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Nwokoye II, Triantis IF. A 3 MHz Low-Error Adaptive Howland Current Source for High-Frequency Bioimpedance Applications. SENSORS (BASEL, SWITZERLAND) 2024; 24:4357. [PMID: 39001136 PMCID: PMC11243945 DOI: 10.3390/s24134357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/07/2024] [Accepted: 06/25/2024] [Indexed: 07/16/2024]
Abstract
Bioimpedance is a diagnostic sensing method used in medical applications, ranging from body composition assessment to detecting skin cancer. Commonly, discrete-component (and at times integrated) circuit variants of the Howland Current Source (HCS) topology are employed for injection of an AC current. Ideally, its amplitude should remain within 1% of its nominal value across a frequency range, and that nominal value should be programmable. However, the method's applicability and accuracy are hindered due to the current amplitude diminishing at frequencies above 100 kHz, with very few designs accomplishing 1 MHz, and only at a single nominal amplitude. This paper presents the design and implementation of an adaptive current source for bioimpedance applications employing automatic gain control (AGC). The "Adaptive Howland Current Source" (AHCS) was experimentally tested, and the results indicate that the design can achieve less than 1% amplitude error for both 1 mA and 100 µA currents for bandwidths up to 3 MHz. Simulations also indicate that the system can be designed to achieve up to 19% noise reduction relative to the most common HCS design. AHCS addresses the need for high bandwidth AC current sources in bioimpedance spectroscopy, offering automatic output current compensation without constant recalibration. The novel structure of AHCS proves crucial in applications requiring higher β-dispersion frequencies exceeding 1 MHz, where greater penetration depths and better cell status assessment can be achieved, e.g., in the detection of skin or breast cancer.
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Affiliation(s)
| | - Iasonas F. Triantis
- Research Centre for Biomedical Engineering, City, University of London, London EC1V 0HB, UK;
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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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Murphy EK, Smith J, Kokko MA, Rutkove SB, Halter RJ. Rapid patient-specific FEM meshes from 3D smart-phone based scans. Physiol Meas 2024; 45:025008. [PMID: 38320323 PMCID: PMC10901069 DOI: 10.1088/1361-6579/ad26d2] [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] [Received: 10/17/2023] [Accepted: 02/06/2024] [Indexed: 02/08/2024]
Abstract
Objective.The objective of this study was to describe and evaluate a smart-phone based method to rapidly generate subject-specific finite element method (FEM) meshes. More accurate FEM meshes should lead to more accurate thoracic electrical impedance tomography (EIT) images.Approach.The method was evaluated on an iPhone®that utilized an app called Heges, to obtain 3D scans (colored, surface triangulations), a custom belt, and custom open-source software developed to produce the subject-specific meshes. The approach was quantitatively validated via mannequin and volunteer tests using an infrared tracker as the gold standard, and qualitatively assessed in a series of tidal-breathing EIT images recorded from 9 subjects.Main results.The subject-specific meshes can be generated in as little as 6.3 min, which requires on average 3.4 min of user interaction. The mannequin tests yielded high levels of precision and accuracy at 3.2 ± 0.4 mm and 4.0 ± 0.3 mm root mean square error (RMSE), respectively. Errors on volunteers were only slightly larger (5.2 ± 2.1 mm RMSE precision and 7.7 ± 2.9 mm RMSE accuracy), illustrating the practical RMSE of the method.Significance.Easy-to-generate, subject-specific meshes could be utilized in the thoracic EIT community, potentially reducing geometric-based artifacts and improving the clinical utility of EIT.
