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Mason K, Maurino-Alperovich F, Holder D, Aristovich K. Noise-based correction for electrical impedance tomography. Physiol Meas 2024; 45:065002. [PMID: 38772395 DOI: 10.1088/1361-6579/ad4e93] [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: 12/11/2023] [Accepted: 05/21/2024] [Indexed: 05/23/2024]
Abstract
Objective.Noisy measurements frequently cause noisy and inaccurate images in impedance imaging. No post-processing technique exists to calculate the propagation of measurement noise and use this to suppress noise in the image. The objectives of this work were (1) to develop a post-processing method for noise-based correction (NBC) in impedance tomography, (2) to test whether NBC improves image quality in electrical impedance tomography (EIT), (3) to determine whether it is preferable to use correlated or uncorrelated noise for NBC, (4) to test whether NBC works within vivodata and (5) to test whether NBC is stable across model and perturbation geometries.Approach.EIT was performedin silicoin a 2D homogeneous circular domain and an anatomically realistic, heterogeneous 3D human head domain for four perturbations and 25 noise levels in each case. This was validated by performing EIT for four perturbations in a circular, saline tank in 2D as well as a human head-shaped saline tank with a realistic skull-like layer in 3D. Images were assessed on the error in the weighted spatial variance (WSV) with respect to the true, target image. The effect of NBC was also tested forin vivoEIT data of lung ventilation in a human thorax and cortical activity in a rat brain.Main results.On visual inspection, NBC maintained or increased image quality for all perturbations and noise levels in 2D and 3D, both experimentally andin silico. Analysis of the WSV showed that NBC significantly improved the WSV in nearly all cases. When the WSV was inferior with NBC, this was either visually imperceptible or a transformation between noisy reconstructions. Forin vivodata, NBC improved image quality in all cases and preserved the expected shape of the reconstructed perturbation.Significance.In practice, uncorrelated NBC performed better than correlated NBC and is recommended as a general-use post-processing technique in EIT.
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Affiliation(s)
- Kai Mason
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | | | - David Holder
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Kirill Aristovich
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
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2
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Pennati F, Angelucci A, Morelli L, Bardini S, Barzanti E, Cavallini F, Conelli A, Di Federico G, Paganelli C, Aliverti A. Electrical Impedance Tomography: From the Traditional Design to the Novel Frontier of Wearables. SENSORS (BASEL, SWITZERLAND) 2023; 23:1182. [PMID: 36772222 PMCID: PMC9921522 DOI: 10.3390/s23031182] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/16/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Electrical impedance tomography (EIT) is a medical imaging technique based on the injection of a current or voltage pattern through electrodes on the skin of the patient, and on the reconstruction of the internal conductivity distribution from the voltages collected by the electrodes. Compared to other imaging techniques, EIT shows significant advantages: it does not use ionizing radiation, is non-invasive and is characterized by high temporal resolution. Moreover, its low cost and high portability make it suitable for real-time, bedside monitoring. However, EIT is also characterized by some technical limitations that cause poor spatial resolution. The possibility to design wearable devices based on EIT has recently given a boost to this technology. In this paper we reviewed EIT physical principles, hardware design and major clinical applications, from the classical to a wearable setup. A wireless and wearable EIT system seems a promising frontier of this technology, as it can both facilitate making clinical measurements and open novel scenarios to EIT systems, such as home monitoring.
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Zhang T, Tian X, Liu X, Ye J, Fu F, Shi X, Liu R, Xu C. Advances of deep learning in electrical impedance tomography image reconstruction. Front Bioeng Biotechnol 2022; 10:1019531. [PMID: 36588934 PMCID: PMC9794741 DOI: 10.3389/fbioe.2022.1019531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 12/02/2022] [Indexed: 12/15/2022] Open
Abstract
Electrical impedance tomography (EIT) has been widely used in biomedical research because of its advantages of real-time imaging and nature of being non-invasive and radiation-free. Additionally, it can reconstruct the distribution or changes in electrical properties in the sensing area. Recently, with the significant advancements in the use of deep learning in intelligent medical imaging, EIT image reconstruction based on deep learning has received considerable attention. This study introduces the basic principles of EIT and summarizes the application progress of deep learning in EIT image reconstruction with regards to three aspects: a single network reconstruction, deep learning combined with traditional algorithm reconstruction, and multiple network hybrid reconstruction. In future, optimizing the datasets may be the main challenge in applying deep learning for EIT image reconstruction. Adopting a better network structure, focusing on the joint reconstruction of EIT and traditional algorithms, and using multimodal deep learning-based EIT may be the solution to existing problems. In general, deep learning offers a fresh approach for improving the performance of EIT image reconstruction and could be the foundation for building an intelligent integrated EIT diagnostic system in the future.
