-cost, small form factor, fast, and safe probes of tissue dielectric properties, with both imaging and diagnostic potential. Nevertheless, challenges inherent to microwave reconstruction have impeded progress, hence microwave imaging (MWI) remains an elusive scientific aim. Herein, we introduce a dedicated experimental framework comprising a robotic navigation system to translate blood-mimicking phantoms within an anatomically realistic human head model. An 8-element ultra-wideband (UWB) array of modified antipodal Vivaldi antennas was developed and driven by a two-port vector network analyzer spanning 0.6-9.0 GHz at an operating power of 1 mw. Complex scattering parameters were measured, and dielectric signatures of hemorrhage were learned using a dedicated deep neural network for prediction of hemorrhage classes and localization. An overall sensitivity and specificity for detection >0.99 was observed, with Rayliegh mean localization error of 1.65 mm. The study establishes the feasibility of a robust experimental model and deep learning solution for UWB microwave stroke detection.

Modern Prehospital Screening Technology for Emergent Neurovascular Disorders

Stroke is a serious neurological disease and a significant contributor to disability worldwide. Traditional in-hospital imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) remain the standard modalities for diagnosing stroke. The development of prehospital stroke detection devices may facilitate earlier diagnosis, initiation of stroke care, and ultimately better patient outcomes. In this review, the authors summarize the features of eight stroke detection devices using noninvasive brain scanning technology. The review summarizes the features of stroke detection devices including portable CT, MRI, transcranial Doppler ultrasound , microwave tomographic imaging, electroencephalography, near-infrared spectroscopy, volumetric impedance phaseshift spectroscopy, and cranial accelerometry. The technologies utilized, the indications for application, the environments indicated for application, the physical features of the eight stroke detection devices, and current commercial products are discussed. As technology advances, multiple portable stroke detection instruments exhibit the promising potential to expedite the diagnosis of stroke and enhance the time taken for treatment, ultimately aiding in prehospital stroke triage.

Design and Evaluation of Wearable Multimodal RF Sensing System for Vascular Dementia Detection

Vascular dementia is the second most common form of dementia and a leading cause of death. Brain stroke and brain atrophy are the major degenerative pathologies associated with vascular dementia. Timely detection of these progressive pathologies is critical to avoid brain damage. Brain imaging is an important diagnostic tool and determines future treatment options available to the patient. Traditional medical technologies are expensive, require extensive supervision and are not easily accessible. This article presents a novel concept of low- complexity wearable sensing system for the detection of brain stroke and brain atrophy using RF sensors. This multimodal RF sensing system provides a first-of-its-kind RF sensing solution for the detection of cerebral blood density variations and blood clots at an initial stage of neurodegeneration. A customized microwave imaging algorithm is presented for the reconstruction of images in affected areas of the brain. Designs are validated using software simulations and hardware modeling. Fabricated sensors are experimentally validated and can effectively detect blood density variation (1050 ± 50 Kg/m3), artificial stroke targets with a volume of 27 mm3 and density of 1025-1050 Kg/m3, and brain atrophy with a cavity of 58 mm3 within a realistic brain phantom. The safety of the proposed wearable RF sensing system is studied through the evaluation of the Specific Absorption Rate (SAR < 1.4 W/Kg, 100 mW) and thermal conductivity of the brain (<0.152 °C). The results indicate that the device is viable as an efficient, portable, and low-cost substitute for vascular dementia detection.

Microwave sensing dataset for noninvasive monitoring of ventricle enlargement due to Alzheimer's disease

This paper presents a dataset generated from a comprehensive study on the potential of microwave imaging for early detection or monitoring of different stages of Alzheimer's disease. The study includes collecting and analyzing frequency-domain data using a radar-based head imaging system. The data was obtained from lamb brain phantoms designed to mimic lateral ventricle enlargement, a common symptom of Alzheimer's disease. The article provides detailed descriptions of the data collection method, experimental setup, and different phantoms used. Additionally, the article highlights the importance and potential of the dataset to be used for evaluating and validating new signal processing and imaging techniques. The dataset includes magnitude and phase information for both reflected and transmitted signals making it useful to evaluate radar-based signal processing and imaging techniques. The dataset is open-source and available to the scientific community, providing a valuable resource for researchers to advance their understanding of the potential use of microwave imaging techniques for detecting or monitoring Alzheimer's disease.