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Affiliation(s)
- Ethan K Murphy
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
| | - Joel Smith
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
| | - Michael A Kokko
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
| | - Seward B Rutkove
- Department of Neurology, Beth Israel Deaconess Medical Center (BIDMC), Boston, MA 02215, United States of America
- Harvard Medical School, Boston, MA 02115, United States of America
| | - Ryan J Halter
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
- Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, United States of America
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Lee H, Culpepper J, Porter E. Analysis of electrode arrangements for brain stroke diagnosis via electrical impedance tomography through numerical computational models. Physiol Meas 2024; 45:025006. [PMID: 38306666 DOI: 10.1088/1361-6579/ad252c] [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: 09/25/2023] [Accepted: 02/02/2024] [Indexed: 02/04/2024]
Abstract
Objective.Rapid stroke-type classification is crucial for improved prognosis. However, current methods for classification are time-consuming, require expensive equipment, and can only be used in the hospital. One method that has demonstrated promise in a rapid, low-cost, non-invasive approach to stroke diagnosis is electrical impedance tomography (EIT). While EIT for stroke diagnosis has been the topic of several studies in recent years, to date, the impact of electrode placements and arrangements has rarely been analyzed or tested and only in limited scenarios. Optimizing the location and choice of electrodes can have the potential to improve performance and reduce hardware cost and complexity and, most importantly, diagnosis time.Approach.In this study, we analyzed the impact of electrodes in realistic numerical models by (1) investigating the effect of individual electrodes on the resulting simulated EIT boundary measurements and (2) testing the performance of different electrode arrangements using a machine learning classification model.Main results.We found that, as expected, the electrodes deemed most significant in detecting stroke depend on the location of the electrode relative to the stroke lesion, as well as the role of the electrode. Despite this dependence, there are notable electrodes used in the models that are consistently considered to be the most significant across the various stroke lesion locations and various head models. Moreover, we demonstrate that a reduction in the number of electrodes used for the EIT measurements is possible, given that the electrodes are approximately evenly distributed.Significance.In this way, electrode arrangement and location are important variables to consider when improving stroke diagnosis methods using EIT.
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Affiliation(s)
- Hannah Lee
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, United States of America
| | - Jared Culpepper
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, United States of America
| | - Emily Porter
- Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Biomedical Engineering, McGill University, Montreal, Canada
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Setyawan G, Sejati PA, Ibrahim KA, Takei M. Breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation-time distribution (EIT-GRTD). JOURNAL OF ELECTRICAL BIOIMPEDANCE 2024; 15:99-106. [PMID: 39263531 PMCID: PMC11387985 DOI: 10.2478/joeb-2024-0011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Indexed: 09/13/2024]
Abstract
The comparison between breast cancer recognition by electrical impedance tomography implemented with Gaussian relaxation time distribution (EIT-GRTD) and conventional EIT has been conducted to evaluate the optimal frequency for cancer detection f cancer. The EIT-GRTD has two steps, which are 1) the determination of the f cancer and 2) the refinement of breast reconstruction through time-constant enhancement. This paper employs two-dimensional numerical simulations by a finite element method (FEM) software to replicate the process of breast cancer recognition. The simulation is constructed based on two distinct electrical properties, which are conductivity σ and permitivitty ε, inherent to two major breast tissues: adipose tissues, and breast cancer tissues. In this case, the σ and ε of breast cancer σ cancer, ε cancer are higher than adipose tissues σ adipose, ε adipose. The simulation results indicate that the most effective frequency for breast cancer detection based on EIT-GRTD is f cancer = 56,234 Hz. Meanwhile, conventional EIT requires more processing to determine the f cancer based on image results or spatial conductivity analysis. Quantitatively, both EIT-GRTD and conventional EIT can clearly show the position of the cancer in layers 1 and 2 for EIT-GRTD and only layer 1 for conventional EIT.