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Affiliation(s)
- Tao Zhang
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China,Drug and Instrument Supervision and Inspection Station, Xining Joint Logistics Support Center, Lanzhou, China
| | - Xiang Tian
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - XueChao Liu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - JianAn Ye
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - Feng Fu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - XueTao Shi
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - RuiGang Liu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - CanHua Xu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China,*Correspondence: CanHua Xu,
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4
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Mirhoseini M, Rezanejad Gatabi Z, Das S, Joveini S, Rezanezhad Gatabi I. Applications of Electrical Impedance Tomography in Neurology. Basic Clin Neurosci 2022; 13:595-608. [PMID: 37313030 PMCID: PMC10258591 DOI: 10.32598/bcn.2021.3087.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/24/2021] [Accepted: 05/14/2021] [Indexed: 11/02/2023] Open
Abstract
Introduction Electrical impedance tomography (EIT) is a non-invasive technique utilized in various medical applications, including brain imaging and other neurological diseases. Recognizing the physiological and anatomical characteristics of organs based on their electrical properties is one of the main applications of EIT, as each variety of tissue structure has its own electrical characteristics. The high potential of brain EIT is established in real-time supervision and early recognition of cerebral brain infarction, hemorrhage, and other diseases. In this paper, we review the studies on the neurological applications of EIT. Methods EIT calculates the internal electrical conductivity distribution of an organ by measuring its surface impedance. A series of electrodes are placed on the surface of the target tissue, and small alternating currents are injected. The related voltages are then observed and analyzed. The electrical permittivity and conductivity distributions inside the tissue are reconstructed by measuring the electrode voltages. Results The electrical characteristic of biological tissues is remarkably dependent on their structures. Some tissues are better electrical conductors than the others since they have more ions that can carry the electrical charges. This difference is attributed to changes in cellular water content, membrane properties, and destruction of tight junctions within cell membranes. Conclusion EIT is an extremely practical device for brain imaging, capturing fast electrical activities in the brain, imaging epileptic seizures, detecting intracranial bleeding, detecting cerebral edema, and diagnosing stroke.
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Affiliation(s)
- Mehri Mirhoseini
- Amol Faculty of Paramedical Sciences, Mazandaran University of Medical Sciences, Sari, Iran
| | - Zahra Rezanejad Gatabi
- Department of Pharmaceutics, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran
| | - Sayantan Das
- Faculty/College of Science and Mathematics, Texas A&M University, San Antonio, United States
| | - Sepideh Joveini
- Department of Pharmaceutics, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran
| | - Iman Rezanezhad Gatabi
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, United States
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Zheng M, Jahanandish H, Li H. Dynamic Classification of Imageless Bioelectrical Impedance Tomography Features with Attention-Driven Spatial Transformer Neural Network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2495-2501. [PMID: 36086650 DOI: 10.1109/embc48229.2022.9870921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Point-of-Care monitoring devices have proven to be pivotal in the timely screening and intervention of critical care patients. The urgent demands for their deployment in the COVID-19 pandemic era has translated into the escalation of rapid, reliable, and low-cost monitoring systems research and development. Electrical Impedance Tomography (EIT) is a highly promising modality in providing deep tissue imaging that aids in patient bedside diagnosis and treatment. Motivated to bring forth an accurate and intelligent EIT screening system, we bypassed the complexity and challenges typically associated with its image reconstruction and feature identification processes by solely focusing on the raw data output to extract the embedded knowledge. We developed a novel machine learning architecture based on an attention-driven spatial transformer neural network to specifically accommodate for the patterns and dependencies within EIT raw data. Through elaborate precision-mapped phantom experiments, we validated the reproduction and recognition of features with systemically controlled changes. We demonstrated over 95% accuracy via state-of-the-art machine learning models, and an enhanced performance using our adapted transformer pipeline with shorter training time and greater computational efficiency. Our approach of using imageless EIT driven by a novel attention-focused feature learning algorithm is highly promising in revolutionizing conventional EIT operations and augmenting its practical usage in medicine and beyond.