On the Role of Training Data for SVM-Based Microwave Brain Stroke Detection and Classification

The aim of this work was to test microwave brain stroke detection and classification using support vector machines (SVMs). We tested how the nature and variability of training data and system parameters impact the achieved classification accuracy. Using experimentally verified numerical models, a large database of synthetic training and test data was created. The models consist of an antenna array surrounding reconfigurable geometrically and dielectrically realistic human head phantoms with virtually inserted strokes of arbitrary size, and different dielectric parameters in different positions. The generated synthetic data sets were used to test four different hypotheses, regarding the appropriate parameters of the training dataset, the appropriate frequency range and the number of frequency points, as well as the level of subject variability to reach the highest SVM classification accuracy. The results indicate that the SVM algorithm is able to detect the presence of the stroke and classify it (i.e., ischemic or hemorrhagic) even when trained with single-frequency data. Moreover, it is shown that data of subjects with smaller strokes appear to be the most suitable for training accurate SVM predictors with high generalization capabilities. Finally, the datasets created for this study are made available to the community for testing and developing their own algorithms.

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.

Monitoring of intracerebral hemorrhage with a linear microwave imaging algorithm

Intracerebral hemorrhage is a life-threatening condition where conventional imaging modalities such as CT and MRI are indispensable in diagnosing. Nevertheless, monitoring the evolution of intracerebral hemorrhage still poses a technological challenge. We consider continuous monitoring of intracerebral hemorrhage in this context and present a differential microwave imaging scheme based on a linearized inverse scattering. Our aim is to reconstruct non-anatomical maps that reveal the volumetric evolution of hemorrhage by using the differences between consecutive electric field measurements. This approach can potentially allow the monitoring of intracerebral hemorrhage in a real-time and cost-effective manner. Here, we devise an indicator function, which reveals the position, volumetric growth, and shrinkage of hemorrhage. Later, the method is numerically tested via a 3D anthropomorphic dielectric head model. Through several simulations performed for different locations of intracerebral hemorrhage, the indicator function-based technique is demonstrated to be capable of detecting the changes accurately. Finally, the robustness under noisy conditions is analyzed to assess the feasibility of the method. This analysis suggests that the method can be used to monitor the evolution of intracerebral hemorrhage in real-world scenarios.

Microwave-induced thermoacoustic imaging for the early detection of canine intracerebral hemorrhage

Purpose: This study aimed to investigate the feasibility and validation of microwave-induced thermoacoustic imaging (TAI) for the early detection of canine intracerebral hemorrhage. Methods: A TAI system was used to record the thermoacoustic signal (TAS) of canine intracerebral hemorrhage in the study. First, the difference in TAS between deionized water, fresh ex vivo porcine blood and brain tissue was explored. Second, the canine hemorrhagic stroke model was established, and canine brain ultrasound examination and TAI examination were performed before modeling and at 0.5 h, 1 h, 2 h, 3 h, 4 h, 4.5 h, 5 h and 6 h after modeling. Finally, pathology and ultrasound were used as the reference diagnoses to verify the accuracy of the thermoacoustic imaging data. Results: The results showed that significant differences were observed in TASs among deionized water, fresh ex vivo porcine blood and brain tissue. The intensity of the thermoacoustic signal of blood was significantly higher than that of ex vivo porcine brain tissue and deionized water. The intracerebral hemorrhage model of five beagles was successfully established. Hematomas presented hyperintensity in TAI. Considering ultrasound and pathology as reference diagnoses, TAI can be used to visualize canine intracerebral hemorrhage at 0.5 h, 1 h, 2 h, 3 h, 4 h, 4.5 h, 5 h and 6 h after modeling. Conclusion: This is the first experimental study to explore the use of TAI in the detection of intracerebral hemorrhage in large live animals (canine). The results indicated that TAI could detect canine intracerebral hemorrhage in the early stage and has the potential to be a rapid and noninvasive method for the detection of intracerebral hemorrhage in humans.

