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Röhrs KJ, Audebert H. Pre-Hospital Stroke Care beyond the MSU. Curr Neurol Neurosci Rep 2024; 24:315-322. [PMID: 38907812 PMCID: PMC11258185 DOI: 10.1007/s11910-024-01351-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/11/2024] [Indexed: 06/24/2024]
Abstract
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.
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Affiliation(s)
- Kian J Röhrs
- Department of Neurology, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Heinrich Audebert
- Department of Neurology, Campus Benjamin Franklin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany.
- Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
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2
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Martínez-Lozano A, Gutierrez R, Juan CG, Blanco-Angulo C, García-Martínez H, Torregrosa G, Sabater-Navarro JM, Ávila-Navarro E. Microwave Imaging System Based on Signal Analysis in a Planar Environment for Detection of Abdominal Aortic Aneurysms. BIOSENSORS 2024; 14:149. [PMID: 38534256 DOI: 10.3390/bios14030149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 03/06/2024] [Accepted: 03/14/2024] [Indexed: 03/28/2024]
Abstract
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.
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Affiliation(s)
- Andrea Martínez-Lozano
- Microwave Laboratory Research Group, Engineering Research Institute of Elche, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - Roberto Gutierrez
- Microwave Laboratory Research Group, Engineering Research Institute of Elche, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - Carlos G Juan
- Neuroengineering Biomedical Research Group, Institute of Bioengineering, Miguel Hernández University of Elche, 03202 Elche, Spain
- Electronic Design and Signal Processing Techniques Research Group, Department of Electronics, Computer Technology and Projects, Technical University of Cartagena, 30202 Cartagena, Spain
| | - Carolina Blanco-Angulo
- Microwave Laboratory Research Group, Engineering Research Institute of Elche, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - Héctor García-Martínez
- Microwave Laboratory Research Group, Engineering Research Institute of Elche, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - Germán Torregrosa
- Microwave Laboratory Research Group, Engineering Research Institute of Elche, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - José María Sabater-Navarro
- Neuroengineering Biomedical Research Group, Institute of Bioengineering, Miguel Hernández University of Elche, 03202 Elche, Spain
| | - Ernesto Ávila-Navarro
- Microwave Laboratory Research Group, Engineering Research Institute of Elche, Miguel Hernández University of Elche, 03202 Elche, Spain
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3
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Anwar U, Arslan T, Lomax P. Crescent Antennas as Sensors: Case of Sensing Brain Pathology. SENSORS (BASEL, SWITZERLAND) 2024; 24:1305. [PMID: 38400463 PMCID: PMC10892644 DOI: 10.3390/s24041305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/15/2024] [Accepted: 02/16/2024] [Indexed: 02/25/2024]
Abstract
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.
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Affiliation(s)
- Usman Anwar
- School of Engineering, The University of Edinburgh, Edinburgh EH9 3FF, UK;
| | - Tughrul Arslan
- School of Engineering, The University of Edinburgh, Edinburgh EH9 3FF, UK;
- Advanced Care Research Centre, The University of Edinburgh, Edinburgh EH16 4UX, UK
| | - Peter Lomax
- School of Engineering, The University of Edinburgh, Edinburgh EH9 3FF, UK;
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4
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Hedayati E, Safari F, Verghese G, Ciancia VR, Sodickson DK, Dehkharghani S, Alon L. An experimental system for detection and localization of hemorrhage using ultra-wideband microwaves with deep learning. ARXIV 2023:arXiv:2310.02215v1. [PMID: 37873017 PMCID: PMC10593075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
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.