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Affiliation(s)
- Galih Setyawan
- Department of Mechanical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, Japan
- Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Prima Asmara Sejati
- Department of Mechanical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, Japan
- Department of Electrical Engineering and Informatics, Vocational College, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Kiagus Aufa Ibrahim
- Department of Mechanical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, Japan
| | - Masahiro Takei
- Department of Mechanical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, Japan
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Toivanen J, Paldanius A, Dekdouk B, Candiani V, Hänninen A, Savolainen T, Strbian D, Forss N, Hyvönen N, Hyttinen J, Kolehmainen V. Simulation-based feasibility study of monitoring of intracerebral hemorrhages and detection of secondary hemorrhages using electrical impedance tomography. J Med Imaging (Bellingham) 2024; 11:014502. [PMID: 38299159 PMCID: PMC10826852 DOI: 10.1117/1.jmi.11.1.014502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/22/2023] [Accepted: 01/08/2024] [Indexed: 02/02/2024] Open
Abstract
Purpose We present a simulation-based feasibility study of electrical impedance tomography (EIT) for continuous bedside monitoring of intracerebral hemorrhages (ICH) and detection of secondary hemorrhages. Approach We simulated EIT measurements for six different hemorrhage sizes at two different hemorrhage locations using an anatomically detailed computational head model. Using this dataset, we test the ICH monitoring and detection performance of our tailor-made, patient-specific stroke-monitoring algorithm that utilizes a novel combination of nonlinear region-of-interest difference imaging, parallel level sets regularization and a prior-conditioned least squares algorithm. We compare the results of our algorithm to the results of two reference algorithms, a total variation regularized absolute imaging algorithm and a linear difference imaging algorithm. Results The tailor-made stroke-monitoring algorithm is capable of indicating smaller changes in the simulated hemorrhages than either of the reference algorithms, indicating better monitoring and detection performance. Conclusions Our simulation results from the anatomically detailed head model indicate that EIT equipped with a patient-specific stroke-monitoring algorithm is a promising technology for the unmet clinical need of having a technology for continuous bedside monitoring of brain status of acute stroke patients.
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Affiliation(s)
- Jussi Toivanen
- University of Eastern Finland, Department of Technical Physics, Kuopio, Finland
| | - Antti Paldanius
- Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland
| | - Bachir Dekdouk
- Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland
| | | | - Asko Hänninen
- University of Eastern Finland, Department of Technical Physics, Kuopio, Finland
| | - Tuomo Savolainen
- University of Eastern Finland, Department of Technical Physics, Kuopio, Finland
| | - Daniel Strbian
- Helsinki University Hospital, HUS Neurocenter, Helsinki, Finland
| | - Nina Forss
- Helsinki University Hospital, HUS Neurocenter, Helsinki, Finland
- Aalto University, Department of Neuroscience and Biomedical Engineering, Helsinki, Finland
| | - Nuutti Hyvönen
- Aalto University, Department of Mathematics and Systems Analysis, Helsinki, Finland
| | - Jari Hyttinen
- Tampere University, Faculty of Medicine and Health Technology, Tampere, Finland
| | - Ville Kolehmainen
- University of Eastern Finland, Department of Technical Physics, Kuopio, Finland
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Culpepper J, Lee H, Santorelli A, Porter E. Applied machine learning for stroke differentiation by electrical impedance tomography with realistic numerical models. Biomed Phys Eng Express 2023; 10:015012. [PMID: 37939489 DOI: 10.1088/2057-1976/ad0adf] [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: 08/28/2023] [Accepted: 11/08/2023] [Indexed: 11/10/2023]
Abstract
Electrical impedance tomography (EIT) may have potential to overcome existing limitations in stroke differentiation, enabling low-cost, rapid, and mobile data collection. Combining bioimpedance measurement technologies such as EIT with machine learning classifiers to support decision-making can avoid commonly faced reconstruction challenges due to the nonlinear and ill-posed nature of EIT imaging. Therefore, in this work, we advance this field through a study integrating realistic head models with clinically relevant test scenarios, and a robust architecture consisting of nested cross-validation and principal component analysis. Specifically, realistic head models are designed which incorporate the highly conductive layers of cerebrospinal fluid in the subarachnoid space and ventricles. In total, 135 unique models are created to represent a large patient population, with normal, haemorrhagic, and ischemic brains. Simulated EIT voltage data generated from these models are used to assess the classification performance of support vector machines. Parameters explored include driving frequency, signal-to-noise ratio, kernel function, and composition of binary classes. Classifier accuracy at 60 dB signal-to-noise ratio, reported as mean and standard deviation, are (79.92% ± 10.82%) for lesion differentiation, (74.78% ± 3.79%) for lesion detection, (77.49% ± 15.90%) for bleed detection, and (60.31% ± 3.98%) for ischemia detection (after ruling out bleed). The results for each method were obtained with statistics from 3 independent runs with 17,280 observations, polynomial kernel functions, and feature reduction of 76% by PCA (from 208 to 50 features). While results of this study show promise for stroke differentiation using EIT data, our findings indicate that the achievable accuracy is highly dependent on the classification scenario and application-specific classifiers may be necessary to achieve acceptable accuracy.