A Proposed Brain-, Spine-, and Mental- Health Screening Methodology (NEUROSCREEN) for Healthcare Systems: Position of the Society for Brain Mapping and Therapeutics

The COVID-19 pandemic has accelerated neurological, mental health disorders, and neurocognitive issues. However, there is a lack of inexpensive and efficient brain evaluation and screening systems. As a result, a considerable fraction of patients with neurocognitive or psychobehavioral predicaments either do not get timely diagnosed or fail to receive personalized treatment plans. This is especially true in the elderly populations, wherein only 16% of seniors say they receive regular cognitive evaluations. Therefore, there is a great need for development of an optimized clinical brain screening workflow methodology like what is already in existence for prostate and breast exams. Such a methodology should be designed to facilitate objective early detection and cost-effective treatment of such disorders. In this paper we have reviewed the existing clinical protocols, recent technological advances and suggested reliable clinical workflows for brain screening. Such protocols range from questionnaires and smartphone apps to multi-modality brain mapping and advanced imaging where applicable. To that end, the Society for Brain Mapping and Therapeutics (SBMT) proposes the Brain, Spine and Mental Health Screening (NEUROSCREEN) as a multi-faceted approach. Beside other assessment tools, NEUROSCREEN employs smartphone guided cognitive assessments and quantitative electroencephalography (qEEG) as well as potential genetic testing for cognitive decline risk as inexpensive and effective screening tools to facilitate objective diagnosis, monitor disease progression, and guide personalized treatment interventions. Operationalizing NEUROSCREEN is expected to result in reduced healthcare costs and improving quality of life at national and later, global scales.

A wearable microwave instrument can detect and monitor traumatic abdominal injuries in a porcine model

Abdominal injury is a frequent cause of death for trauma patients, and early recognition is essential to limit fatalities. There is a need for a wearable sensor system for prehospital settings that can detect and monitor bleeding in the abdomen (hemoperitoneum). This study evaluates the potential for microwave technology to fill that gap. A simple prototype of a wearable microwave sensor was constructed using eight antennas. A realistic porcine model of hemoperitoneum was developed using anesthetized pigs. Ten animals were measured at healthy state and at two sizes of bleeding. Statistical tests and a machine learning method were used to evaluate blood detection sensitivity. All subjects presented similar changes due to accumulation of blood, which dampened the microwave signal (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 0.05$$\end{document}p<0.05). The machine learning analysis yielded an area under the receiver operating characteristic (ROC) curve (AUC) of 0.93, showing 100% sensitivity at 90% specificity. Large inter-individual variability of the healthy state signal complicated differentiation of bleedings from healthy state. A wearable microwave instrument has potential for accurate detection and monitoring of hemoperitoneum, with automated analysis making the instrument easy-to-use. Future hardware development is necessary to suppress measurement system variability and enable detection of smaller bleedings.

Experimental Validation of a Microwave System for Brain Stroke 3-D Imaging

This paper experimentally validates the capability of a microwave prototype device to localize hemorrhages and ischemias within the brain as well as proposes an innovative calibration technique based on the measured data. In the reported experiments, a 3-D human-like head phantom is considered, where the brain is represented either with a homogeneous liquid mimicking brain dielectric properties or with ex vivo calf brains. The microwave imaging (MWI) system works at 1 GHz, and it is realized with a low-complexity architecture formed by an array of twenty-four printed monopole antennas. Each antenna is embedded into the “brick” of a semi-flexible dielectric matching medium, and it is positioned conformal to the head upper part. The imaging algorithm exploits a differential approach and provides 3-D images of the brain region. It employs the singular value decomposition of the discretized scattering operator obtained via accurate numerical models. The MWI system analysis shows promising reconstruction results and extends the device validation.