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Ke XY, Hou W, Huang Q, Hou X, Bao XY, Kong WX, Li CX, Qiu YQ, Hu SY, Dong LH. Advances in electrical impedance tomography-based brain imaging. Mil Med Res 2022; 9:10. [PMID: 35227324 PMCID: PMC8883715 DOI: 10.1186/s40779-022-00370-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 02/08/2022] [Indexed: 11/10/2022] Open
Abstract
Novel advances in the field of brain imaging have enabled the unprecedented clinical application of various imaging modalities to facilitate disease diagnosis and treatment. Electrical impedance tomography (EIT) is a functional imaging technique that measures the transfer impedances between electrodes on the body surface to estimate the spatial distribution of electrical properties of tissues. EIT offers many advantages over other neuroimaging technologies, which has led to its potential clinical use. This qualitative review provides an overview of the basic principles, algorithms, and system composition of EIT. Recent advances in the field of EIT are discussed in the context of epilepsy, stroke, brain injuries and edema, and other brain diseases. Further, we summarize factors limiting the development of brain EIT and highlight prospects for the field. In epilepsy imaging, there have been advances in EIT imaging depth, from cortical to subcortical regions. In stroke research, a bedside EIT stroke monitoring system has been developed for clinical practice, and data support the role of EIT in multi-modal imaging for diagnosing stroke. Additionally, EIT has been applied to monitor the changes in brain water content associated with cerebral edema, enabling the early identification of brain edema and the evaluation of mannitol dehydration. However, anatomically realistic geometry, inhomogeneity, cranium completeness, anisotropy and skull type, etc., must be considered to improve the accuracy of EIT modeling. Thus, the further establishment of EIT as a mature and routine diagnostic technique will necessitate the accumulation of more supporting evidence.
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Affiliation(s)
- Xi-Yang Ke
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Wei Hou
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Qi Huang
- CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, Jiangsu, China
| | - Xue Hou
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Xue-Ying Bao
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Wei-Xuan Kong
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China
| | - Cheng-Xiang Li
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Yu-Qi Qiu
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Si-Yi Hu
- CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, Jiangsu, China.
| | - Li-Hua Dong
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China. .,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China. .,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China.
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Ravagli E, Mastitskaya S, Holder DS, Aristovich KY. Simplifying the hardware requirements for fast neural EIT of peripheral nerves. Physiol Meas 2021; 43. [PMID: 34915462 DOI: 10.1088/1361-6579/ac43c0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/16/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The main objective of this study was to assess the feasibility of lowering the hardware requirements for fast neural EIT in order to support the distribution of this technique. Specifically, the feasibility of replacing the commercial modules present in the existing high-end setup with compact and cheap customized circuitry was assessed. APPROACH Nerve EIT imaging was performed on rat sciatic nerves with both our standard ScouseTom setup and a customized version in which commercial benchtop current sources were replaced by custom circuitry. Electrophysiological data and images collected in the same experimental conditions with the two setups were compared. Data from the customized setup was subject to a down-sampling analysis to simulate the use of a recording module with lower specifications. MAIN RESULTS Compound action potentials (573±287µV and 487±279µV, p=0.28) and impedance changes (36±14µV and 31±16µV, p=0.49) did not differ significantly when measured using commercial high-end current sources or our custom circuitry, respectively. Images reconstructed from both setups showed neglibile (<1voxel, i.e. 40µm) difference in peak location and a high degree of correlation (R2=0.97). When down-sampling from 24 to 16 bits ADC resolution and from 100KHz to 50KHz sampling frequency, signal-to-noise ratio showed acceptable decrease (<-20%), and no meaningful image quality loss was detected (peak location difference <1voxel, pixel-by-pixel correlation R2=0.99). SIGNIFICANCE The technology developed for this study greatly reduces the cost and size of a fast neural EIT setup without impacting quality and thus promotes the adoption of this technique by the neuroscience research community.