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Affiliation(s)
| | - Hannah Lee
- University of Texas at Austin, United States of America
| | | | - Emily Porter
- University of Texas at Austin, United States of America
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Yu Y, Kalra AM, Anand G, Lowe A. A Pilot Study Examining the Dielectric Response of Human Forearm Tissues. BIOSENSORS 2023; 13:961. [PMID: 37998136 PMCID: PMC10669245 DOI: 10.3390/bios13110961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 10/06/2023] [Accepted: 10/24/2023] [Indexed: 11/25/2023]
Abstract
This work aims to describe the dielectric behaviors of four main tissues in the human forearm using mathematical modelling, including fat, muscle, blood and bone. Multi-frequency bioimpedance analysis (MF-BIA) was initially performed using the finite element method (FEM) with a 3D forearm model to estimate impedance spectra from 10 kHz to 1 MHz, followed by a pilot study involving two healthy subjects to characterize the response of actual forearm tissues from 1 kHz to 349 kHz. Both the simulation and experimental results were fitted to a single-dispersion Cole model (SDCM) and a multi-dispersion Cole model (MDCM) to determine the Cole parameters for each tissue. Cole-type responses of both simulated and actual human forearms were observed. A paired t-test based on the root mean squared error (RMSE) values indicated that both Cole models performed comparably in fitting both simulated and measured bioimpedance data. However, MDCM exhibited higher accuracy, with a correlation coefficient (R2) of 0.99 and 0.89, RMSE of 0.22 Ω and 0.56 Ω, mean difference (mean ± standard deviation) of 0.00 ± 0.23 Ω and -0.28 ± 0.23 Ω, and mean absolute error (MAE) of 0.0007 Ω and 0.2789 Ω for the real part and imaginary part of impedance, respectively. Determining the electrical response of multi-tissues can be helpful in developing physiological monitoring of an organ or a section of the human body through MF-BIA and hemodynamic monitoring by filtering out the impedance contributions from the surrounding tissues to blood-flow-induced impedance variations.
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Affiliation(s)
| | - Anubha Manju Kalra
- Institute of Biomedical Technologies, Auckland University of Technology, Auckland 1010, New Zealand; (Y.Y.); (G.A.); (A.L.)
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Ma J, Guo J, Li Y, Wang Z, Dong Y, Ma J, Zhu Y, Wu G, Yi L, Shi X. Exploratory study of a multifrequency EIT-based method for detecting intracranial abnormalities. Front Neurol 2023; 14:1210991. [PMID: 37638201 PMCID: PMC10457004 DOI: 10.3389/fneur.2023.1210991] [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: 04/25/2023] [Accepted: 07/20/2023] [Indexed: 08/29/2023] Open
Abstract
Objective The purpose of this paper is to compare the differences in the features of multifrequency electrical impedance tomography (MFEIT) images of human heads between healthy subjects and patients with brain diseases and to explore the possibility of applying MFEIT to intracranial abnormality detection. Methods Sixteen healthy volunteers and 8 patients with brain diseases were recruited as subjects, and the cerebral MFEIT data of 9 frequencies in the range of 21 kHz - 100 kHz of all subjects were acquired with an MFEIT system. MFEIT image sequences were obtained according to certain imaging algorithms, and the area ratio of the ROI (AR_ROI) and the mean value of the reconstructed resistivity change of the ROI (MVRRC_ROI) on both the left and right sides of these images were extracted. The geometric asymmetry index (GAI) and intensity asymmetry index (IAI) were further proposed to characterize the symmetry of MFEIT images based on the extracted indices and to statistically compare and analyze the differences between the two groups of subjects on MFEIT images. Results There were no significant differences in either the AR_ROI or the MVRRC_ROI between the two sides of the brains of healthy volunteers (p > 0.05); some of the MFEIT images mainly in the range of 30 kHz - 60 kHz of patients with brain diseases showed stronger resistivity distributions (larger area or stronger signal) that were approximately symmetric with the location of the lesions. However, statistical analysis showed that the AR_ROI and the MVRRC_ROI on the healthy sides of MFEIT images of patients with unilateral brain disease were not significantly different from those on the affected side (p > 0.05). The GAI and IAI were higher in all patients with brain diseases than in healthy volunteers except for 80 kHz (p < 0.05). Conclusion There were significant differences in the geometric symmetry and the signal intensity symmetry of the reconstructed targets in the MFEIT images between healthy volunteers and patients with brain diseases, and the above findings provide a reference for the rapid detection of intracranial abnormalities using MFEIT images and may provide a basis for further exploration of MFEIT for the detection of brain diseases.