Developing Artefact Removal Algorithms to Process Data from a Microwave Imaging Device for Haemorrhagic Stroke Detection

In this paper, we present an investigation of different artefact removal methods for ultra-wideband Microwave Imaging (MWI) to evaluate and quantify current methods in a real environment through measurements using an MWI device. The MWI device measures the scattered signals in a multi-bistatic fashion and employs an imaging procedure based on Huygens principle. A simple two-layered phantom mimicking human head tissue is realised, applying a cylindrically shaped inclusion to emulate brain haemorrhage. Detection has been successfully achieved using the superimposition of five transmitter triplet positions, after applying different artefact removal methods, with the inclusion positioned at 0°, 90°, 180°, and 270°. The different artifact removal methods have been proposed for comparison to improve the stroke detection process. To provide a valid comparison between these methods, image quantification metrics are presented. An "ideal/reference" image is used to compare the artefact removal methods. Moreover, the quantification of artefact removal procedures through measurements using MWI device is performed.

A Prototype Microwave System for 3D Brain Stroke Imaging

This work focuses on brain stroke imaging via microwave technology. In particular, the open issue of monitoring patients after stroke onset is addressed here in order to provide clinicians with a tool to control the effectiveness of administered therapies during the follow-up period. In this paper, a novel prototype is presented and characterized. The device is based on a low-complexity architecture which makes use of a minimum number of properly positioned and designed antennas placed on a helmet. It exploits a differential imaging approach and provides 3D images of the stroke. Preliminary experiments involving a 3D phantom filled with brain tissue-mimicking liquid confirm the potential of the technology in imaging a spherical target mimicking a stroke of a radius equal to 1.25 cm.

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.

Investigation of an Ultra Wideband Noise Sensor for Health Monitoring

Quick on-scene assessment and early intervention is the key to reduce the mortality of stroke and trauma patients, and it is highly desirable to develop ambulance-based diagnostic and monitoring devices in order to provide additional support to the medical personnel. We developed a compact and low cost ultra wideband noise sensor for medical diagnostics and vital sign monitoring in pre-hospital settings. In this work, we demonstrated the functionality of the sensor for respiration and heartbeat monitoring. In the test, metronome was used to manipulate the breathing pattern and the heartbeat rate reference was obtained with a commercial electrocardiogram (ECG) device. With seventeen tests performed for respiration rate detection, sixteen of them were successfully detected. The results also show that it is possible to detect the heartbeat rate accurately with the developed sensor.

Experimental study on differential diagnosis of cerebral hemorrhagic and ischemic stroke based on microwave measurement

BACKGROUND

Hemorrhagic stroke and ischemic stroke have similar symptoms at the onset of the disease, but their clinical treatment is completely different. The early, effective identification of stroke types can effectively improve the cure rate.

OBJECTIVE

In this study, an early, noncontact identification of the stroke type, i.e., hemorrhagic or ischemic, based on a microwave measurement technique was investigated.

METHODS

This study was based on animal models of cerebral hemorrhage and cerebral ischemia and the design of a microwave scattering parameter measurement system.

RESULTS

The accuracy of the cerebral hemorrhage model with a blood loss interval of 2 ml reached 93.75%. While the accuracy of the cerebral ischemia model with an ischemic interval of 42 minutes reached 91.7%.

CONCLUSION

The experimental results show that the system for identifying cerebral stroke based on microwaves can distinguish between cerebral hemorrhage and cerebral ischemia models and effectively distinguish between different degrees of cerebral hemorrhage or different durations of cerebral ischemia. This experimental system is inexpensive, portable, noninvasive, simple, and rapid and thus has good potential as a method for identifying the stroke type prior to hospitalization.

Feasibility Study of Enhancing Microwave Brain Imaging Using Metamaterials

We present an approach to enhance microwave brain imaging with an innovative metamaterial (MM) planar design based on a cross-shaped split-ring resonator (SRR-CS). The proposed metasurface is incorporated in different setups, and its interaction with EM waves is studied both experimentally and by using CST Microwave Studio® and is compared to a "no MM" case scenario. We show that the MM can enhance the penetration of the transmitted signals into the human head when placed in contact with skin tissue, acting as an impedance-matching layer. In addition, we show that the MM can improve the transceivers' ability to detect useful "weak" signals when incorporated in a headband scanner for brain imaging by increasing the signal difference from a blood-like dielectric target introduced into the brain volume. Our results suggest that the proposed MM film can be a powerful hardware advance towards the development of scanners for brain haemorrhage detection and monitoring.