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Affiliation(s)
- Enrico Ravagli
- Medical Physics and Biomedical Engineering, University College London, Malet Place Engineering Building, London, WC1E 6BT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Svetlana Mastitskaya
- Medical Physics and Biomedical Engineering, University College London, Malet Place Engineering Building, London, London, WC1E 6BT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - David S Holder
- Department of Medical Physics and Bioengineering, University College London, Malet Place Engineering Building, Gower Street, London, London, WC1E 6BT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Kirill Y Aristovich
- Department of Medical Physics and Bioengineering, University College London, Malet Place Engineering Building - Gower Street - London, London, WC1E 6BT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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Zheng M, Ibrahim B. Performance Prediction, Sensitivity Analysis and Parametric Optimization of Electrical Impedance Tomography Using A Bioelectrical Tissue Simulation Platform. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2864-2870. [PMID: 34891845 DOI: 10.1109/embc46164.2021.9629786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
There is an urgent need to bring forth portable, low-cost, point-of-care diagnostic instruments to monitor patient health and wellbeing. This is elevated by the COVID-19 global pandemic in which the availability of proper lung imaging equipment has proven to be pivotal in the timely treatment of patients. Electrical impedance tomography (EIT) has long been studied and utilized as such a critical imaging device in hospitals especially for lung ventilation. Despite decades of research and development, many challenges remain with EIT in terms of 1) optimal image reconstruction algorithms, 2) simulation and measurement protocols, 3) hardware imperfections, and 4) uncompensated tissue bioelectrical physiology. Due to the inter-connectivity of these challenges, singular solutions to improve EIT performance continue to fall short of the desired sensitivity and accuracy. Motivated to gain a better understanding and optimization of the EIT system, we report the development of a bioelectric facsimile simulator demonstrating the dynamic operations, sensitivity analysis, and reconstruction outcome prediction of the EIT sensor with stepwise visualization. By building a sandbox platform to incorporate full anatomical and bioelectrical properties of the tissue under study into the simulation, we created a tissue-mimicking phantom with adjustable EIT parameters to interpret bioelectrical interactions and to optimize image reconstruction accuracy through improved hardware setup and sensing protocol selections.
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9
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Witkowska-Wrobel A, Aristovich K, Crawford A, Perkins JD, Holder D. Imaging of focal seizures with Electrical Impedance Tomography and depth electrodes in real time. Neuroimage 2021; 234:117972. [PMID: 33757909 PMCID: PMC8204270 DOI: 10.1016/j.neuroimage.2021.117972] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 01/31/2021] [Accepted: 03/12/2021] [Indexed: 11/26/2022] Open
Abstract
Intracranial EEG is the current gold standard technique for localizing seizures for surgery, but it can be insensitive to tangential dipole or distant sources. Electrical Impedance Tomography (EIT) offers a novel method to improve coverage and seizure onset localization. The feasibility of EIT has been previously assessed in a computer simulation, which revealed an improved accuracy of seizure detection with EIT compared to intracranial EEG. In this study, slow impedance changes, evoked by cell swelling occurring over seconds, were reconstructed in real time by frequency division multiplexing EIT using depth and subdural electrodes in a swine model of epilepsy. EIT allowed to generate repetitive images of ictal events at similar time course to fMRI but without its significant limitations. EIT was recorded with a system consisting of 32 parallel current sources and 64 voltage recorders. Seizures triggered with intracranial injection of benzylpenicillin (BPN) in five pigs caused a repetitive peak impedance increase of 3.4 ± 1.5 mV and 9.5 ± 3% (N =205 seizures); the impedance signal change was seen already after a single, first seizure. EIT enabled reconstruction of the seizure onset 9 ± 1.5 mm from the BPN cannula and 7.5 ± 1.1 mm from the closest SEEG contact (p<0.05, n =37 focal seizures in three pigs) and it could address problems with sampling error in intracranial EEG. The amplitude of the impedance change correlated with the spread of the seizure on the SEEG (p <<0.001, n =37). The results presented here suggest that combining a parallel EIT system with intracranial EEG monitoring has a potential to improve the diagnostic yield in epileptic patients and become a vital tool in improving our understanding of epilepsy.