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Affiliation(s)
- Jieshi Ma
- Department of Medical Engineering, Army Medical Center of PLA, Chongqing, China
| | - Jie Guo
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, China
| | - Yang Li
- Department of Medical Engineering, Army Medical Center of PLA, Chongqing, China
| | - Zheng Wang
- Department of Neurosurgery, Army Medical Center of PLA, Chongqing, China
| | - Yunpeng Dong
- Department of Neurosurgery, Army Medical Center of PLA, Chongqing, China
| | - Jianxing Ma
- Department of Neurosurgery, Army Medical Center of PLA, Chongqing, China
| | - Yan Zhu
- Hangzhou Utron Technology Co., Ltd., Hangzhou, China
| | - Guan Wu
- Hangzhou Utron Technology Co., Ltd., Hangzhou, China
| | - Liang Yi
- Department of Neurosurgery, Army Medical Center of PLA, Chongqing, China
| | - Xuetao Shi
- Department of Medical Electronic Engineering, School of Biomedical Engineering, Air Force Medical University of PLA, Xi'an, China
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Ouypornkochagorn T, Polydorides N, McCann H. Towards continuous EIT monitoring for hemorrhagic stroke patients. Front Physiol 2023; 14:1157371. [PMID: 37089433 PMCID: PMC10115159 DOI: 10.3389/fphys.2023.1157371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 03/24/2023] [Indexed: 04/08/2023] Open
Abstract
The practical implementation of continuous monitoring of stroke patients by Electrical Impedance Tomography (EIT) is addressed. In a previous paper, we have demonstrated EIT sensitivity to cerebral hemodynamics, using scalp-mounted electrodes, very low-noise measurements, and a novel image reconstruction method. In the present paper, we investigate the potential to adapt that system for clinical application, by using 50% fewer electrodes and by incorporating into the measurement protocol an additional high-frequency measurement to provide an effective reference. Previously published image reconstruction methods for multi-frequency EIT are substantially improved by exploiting the forward calculations enabled by the detailed head model, particularly to make the referencing method more robust and to attempt to remove the effects of modelling error. Images are presented from simulation of a typical hemorrhagic stroke and its growth. These results are encouraging for exploration of the potential clinical benefit of the methodology in long-term monitoring of hemorrhagic stroke.