Integrated Flexible Hybrid Silicone-Textile Dual-Resonant Sensors and Switching Circuit for Wearable Neurodegeneration Monitoring Systems

This paper describes the design, development, and testing of flexible hybrid silicone-textile sensors and a flexible switching circuit that were integrated into a wearable system for monitoring neurodegenerative diseases. A total of 6 planar monopole antenna sensors were fabricated that propagates at two separate resonant frequencies: 800 MHz and 2.1 GHz respectively. In addition, 2 switching circuits, each having 3 switches and 4 SMA breakout boards, were assembled and placed on the wearable neurodegeneration monitoring system. Each switching circuit connects 3 sensors to a single port on a vector network analyzer (VNA) that is used to generate and receive microwave signals. Experiments were performed using the wearable device with the developed sensors and switching circuit on phantoms mimicking two common physiological changes in the brain caused by neurodegenerative diseases: 1) brain atrophy and 2) lateral ventricle enlargement. The dual nature of the sensors' resonance allows it to detect both brain atrophy and lateral ventricle enlargement separately at different operating frequency. This provides the advantage of minimizing the number of sensor elements needed to monitor neurodegenerative disease. The use of a switching circuit also allows for quick and convenient measurements by choosing which sensors are active for ports 1 and 2 on the VNA respectively. In addition to being low-cost, the flexibility of the materials used in fabrication allows the sensors and switching circuit to be conformal to the patient's head. Results from the experiments indicates that the sensors and switching circuit were working successfully when integrated into the wearable device.

3D Simulations of Intracerebral Hemorrhage Detection Using Broadband Microwave Technology

Early, preferably prehospital, detection of intracranial bleeding after trauma or stroke would dramatically improve the acute care of these large patient groups. In this paper, we use simulated microwave transmission data to investigate the performance of a machine learning classification algorithm based on subspace distances for the detection of intracranial bleeding. A computational model, consisting of realistic human head models of patients with bleeding, as well as healthy subjects, was inserted in an antenna array model. The Finite-Difference Time-Domain (FDTD) method was then used to generate simulated transmission coefficients between all possible combinations of antenna pairs. These transmission data were used both to train and evaluate the performance of the classification algorithm and to investigate its ability to distinguish patients with versus without intracranial bleeding. We studied how classification results were affected by the number of healthy subjects and patients used to train the algorithm, and in particular, we were interested in investigating how many samples were needed in the training dataset to obtain classification results better than chance. Our results indicated that at least 200 subjects, i.e., 100 each of the healthy subjects and bleeding patients, were needed to obtain classification results consistently better than chance (p < 0.05 using Student's t-test). The results also showed that classification results improved with the number of subjects in the training data. With a sample size that approached 1000 subjects, classifications results characterized as area under the receiver operating curve (AUC) approached 1.0, indicating very high sensitivity and specificity.

A retrospective review of patients with significant traumatic brain injury transported by emergency medical services within the south east of England

INTRODUCTION

Traumatic brain injury (TBI) will be a leading cause of death and disability within the Western world by 2020. Currently, 80% of all TBI patients in England are transported to hospital by an ambulance service. The aim of this retrospective study is to compare TBI patients transported to a major trauma centre (MTC) against those transported to a trauma unit (TU).

METHOD

All patients with a primary injury of TBI who were transported to hospital by South East Coast Ambulance Service NHS Foundation Trust (SECAmb) from 1 January 2016 to 31 December 2016 and entered into the Trauma Audit & Research Network (TARN) registry were reviewed. Patients were stratified by hospital designation (MTC or TU). Severity of TBI was categorised using the patients' pre-hospital Glasgow Coma Scale (GCS) and Abbreviated Injury Score (AIS) Head. The outcomes of interest were 30-day mortality and Glasgow Outcome Score (GOS) at discharge.