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Affiliation(s)
- Eisa Hedayati
- Center for Advanced Imaging Innovation and Research (CAIR), New York University School of Medicine, New York, NY, United States
- Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States
| | - Fatemeh Safari
- Center for Advanced Imaging Innovation and Research (CAIR), New York University School of Medicine, New York, NY, United States
- Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States
| | - George Verghese
- Center for Advanced Imaging Innovation and Research (CAIR), New York University School of Medicine, New York, NY, United States
- Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States
| | | | - Daniel K. Sodickson
- Center for Advanced Imaging Innovation and Research (CAIR), New York University School of Medicine, New York, NY, United States
- Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States
| | - Seena Dehkharghani
- Center for Advanced Imaging Innovation and Research (CAIR), New York University School of Medicine, New York, NY, United States
- Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States
- Department of Neurology, New York University Langone Medical Center
| | - Leeor Alon
- Center for Advanced Imaging Innovation and Research (CAIR), New York University School of Medicine, New York, NY, United States
- Center for Biomedical Imaging, New York University School of Medicine, New York, NY, United States
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5
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Guo X, Dye J. Modern Prehospital Screening Technology for Emergent Neurovascular Disorders. Adv Biol (Weinh) 2023; 7:e2300174. [PMID: 37357150 DOI: 10.1002/adbi.202300174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 05/14/2023] [Indexed: 06/27/2023]
Abstract
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.
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Affiliation(s)
- Xiaofan Guo
- Department of Neurology, Loma Linda University, Loma Linda, CA, 92354, USA
| | - Justin Dye
- Department of Neurosurgery, Loma Linda University, Loma Linda, CA, 92354, USA
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6
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Ullah R, Saied I, Arslan T. Microwave sensing dataset for noninvasive monitoring of ventricle enlargement due to Alzheimer's disease. Data Brief 2023; 47:109006. [PMID: 36909017 PMCID: PMC9999157 DOI: 10.1016/j.dib.2023.109006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/11/2023] [Accepted: 02/16/2023] [Indexed: 02/25/2023] Open
Abstract
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.
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Affiliation(s)
- Rahmat Ullah
- School of Engineering, University of Edinburgh, Scotland
| | - Imran Saied
- School of Engineering, University of Edinburgh, Scotland
| | - Tughrul Arslan
- School of Engineering, University of Edinburgh, Scotland
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7
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Pokorny T, Vrba J, Fiser O, Vrba D, Drizdal T, Novak M, Tosi L, Polo A, Salucci M. On the Role of Training Data for SVM-Based Microwave Brain Stroke Detection and Classification. SENSORS (BASEL, SWITZERLAND) 2023; 23:2031. [PMID: 36850630 PMCID: PMC9962620 DOI: 10.3390/s23042031] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/04/2023] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
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.
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Affiliation(s)
- Tomas Pokorny
- Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 166 00 Prague, Czech Republic
| | - Jan Vrba
- Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 166 00 Prague, Czech Republic
| | - Ondrej Fiser
- Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 166 00 Prague, Czech Republic
| | - David Vrba
- Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 166 00 Prague, Czech Republic
| | - Tomas Drizdal
- Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 166 00 Prague, Czech Republic
| | - Marek Novak
- Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, 166 00 Prague, Czech Republic
| | - Luca Tosi
- ELEDIA Research Center (ELEDIA@UniTN—University of Trento), DICAM—Department of Civil, Environmental, and Mechanical Engineering, Via Mesiano 77, 38123 Trento, Italy
| | - Alessandro Polo
- ELEDIA Research Center (ELEDIA@UniTN—University of Trento), DICAM—Department of Civil, Environmental, and Mechanical Engineering, Via Mesiano 77, 38123 Trento, Italy
| | - Marco Salucci
- ELEDIA Research Center (ELEDIA@UniTN—University of Trento), DICAM—Department of Civil, Environmental, and Mechanical Engineering, Via Mesiano 77, 38123 Trento, Italy
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8
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Martusevich AK, Nazarov VV, Surovegina AV, Novikov AV. Near-Field Microwave Tomography of Biological Tissues: Future Perspectives. Crit Rev Biomed Eng 2023; 50:1-12. [PMID: 36734863 DOI: 10.1615/critrevbiomedeng.2022042194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
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.