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Affiliation(s)
| | - Nick Polydorides
- School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom
| | - Hugh McCann
- School of Engineering, The University of Edinburgh, Edinburgh, United Kingdom
- *Correspondence: Hugh McCann,
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Mrozowski MS, Chalmers IC, Ingleby SJ, Griffin PF, Riis E. Ultra-low noise, bi-polar, programmable current sources. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2023; 94:014701. [PMID: 36725565 DOI: 10.1063/5.0114760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 12/07/2022] [Indexed: 06/18/2023]
Abstract
We present the design process and implementation of fully open-source, ultra-low noise programmable current source systems in two configurations. Although originally designed as coil drivers for Optically Pumped Magnetometers (OPMs), the device specifications make them potentially useful in a range of applications. The devices feature a bi-directional current range of ±10 and ±250 mA on three independent channels with 16-bit resolution. Both devices feature a narrow 1/f noise bandwidth of 1 Hz, enabling magnetic field manipulation for high-performance OPMs. They exhibit a low noise of 146 pA/Hz and 4.1 nA/Hz, which translates to 15 and 16 ppb/Hz noise relative to full scale.
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Affiliation(s)
- M S Mrozowski
- Department of Physics, SUPA, University of Strathclyde, Glasgow G4 0NG, United Kingdom
| | - I C Chalmers
- Department of Physics, SUPA, University of Strathclyde, Glasgow G4 0NG, United Kingdom
| | - S J Ingleby
- Department of Physics, SUPA, University of Strathclyde, Glasgow G4 0NG, United Kingdom
| | - P F Griffin
- Department of Physics, SUPA, University of Strathclyde, Glasgow G4 0NG, United Kingdom
| | - E Riis
- Department of Physics, SUPA, University of Strathclyde, Glasgow G4 0NG, United Kingdom
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12
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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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Hamilton SJ, Muller PA, Isaacson D, Kolehmainen V, Newell J, Rajabi Shishvan O, Saulnier G, Toivanen J. Fast absolute 3D CGO-based electrical impedance tomography on experimental tank data. Physiol Meas 2022; 43:10.1088/1361-6579/aca26b. [PMID: 36374007 PMCID: PMC10028616 DOI: 10.1088/1361-6579/aca26b] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 11/14/2022] [Indexed: 11/16/2022]
Abstract
Objective.To present the first 3D CGO-based absolute EIT reconstructions from experimental tank data.Approach.CGO-based methods for absolute EIT imaging are compared to traditional TV regularized non-linear least squares reconstruction methods. Additional robustness testing is performed by considering incorrect modeling of domain shape.Main Results.The CGO-based methods are fast, and show strong robustness to incorrect domain modeling comparable to classic difference EIT imaging and fewer boundary artefacts than the TV regularized non-linear least squares reference reconstructions.Significance.This work is the first to demonstrate fully 3D CGO-based absolute EIT reconstruction on experimental data and also compares to TV-regularized absolute reconstruction. The speed (1-5 s) and quality of the reconstructions is encouraging for future work in absolute EIT.
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Affiliation(s)
- S J Hamilton
- Department of Mathematical and Statistical Sciences; Marquette University, Milwaukee, WI 53233 United States of America
| | - P A Muller
- Department of Mathematics & Statistics; Villanova University, Villanova, PA 19085 United States of America
| | - D Isaacson
- Department of Mathematics, Rensselaer Polytechnic Institute, Troy, NY 12180, United States of America
| | - V Kolehmainen
- Department of Applied Physics, University of Eastern Finland, FI-70210 Kuopio, Finland
| | - J Newell
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, United States of America
| | - O Rajabi Shishvan
- Department of Electrical and Computer Engineering, University at Albany-SUNY, Albany, NY 12222, United States of America
| | - G Saulnier
- Department of Electrical and Computer Engineering, University at Albany-SUNY, Albany, NY 12222, United States of America
| | - J Toivanen
- Department of Applied Physics, University of Eastern Finland, FI-70210 Kuopio, Finland
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14
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Abdelwahed M, Zerioul L, Pitti A, Romain O. Using Novel Multi-Frequency Analysis Methods to Retrieve Material and Temperature Information in Tactile Sensing Areas. SENSORS (BASEL, SWITZERLAND) 2022; 22:8876. [PMID: 36433473 PMCID: PMC9693584 DOI: 10.3390/s22228876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 10/19/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
This article presents a novel artificial skin technology based on the Electric Impedance Tomography (EIT) that employs multi-frequency currents for detecting the material and the temperature of objects in contact with piezoresistive sheets. To date, few artificial skins in the literature are capable of detecting an object's material, e.g., wood, skin, leather, or plastic. EIT-based artificial skins have been employed mostly to detect the position of the contact but not its characteristics. Thanks to multi-frequency currents, our EIT-based artificial skin is capable of characterising the spectral profile of objects in contact and identifying an object's material at ambient temperature. Moreover, our model is capable of detecting several levels of temperature (from -10 up to 60 °C) and can also maintain a certain accuracy for material identification. In addition to the known capabilities of EIT-based artificial skins concerning detecting pressure and location of objects, as well as being low cost, these two novel modalities demonstrate the potential of EIT-based artificial skins to achieve global tactile sensing.