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Affiliation(s)
| | - Kirill Aristovich
- Medical Physics and Biomedical Engineering, University College London, UK
| | - Abbe Crawford
- Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, Hertfordshire AL9 7TA, UK
| | - Justin D Perkins
- Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, Hertfordshire AL9 7TA, UK
| | - David Holder
- Medical Physics and Biomedical Engineering, University College London, UK
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10
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Hannan S, Aristovich K, Faulkner M, Avery J, Walker MC, Holder DS. Imaging slow brain activity during neocortical and hippocampal epileptiform events with electrical impedance tomography. Physiol Meas 2021; 42:014001. [PMID: 33361567 DOI: 10.1088/1361-6579/abd67a] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
OBJECTIVE Electrical impedance tomography (EIT) is an imaging technique that produces tomographic images of internal impedance changes within an object using surface electrodes. It can be used to image the slow increase in cerebral tissue impedance that occurs over seconds during epileptic seizures, which is attributed to cell swelling due to disturbances in ion homeostasis following hypersynchronous neuronal firing and its associated metabolic demands. In this study, we characterised and imaged this slow impedance response during neocortical and hippocampal epileptiform events in the rat brain and evaluated its relationship to the underlying neural activity. APPROACH Neocortical or hippocampal seizures, comprising repeatable series of high-amplitude ictal spikes, were induced by electrically stimulating the sensorimotor cortex or perforant path of rats anaesthetised with fentanyl-isoflurane. Transfer impedances were measured during ≥30 consecutive seizures, by applying a sinusoidal current through independent electrode pairs on an epicortical array, and combined to generate an EIT image of slow activity. MAIN RESULTS The slow impedance responses were consistently time-matched to the end of seizures and EIT images of this activity were reconstructed reproducibly in all animals (p < 0.03125, N = 5). These displayed foci of activity that were spatially confined to the facial somatosensory cortex and dentate gyrus for neocortical and hippocampal seizures, respectively, and encompassed a larger volume as the seizure progressed. Centre-of-mass analysis of reconstructions revealed that this activity corresponded to the true location of the epileptogenic zone, as determined by EEG recordings and fast neural EIT measurements which were obtained simultaneously. SIGNIFICANCE These findings suggest that the slow impedance response presents a reliable marker of hypersynchronous neuronal activity during epileptic seizures and can thus be utilised for investigating the mechanisms of epileptogenesis in vivo and for aiding localisation of the epileptogenic zone during presurgical evaluation of patients with refractory epilepsies.
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Affiliation(s)
- Sana Hannan
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom
| | - Kirill Aristovich
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom
| | - Mayo Faulkner
- Wolfson Institute for Biomedical Research, University College London, United Kingdom
| | - James Avery
- Department of Surgery and Cancer, Imperial College London, United Kingdom
| | - Matthew C Walker
- UCL Queen Square Institute of Neurology, University College London, United Kingdom
| | - David S Holder
- Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom
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Mansouri S, Alharbi Y, Haddad F, Chabcoub S, Alshrouf A, Abd-Elghany AA. Electrical Impedance Tomography - Recent Applications and Developments. JOURNAL OF ELECTRICAL BIOIMPEDANCE 2021; 12:50-62. [PMID: 35069942 PMCID: PMC8667811 DOI: 10.2478/joeb-2021-0007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Indexed: 06/12/2023]
Abstract
Electrical impedance tomography (EIT) is a low-cost noninvasive imaging method. The main purpose of this paper is to highlight the main aspects of the EIT method and to review the recent advances and developments. The advances in instrumentation and in the different image reconstruction methods and systems are demonstrated in this review. The main applications of the EIT are presented and a special attention made to the papers published during the last years (from 2015 until 2020). The advantages and limitations of EIT are also presented. In conclusion, EIT is a promising imaging approach with a strong potential that has a large margin of progression before reaching the maturity phase.