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Affiliation(s)
- Andrew K Martusevich
- Privolzhsky Research Medical University, Nizhny Novgorod, Russia; Nizhny Novgorod State Agricultural Academy, Nizhny Novgorod, Russia
| | - Vladimir V Nazarov
- Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia; Institute of Applied Physics, Russian Academy of Sciences, Nizhny Novgorod 603950, Russia
| | - Alexandra V Surovegina
- Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia; Nizhny Novgorod State Agricultural Academy, Nizhny Novgorod 603109, Russia
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9
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Dilman İ, Bilgin E, Akıncı MN, Coşğun S, Doğu S, Çayören M, Akduman İ. Monitoring of intracerebral hemorrhage with a linear microwave imaging algorithm. Med Biol Eng Comput 2023; 61:33-43. [PMID: 36307743 DOI: 10.1007/s11517-022-02694-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/02/2022] [Indexed: 11/07/2022]
Abstract
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.
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Affiliation(s)
- İsmail Dilman
- Dept. of Electronic and Communication Engineering, Istanbul Technical University, Maslak, 34469, Istanbul, Turkey.
| | - Egemen Bilgin
- Dept. of Electrical and Electronics Engineering, MEF University, Maslak, 34396, Istanbul, Turkey
| | - Mehmet Nuri Akıncı
- Dept. of Electronic and Communication Engineering, Istanbul Technical University, Maslak, 34469, Istanbul, Turkey
| | - Sema Coşğun
- Dept. of Electrical and Electronic Engineering, Bolu Abant Izzet Baysal University, Gölköy, 14030, Bolu, Turkey
| | - Semih Doğu
- Dept. of Electronic and Communication Engineering, Istanbul Technical University, Maslak, 34469, Istanbul, Turkey
| | - Mehmet Çayören
- Dept. of Electronic and Communication Engineering, Istanbul Technical University, Maslak, 34469, Istanbul, Turkey
| | - İbrahim Akduman
- Dept. of Electronic and Communication Engineering, Istanbul Technical University, Maslak, 34469, Istanbul, Turkey
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10
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Li J, Wu Z, Peng C, Song L, Luo Y. Microwave-induced thermoacoustic imaging for the early detection of canine intracerebral hemorrhage. Front Physiol 2022; 13:1067948. [PMID: 36467679 PMCID: PMC9709279 DOI: 10.3389/fphys.2022.1067948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/02/2022] [Indexed: 11/07/2023] Open
Abstract
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.
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Affiliation(s)
- Jiawu Li
- Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, China
| | - Zhenru Wu
- Institute of Clinical Pathology, Key Laboratory of Transplant Engineering and Immunology, NHC, West China Hospital, Sichuan University, Chengdu, China
| | - Chihan Peng
- Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, China
| | - Ling Song
- Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, China
| | - Yan Luo
- Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, China
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11
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Nami M, Thatcher R, Kashou N, Lopes D, Lobo M, Bolanos JF, Morris K, Sadri M, Bustos T, Sanchez GE, Mohd-Yusof A, Fiallos J, Dye J, Guo X, Peatfield N, Asiryan M, Mayuku-Dore A, Krakauskaite S, Soler EP, Cramer SC, Besio WG, Berenyi A, Tripathi M, Hagedorn D, Ingemanson M, Gombosev M, Liker M, Salimpour Y, Mortazavi M, Braverman E, Prichep LS, Chopra D, Eliashiv DS, Hariri R, Tiwari A, Green K, Cormier J, Hussain N, Tarhan N, Sipple D, Roy M, Yu JS, Filler A, Chen M, Wheeler C, Ashford JW, Blum K, Zelinsky D, Yamamoto V, Kateb B. A Proposed Brain-, Spine-, and Mental- Health Screening Methodology (NEUROSCREEN) for Healthcare Systems: Position of the Society for Brain Mapping and Therapeutics. J Alzheimers Dis 2022; 86:21-42. [PMID: 35034899 DOI: 10.3233/jad-215240] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
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.