RESULTS

Between 1 January and 31 December 2016, 549 TBI patients were identified in the TARN database as being transported by SECAmb to either an MTC or a TU. The majority of patients were transported to a TU (77.96%), and the median age of the TU cohort was older than the MTC group (TU 82.15 IQR 16.73 vs. MTC 62.1 IQR 42.6). The median Injury Severity Score (ISS) was greater in the MTC cohort (22 IQR 10 vs. 17 IQR 9), where falls from height and road traffic collisions (RTCs) contributed to 50.51% of all injuries. Within the TU cohort, falls from less than 2 metres (standing height) were the main mechanism of injury (MOI) (77.62%). The median length of hospital stay (LOS) was longer in the MTC cohort compared to the TU cohort (10 IQR 13.25 vs. 8 IQR 14).

CONCLUSION

The high proportion of mild TBI and absence of reliable triage guidelines make it difficult for ambulance clinicians to identify patients who will benefit from transport to an MTC. Future research should focus on how TBI triage influences outcomes and how ambulance services can better identify patients with a TBI and who would benefit from specialist care.

Identification of the visco-hyperelastic properties of brain white matter based on the combination of inverse method and experiment

To fully understand the brain injury mechanism and develop effective protective approaches, an accurate constitutive model of brain tissue is firstly required. Generally, the brain tissue is regarded as a kind of viscoelastic material and is simply used in the simulation of brain injury. In fact, the brain tissue has the behavior of the visco-hyperelastic property. Therefore, this paper presents an effective computational inverse method to determine the material parameters of visco-hyperelastic constitutive model of brain white matter through compression experiments. First, with the help of 3D hand scanner, 3D geometries of brain white matter specimens are obtained to make it possible to establish the accurate simulation models of the specific specimens. Then, the global sensitivity analysis is adopted to evaluate the importance of the material parameters and further determine the parameters which may be identified. Subsequently, based on the genetic algorithm, the optimal material parameters of brain white matter can be identified by minimizing the match error between the experimental and simulated responses. Finally, by comparing the experiment and simulation results on the other specific specimen, and the simulation results with the material parameters from the references, respectively, the accuracy and reliability of the constitutive model parameters of brain white matter are demonstrated. Graphical abstract The main flowchart of the computational inverse technique for determining the material parameters of specimen-specific on brain white matter. Generalization: Combining the computational inverse method and unconfined uniaxial compression experiment of the specific specimen, an effective identification method is presented to accurately determine the hyperelastic and viscoelastic parameters of brain white matter in this paper.

Adaptive Monostatic System for Measuring Microwave Reflections from the Breast

A second-generation monostatic radar system to measure microwave reflections from the human breast is presented and analyzed. The present system can measure the outline of the breast with an accuracy of ±1 mm and precisely place the microwave sensor in an adaptive matter such that microwaves are normally incident on the skin. Microwave reflections are measured between 10 MHz to 12 GHz with sensitivity of 65 to 75 dB below the input power and a total scan time of 30 min for 140 locations. The time domain reflections measured from a volunteer show fidelity above 0.98 for signals in a single scan. Finally, multiple scans of a breast phantoms demonstrate the consistency of the system in terms of recorded reflection, outline measurement, and image reconstruction.

Clinical Evaluation of a Microwave-Based Device for Detection of Traumatic Intracranial Hemorrhage

Traumatic brain injury (TBI) is the leading cause of death and disability among young persons. A key to improve outcome for patients with TBI is to reduce the time from injury to definitive care by achieving high triage accuracy. Microwave technology (MWT) allows for a portable device to be used in the pre-hospital setting for detection of intracranial hematomas at the scene of injury, thereby enhancing early triage and allowing for more adequate early care. MWT has previously been evaluated for medical applications including the ability to differentiate between hemorrhagic and ischemic stroke. The purpose of this study was to test whether MWT in conjunction with a diagnostic mathematical algorithm could be used as a medical screening tool to differentiate patients with traumatic intracranial hematomas, chronic subdural hematomas (cSDH), from a healthy control (HC) group. Twenty patients with cSDH and 20 HC were measured with a MWT device. The accuracy of the diagnostic algorithm was assessed using a leave-one-out analysis. At 100% sensitivity, the specificity was 75%—i.e., all hematomas were detected at the cost of 25% false positives (patients who would be overtriaged). Considering the need for methods to identify patients with intracranial hematomas in the pre-hospital setting, MWT shows promise as a tool to improve triage accuracy. Further studies are under way to evaluate MWT in patients with other intracranial hemorrhages.