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Affiliation(s)
- Sofiene Mansouri
- Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
- Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, TunisTunisia
| | - Yousef Alharbi
- Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Fatma Haddad
- Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, TunisTunisia
| | - Souhir Chabcoub
- Laboratory of Biophysics and Medical Technologies, Higher Institute of Medical Technologies of Tunis, University of Tunis El Manar, TunisTunisia
| | - Anwar Alshrouf
- Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Amr A. Abd-Elghany
- Department of Biomedical Technology, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
- Biophysics Department, Faculty of Science, Cairo University, CairoEgypt
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Hannan S, Faulkner M, Aristovich K, Avery J, Walker MC, Holder DS. Optimised induction of on-demand focal hippocampal and neocortical seizures by electrical stimulation. J Neurosci Methods 2020; 346:108911. [DOI: 10.1016/j.jneumeth.2020.108911] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 08/10/2020] [Accepted: 08/12/2020] [Indexed: 11/25/2022]
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13
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Hannan S, Faulkner M, Aristovich K, Avery J, Walker MC, Holder DS. In vivo imaging of deep neural activity from the cortical surface during hippocampal epileptiform events in the rat brain using electrical impedance tomography. Neuroimage 2020; 209:116525. [DOI: 10.1016/j.neuroimage.2020.116525] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 12/12/2019] [Accepted: 01/06/2020] [Indexed: 02/07/2023] Open
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Hope J, Aqrawe Z, Lim M, Vanholsbeeck F, McDaid A. Increasing signal amplitude in electrical impedance tomography of neural activity using a parallel resistor inductor capacitor (RLC) circuit. J Neural Eng 2019; 16:066041. [PMID: 31536974 DOI: 10.1088/1741-2552/ab462b] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE To increase the impedance signal amplitude produced during neural activity using a novel approach of implementing a parallel resistor inductor capacitor (RLC) circuit across the current source used in electrical impedance tomography (EIT) of peripheral nerve. APPROACH The frequency response of the impedance signal was characterized in the range 4-18 kHz, then a frequency range with significant capacitive charge transfer was selected for experiment with the RLC circuit. Design of the RLC circuit was aided by in vitro impedance measurements on nerve and nerve cuff in the range 5 Hz to 50 kHz. MAIN RESULTS The frequency response of the impedance signal across 4-18 kHz showed maximum amplitude at 6-8 kHz, and steady decline in amplitude between 8 and 18 kHz with -6 dB reduction at 14 kHz. The frequency range 17 ± 1 kHz was selected for the RLC experiment. The RLC experiment was performed on four subjects using an RLC circuit designed to produce a resonant frequency of 17 kHz with a bandwidth of 3.6 kHz, and containing a 22 mH inductive element and a 3.45 nF capacitive element with +0.8/- 3.45 nF manual tuning range. With the RLC circuit connected, relative increases in the impedance signal (±3σ noise) of 44% (±15%), 33% (±30%), 37% (±8.6%), and 16% (±19%) were produced. SIGNIFICANCE The increase in impedance signal amplitude at high frequencies, generated by the novel implementation of a parallel RLC circuit across the drive current, improves spatial resolution by increasing the number of parallel drive currents which can be implemented in a frequency division multiplexed (FDM) EIT system, and aids the long term goal of a real-time FDM EIT system by reducing the need for ensemble averaging.
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Affiliation(s)
- J Hope
- The Department of Mechanical Engineering, The University of Auckland, Auckland, New Zealand. Dodd Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
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Tarotin I, Aristovich K, Holder D. Simulation of impedance changes with a FEM model of a myelinated nerve fibre. J Neural Eng 2019; 16:056026. [DOI: 10.1088/1741-2552/ab2d1c] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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16
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Yang B, Li B, Xu C, Hu S, Dai M, Xia J, Luo P, Shi X, Zhao Z, Dong X, Fei Z, Fu F. Comparison of electrical impedance tomography and intracranial pressure during dehydration treatment of cerebral edema. NEUROIMAGE-CLINICAL 2019; 23:101909. [PMID: 31284231 PMCID: PMC6612924 DOI: 10.1016/j.nicl.2019.101909] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Revised: 06/19/2019] [Accepted: 06/24/2019] [Indexed: 11/04/2022]
Abstract
Cerebral edema after brain injury can lead to brain damage and death if diagnosis and treatment are delayed. This study investigates the feasibility of employing electrical impedance tomography (EIT) as a non-invasive imaging tool for monitoring the development of cerebral edema, in which impedance imaging of the brain related to brain water content is compared with intracranial pressure (ICP). We enrolled forty patients with cerebral hemorrhage who underwent lateral external ventricular drain with intraventricular ICP and EIT monitoring for 3 h after initiation of dehydration treatment. The average reconstructed impedance value (ARV) calculated from EIT images was compared with ICP. Dehydration effects induced changes in ARV and ICP showed a close negative correlation in all patients, and the mean correlation reached R2 = 0.78 ± 0.16 (p < .001). A regression equation (R2 = 0.62, p < .001) was formulated from the total of measurement data. The 95% limits of agreement were − 6.13 to 6.13 mmHg. Adaptive clustering and variance analysis of normalized changes in ARV and ICP showed 92.5% similarity and no statistically significant differences (p > .05). Moreover, the sensitivity, specificity and area under the curve of changes in ICP >10 mmHg were 0.65, 0.73 and 0.70 respectively. The findings show that EIT can monitor changes in brain water content associated with cerebral edema, which could provide a real-time and non-invasive imaging tool for early identification of cerebral edema and the evaluation of mannitol dehydration. Changes in brain water content due to cerebral edema alter EIT and ICP simultaneously. EIT has a close negative correlation with ICP during changes in brain water content. Cerebral edema can be early identified by EIT for initiating timely therapy. The efficacy of dehydration can be evaluation by EIT for guiding personalized therapy. The results suggest EIT can monitor cerebral edema real-timely and non-invasively.