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Affiliation(s)
- Mohammad Nami
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Neuroscience Center, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), City of Knowledge, Panama.,Department of Neuroscience, School of Advanced Medical Sciences and Technologies, and Dana Brain Health Institute, Shiraz University of Medical Sciences, Shiraz, Iran.,Inclusive Brain Health and BrainLabs International, Swiss Alternative Medicine, Geneva, Switzerland
| | - Robert Thatcher
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Applied Neuroscience, Inc., St Petersburg, FL, USA
| | - Nasser Kashou
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Dahabada Lopes
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Maria Lobo
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Joe F Bolanos
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Kevin Morris
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Melody Sadri
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Teshia Bustos
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Gilberto E Sanchez
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Alena Mohd-Yusof
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - John Fiallos
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Justin Dye
- Department of Neurosurgery, Loma Linda University, Loma Linda, CA, USA
| | - Xiaofan Guo
- Department of Neurology, Loma Linda University, CA, USA
| | | | - Milena Asiryan
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Alero Mayuku-Dore
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Solventa Krakauskaite
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Ernesto Palmero Soler
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Steven C Cramer
- Department of Neurology, UCLA, and California Rehabilitation Institute, Los Angeles, CA, USA
| | - Walter G Besio
- Electrical Computer and Biomedical Engineering Department and Interdisciplinary Neuroscience Program, University of Rhode Island, RI, USA
| | - Antal Berenyi
- The Neuroscience Institute, New York University, New York, NY, USA
| | - Manjari Tripathi
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | | | | | | | - Mark Liker
- Department of Neurosurgery, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Yousef Salimpour
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | | | | | | | - Dawn S Eliashiv
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,UCLA David Geffen, School of Medicine, Department of Neurology, Los Angeles, CA, USA
| | - Robert Hariri
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Celularity Corporation, Warren, NJ, USA.,Weill Cornell School of Medicine, Department of Neurosurgery, New York, NY, USA.,Brain Technology and Innovation Park, Los Angeles, CA, USA
| | - Ambooj Tiwari
- Departments of Neurology, Radiology & Neurosurgery - NYU Grossman School of Medicine, New York, NY, USA
| | - Ken Green
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | - Jason Cormier
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Lafayette Surgical Specialty Hospital, Lafayette, LA, USA
| | - Namath Hussain
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Department of Psychiatry, Faculty of Medicine, Uskudar University, Turkey
| | - Nevzat Tarhan
- Department of Psychiatry, Faculty of Medicine, Uskudar University, Turkey
| | - Daniel Sipple
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Midwest Spine and Brain Institute, Roseville, MN, USA
| | - Michael Roy
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Uniformed Services University Health Science (USUHS), Baltimore, MD, USA
| | - John S Yu
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aaron Filler
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Institute for Nerve Medicine, Santa Monica, CA, USA.,Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Mike Chen
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Department of Neurosurgery, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | - Chris Wheeler
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA
| | | | - Kenneth Blum
- Division of Addiction Research, Center for Psychiatry, Medicine, and Primary Care, Western Health Sciences, Pomona, CA, USA
| | | | - Vicky Yamamoto
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,USC Keck School of Medicine, The USC Caruso Department of Otolaryngology-Head and Neck Surgery, Los Angeles, CA, USA.,USC-Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | - Babak Kateb
- Society for Brain Mapping and Therapeutics (SBMT), Los Angeles, CA, USA.,Brain Mapping Foundation (BMF), Los Angeles, CA, USA.,Loma Linda University, Department of Neurosurgery, Loma Linda, CA, USA.,National Center for NanoBioElectronic (NCNBE), Los Angeles, CA, USA.,Brain Technology and Innovation Park, Los Angeles, CA, USA
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12
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A wearable microwave instrument can detect and monitor traumatic abdominal injuries in a porcine model. Sci Rep 2021; 11:23220. [PMID: 34853326 PMCID: PMC8636634 DOI: 10.1038/s41598-021-02008-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 11/08/2021] [Indexed: 12/20/2022] Open
Abstract
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}
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\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.
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13
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Rodriguez-Duarte DO, Tobon Vasquez JA, Scapaticci R, Turvani G, Cavagnaro M, Casu MR, Crocco L, Vipiana F. Experimental Validation of a Microwave System for Brain Stroke 3-D Imaging. Diagnostics (Basel) 2021; 11:diagnostics11071232. [PMID: 34359315 PMCID: PMC8307256 DOI: 10.3390/diagnostics11071232] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 06/30/2021] [Accepted: 07/01/2021] [Indexed: 12/26/2022] Open
Abstract
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.
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Affiliation(s)
- David O. Rodriguez-Duarte
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy; (D.O.R.-D.); (J.A.T.V.); (G.T.); (M.R.C.)
| | - Jorge A. Tobon Vasquez
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy; (D.O.R.-D.); (J.A.T.V.); (G.T.); (M.R.C.)
| | - Rosa Scapaticci
- Institute for the Electromagnetic Sensing of the Environment, National Research Council of Italy, 80124 Naples, Italy; (R.S.); (L.C.)
| | - Giovanna Turvani
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy; (D.O.R.-D.); (J.A.T.V.); (G.T.); (M.R.C.)
| | - Marta Cavagnaro
- Department of Information Engineering, Electronics, and Telecommunications, Sapienza University of Rome, 00184 Roma, Italy;
| | - Mario R. Casu
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy; (D.O.R.-D.); (J.A.T.V.); (G.T.); (M.R.C.)
| | - Lorenzo Crocco
- Institute for the Electromagnetic Sensing of the Environment, National Research Council of Italy, 80124 Naples, Italy; (R.S.); (L.C.)
| | - Francesca Vipiana
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy; (D.O.R.-D.); (J.A.T.V.); (G.T.); (M.R.C.)
- Correspondence:
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14
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Sohani B, Puttock J, Khalesi B, Ghavami N, Ghavami M, Dudley S, Tiberi G. Developing Artefact Removal Algorithms to Process Data from a Microwave Imaging Device for Haemorrhagic Stroke Detection. SENSORS 2020; 20:s20195545. [PMID: 32998256 PMCID: PMC7582349 DOI: 10.3390/s20195545] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 09/19/2020] [Accepted: 09/25/2020] [Indexed: 11/16/2022]
Abstract
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.
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Affiliation(s)
- Behnaz Sohani
- School of Engineering, London South Bank University, London SE1 0AA, UK; (J.P.); (B.K.); (M.G.); (S.D.); (G.T.)
- Correspondence:
| | - James Puttock
- School of Engineering, London South Bank University, London SE1 0AA, UK; (J.P.); (B.K.); (M.G.); (S.D.); (G.T.)
| | - Banafsheh Khalesi
- School of Engineering, London South Bank University, London SE1 0AA, UK; (J.P.); (B.K.); (M.G.); (S.D.); (G.T.)
| | - Navid Ghavami
- UBT-Umbria Bioengineering Technologies, Spin off of University of Perugia, 06081 Assisi, Italy;
| | - Mohammad Ghavami
- School of Engineering, London South Bank University, London SE1 0AA, UK; (J.P.); (B.K.); (M.G.); (S.D.); (G.T.)
| | - Sandra Dudley
- School of Engineering, London South Bank University, London SE1 0AA, UK; (J.P.); (B.K.); (M.G.); (S.D.); (G.T.)
| | - Gianluigi Tiberi
- School of Engineering, London South Bank University, London SE1 0AA, UK; (J.P.); (B.K.); (M.G.); (S.D.); (G.T.)
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15
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Detection of haemorrhagic stroke in simulation and realistic 3-D human head phantom using microwave imaging. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102001] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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16
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A Prototype Microwave System for 3D Brain Stroke Imaging. SENSORS 2020; 20:s20092607. [PMID: 32375220 PMCID: PMC7248903 DOI: 10.3390/s20092607] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 04/23/2020] [Accepted: 04/30/2020] [Indexed: 12/14/2022]
Abstract
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.
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17
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Shao W, McCollough T. Advances in Microwave Near-Field Imaging: Prototypes, Systems, and Applications. IEEE MICROWAVE MAGAZINE 2020; 21:94-119. [PMID: 34168520 PMCID: PMC8221233 DOI: 10.1109/mmm.2020.2971375] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
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.