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Affiliation(s)
- Bin Yang
- Department of Biomedical Engineering, Fourth Military Medical University, 710032 Xi'an, China
| | - Bing Li
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, 710032 Xi'an, China
| | - Canhua Xu
- Department of Biomedical Engineering, Fourth Military Medical University, 710032 Xi'an, China
| | - Shijie Hu
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, 710032 Xi'an, China
| | - Meng Dai
- Department of Biomedical Engineering, Fourth Military Medical University, 710032 Xi'an, China
| | - Junying Xia
- Department of Biomedical Engineering, Fourth Military Medical University, 710032 Xi'an, China
| | - Peng Luo
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, 710032 Xi'an, China
| | - Xuetao Shi
- Department of Biomedical Engineering, Fourth Military Medical University, 710032 Xi'an, China
| | - Zhanqi Zhao
- Department of Biomedical Engineering, Fourth Military Medical University, 710032 Xi'an, China; Institute of Technical Medicine, Furtwangen University, 78054 Villingen-Schwenningen, Germany
| | - Xiuzhen Dong
- Department of Biomedical Engineering, Fourth Military Medical University, 710032 Xi'an, China
| | - Zhou Fei
- Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, 710032 Xi'an, China.
| | - Feng Fu
- Department of Biomedical Engineering, Fourth Military Medical University, 710032 Xi'an, China.
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Hannan S, Faulkner M, Aristovich K, Avery J, Holder D. Investigating the safety of fast neural electrical impedance tomography in the rat brain. Physiol Meas 2019; 40:034003. [DOI: 10.1088/1361-6579/ab0d53] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Avery J, Dowrick T, Witkowska-Wrobel A, Faulkner M, Aristovich K, Holder D. Simultaneous EIT and EEG using frequency division multiplexing. Physiol Meas 2019; 40:034007. [DOI: 10.1088/1361-6579/ab0bbc] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Moridani MK, Choopani F, Kia M. Recognition of Lung Volume Condition based on Phase Space Mapping Using Electrical Impedance Tomography. JOURNAL OF ELECTRICAL BIOIMPEDANCE 2019; 10:34-39. [PMID: 33584880 PMCID: PMC7531212 DOI: 10.2478/joeb-2019-0005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Indexed: 06/12/2023]
Abstract
The purpose of this paper is to identify differences between abnormal and normal lung signals gathered by an EIT device, which is a new, non-invasive system that seeks the electrical conductivity and permittivity inside a body. Lung performances in patients are investigated using Phase Space Mapping technique on Electrical EIT signals. The database used in this paper contains 82 registered records of 52 individuals with proper lung volume. The results of this paper show that as the delay parameter (τ) increases, the SD1 parameter of phase space mapping indicates a significant difference between normal and abnormal lung volumes. The value of the SD1 parameter with τ = 6 in the case that the lung volume is in a normal condition is 342.57 ± 32.75 while it is 156.71 ± 26.01 in non-optimal mode. This method can be used to identify the patients' lung volumes with chronic respiratory illnesses and is an accurate assessment of the diverse methods to treat respiratory system illnesses in addition to saving various therapeutic costs and dangerous consequences that are likely to occur by using improper treatment methods. It can also reduce the required treatment durations.
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Affiliation(s)
- Mohammad Karimi Moridani
- Department of Biomedical Engineering, Faculty of Health, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Fatemeh Choopani
- Department of Biomedical Engineering, Faculty of Health, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Mandana Kia
- Department of Biomedical Engineering, Faculty of Health, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
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