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Affiliation(s)
- Wenyi Shao
- Johns Hopkins University, Baltimore, Maryland, United States
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18
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Zeng X, Robakowski J, Persson M, Monteith A, Fhager A. Investigation of an Ultra Wideband Noise Sensor for Health Monitoring. SENSORS 2020; 20:s20041034. [PMID: 32075120 PMCID: PMC7071039 DOI: 10.3390/s20041034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 01/27/2020] [Accepted: 02/10/2020] [Indexed: 01/21/2023]
Abstract
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.
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Affiliation(s)
- Xuezhi Zeng
- Department of Electrical Engineering, Chalmers University of Technology, SE412-96 Gothenburg, Sweden
- Correspondence:
| | - Joakim Robakowski
- Department of Electrical Engineering, Chalmers University of Technology, SE412-96 Gothenburg, Sweden
| | - Mikael Persson
- Department of Electrical Engineering, Chalmers University of Technology, SE412-96 Gothenburg, Sweden
| | - Albert Monteith
- Department of Space, Earth and Environment, Chalmers University of Technology, SE412-96 Gothenburg, Sweden
| | - Andreas Fhager
- Department of Electrical Engineering, Chalmers University of Technology, SE412-96 Gothenburg, Sweden
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19
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Wang F, Zhang H, Bao J, Li H, Peng W, Xu J, Yang J, Zhuang W, Ning X, Xu L, Qiao L, Qin M, Chen M. Experimental study on differential diagnosis of cerebral hemorrhagic and ischemic stroke based on microwave measurement. Technol Health Care 2020; 28:289-301. [PMID: 32364161 PMCID: PMC7369055 DOI: 10.3233/thc-209029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
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.
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Affiliation(s)
- Feng Wang
- College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Haisheng Zhang
- College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Junlin Bao
- Hospital 32308, Zhangjiakou, Hebei, China
| | - Huaiqiang Li
- Xinjiang Drug Equipment Inspection Institute, Xinjiang, China
| | | | - Jia Xu
- College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Jun Yang
- College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Wei Zhuang
- College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Xu Ning
- College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Lin Xu
- College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Liang Qiao
- College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Mingxin Qin
- College of Biomedical Engineering, Army Medical University, Chongqing, China
| | - Mingsheng Chen
- College of Biomedical Engineering, Army Medical University, Chongqing, China
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20
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Razzicchia E, Sotiriou I, Cano-Garcia H, Kallos E, Palikaras G, Kosmas P. Feasibility Study of Enhancing Microwave Brain Imaging Using Metamaterials. SENSORS (BASEL, SWITZERLAND) 2019; 19:E5472. [PMID: 31842266 PMCID: PMC6961002 DOI: 10.3390/s19245472] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 12/08/2019] [Accepted: 12/10/2019] [Indexed: 11/16/2022]
Abstract
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.
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Affiliation(s)
- Eleonora Razzicchia
- Faculty of Natural and Mathematical Sciences, King’s College London, Strand, London WC2R 2LS, UK;
| | - Ioannis Sotiriou
- Faculty of Natural and Mathematical Sciences, King’s College London, Strand, London WC2R 2LS, UK;
| | | | | | | | - Panagiotis Kosmas
- Faculty of Natural and Mathematical Sciences, King’s College London, Strand, London WC2R 2LS, UK;
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21
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Saied IM, Chandran S, Arslan T. Integrated Flexible Hybrid Silicone-Textile Dual-Resonant Sensors and Switching Circuit for Wearable Neurodegeneration Monitoring Systems. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:1304-1312. [PMID: 31689207 DOI: 10.1109/tbcas.2019.2951500] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
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.
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22
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Fhager A, Candefjord S, Elam M, Persson M. 3D Simulations of Intracerebral Hemorrhage Detection Using Broadband Microwave Technology. SENSORS (BASEL, SWITZERLAND) 2019; 19:E3482. [PMID: 31395840 PMCID: PMC6719940 DOI: 10.3390/s19163482] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 08/01/2019] [Accepted: 08/06/2019] [Indexed: 01/27/2023]
Abstract
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.
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Affiliation(s)
- Andreas Fhager
- Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden.
- MedTech West, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden.
| | - Stefan Candefjord
- Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
- MedTech West, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Mikael Elam
- MedTech West, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
- Inst of Neuroscience and Physiology, Dept. of Clinical Neurophysiology, Sahlgrenska Academy, Göteborg University and with Neuro-Division, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
| | - Mikael Persson
- Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
- MedTech West, Sahlgrenska University Hospital, 413 45 Gothenburg, Sweden
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23
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Barrett JW. A retrospective review of patients with significant traumatic brain injury transported by emergency medical services within the south east of England. Br Paramed J 2019; 3:1-7. [PMID: 33328810 PMCID: PMC7706740 DOI: 10.29045/14784726.2019.03.3.4.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
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.
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Liu Q, Liu J, Guan F, Han X, Cao L, Shan K. Identification of the visco-hyperelastic properties of brain white matter based on the combination of inverse method and experiment. Med Biol Eng Comput 2019; 57:1109-1120. [PMID: 30635831 DOI: 10.1007/s11517-018-1944-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 12/10/2018] [Indexed: 10/27/2022]
Abstract
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.
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Affiliation(s)
- Qiming Liu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin, 300401, People's Republic of China.,State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, People's Republic of China
| | - Jie Liu
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, People's Republic of China.
| | - Fengjiao Guan
- Science and Technology on Integrated Logistics Support Laboratory, National University of Defense Technology, Changsha, 410073, People's Republic of China
| | - Xu Han
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin, 300401, People's Republic of China. .,State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, People's Republic of China.
| | - Lixiong Cao
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, People's Republic of China
| | - Kezhen Shan
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, People's Republic of China
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Bourqui J, Kuhlmann M, Kurrant DJ, Lavoie BR, Fear EC. Adaptive Monostatic System for Measuring Microwave Reflections from the Breast. SENSORS (BASEL, SWITZERLAND) 2018; 18:E1340. [PMID: 29701677 PMCID: PMC5982192 DOI: 10.3390/s18051340] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 04/18/2018] [Accepted: 04/22/2018] [Indexed: 12/27/2022]
Abstract
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.
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Affiliation(s)
- Jeremie Bourqui
- Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
| | - Martin Kuhlmann
- Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
- College of Engineering and Architecture of Fribourg, 1700 Fribourg, Switzerland.
| | - Douglas J Kurrant
- Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
| | - Benjamin R Lavoie
- Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
| | - Elise C Fear
- Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
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Ljungqvist J, Candefjord S, Persson M, Jönsson L, Skoglund T, Elam M. Clinical Evaluation of a Microwave-Based Device for Detection of Traumatic Intracranial Hemorrhage. J Neurotrauma 2017; 34:2176-2182. [PMID: 28287909 PMCID: PMC5510669 DOI: 10.1089/neu.2016.4869] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
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.
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Affiliation(s)
- Johan Ljungqvist
- 1 Department of Neurosurgery, Sahlgrenska University Hospital , Gothenburg, Sweden .,2 Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, The Sahlgrenska Academy at the University of Gothenburg , Gothenburg, Sweden
| | - Stefan Candefjord
- 3 Department of Signals and Systems, Chalmers University of Technology , Gothenburg, Sweden .,4 MedTech West, Sahlgrenska University Hospital , Gothenburg, Sweden .,5 SAFER Vehicle and Traffic Safety Centre at Chalmers , Gothenburg, Sweden
| | - Mikael Persson
- 3 Department of Signals and Systems, Chalmers University of Technology , Gothenburg, Sweden .,4 MedTech West, Sahlgrenska University Hospital , Gothenburg, Sweden
| | - Lars Jönsson
- 6 Department of Neuroradiology, Sahlgrenska University Hospital , Gothenburg, Sweden
| | - Thomas Skoglund
- 1 Department of Neurosurgery, Sahlgrenska University Hospital , Gothenburg, Sweden .,2 Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, The Sahlgrenska Academy at the University of Gothenburg , Gothenburg, Sweden
| | - Mikael Elam
- 4 MedTech West, Sahlgrenska University Hospital , Gothenburg, Sweden .,7 Department of Clinical Neurophysiology, Sahlgrenska University Hospital , Gothenburg, Sweden
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