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Affiliation(s)
- Mehdi Abdelwahed
- ETIS, CY Cergy Paris University, ENSEA, CNRS UMR 8051, 95000 Cergy, France
- Institut VEDECOM, 78000 Versailles, France
| | - Lounis Zerioul
- ETIS, CY Cergy Paris University, ENSEA, CNRS UMR 8051, 95000 Cergy, France
| | - Alexandre Pitti
- ETIS, CY Cergy Paris University, ENSEA, CNRS UMR 8051, 95000 Cergy, France
| | - Olivier Romain
- ETIS, CY Cergy Paris University, ENSEA, CNRS UMR 8051, 95000 Cergy, France
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15
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Chen Y, Dong F, Tan C. Space-constrained optimized Tikhonov regularization method for 3D hemorrhage reconstruction by open magnetic induction tomography. Phys Med Biol 2022; 67. [PMID: 36317273 DOI: 10.1088/1361-6560/ac9e42] [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: 06/27/2022] [Accepted: 10/27/2022] [Indexed: 11/17/2022]
Abstract
Objective. Open magnetic induction tomography (MIT) is a promising technique for detecting the intracranial hemorrhage due to the non-radioactive, non-invasive and portable features. However, severe inhomogeneity of the sensitivity distribution under the open MIT sensor array and the ill-conditioned nature of MIT inverse problem limit the imaging quality in hemorrhage reconstruction. More accurate and robust imaging algorithms are urgently needed in clinical diagnosis.Approach.In this study, the space-constrained optimized Tikhonov regularization (SOTR) method is proposed for 3D hemorrhage reconstruction by open MIT. The sensitivity matrix is optimized according to the characteristics of sensitivity distribution under the open MIT sensor array. To test the performance of the SOTR method, 3D anatomical head models with hemorrhages in different volumes and locations were established. The images of the hemorrhages were reconstructed by the Tikhonov regularization (TR), total variation (TV) regularization, isotropic SOTR, and anisotropic SOTR method. Correlation coefficientCC,localization errorLE,and volume errorVEwere calculated to evaluate the hemorrhage imaging quality. Mainresults. Compared with the traditional sensitivity matrix, the optimized sensitivity matrix has smaller column number and better uniformity, which alleviates the under-determined and ill-conditioned problem of MIT. The imaging results indicate that both the isotropic and anisotropic SOTR methods can effectively improve the reconstruction accuracy for the location and volume of the hemorrhages. Moreover, compared with the TR and TV methods, the two SOTR methods are more robust against the measurement noise.Significance. The proposed method improves the imaging quality of the intracranial hemorrhage, which promotes the clinical applications of open MIT.
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Affiliation(s)
- Yixuan Chen
- Tianjin Key Laboratory of Process Measurement and Control, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Feng Dong
- Tianjin Key Laboratory of Process Measurement and Control, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China
| | - Chao Tan
- Tianjin Key Laboratory of Process Measurement and Control, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, People's Republic of China
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16
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Patil S, Rossi R, Jabrah D, Doyle K. Detection, Diagnosis and Treatment of Acute Ischemic Stroke: Current and Future Perspectives. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:748949. [PMID: 35813155 PMCID: PMC9263220 DOI: 10.3389/fmedt.2022.748949] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 06/02/2022] [Indexed: 11/30/2022] Open
Abstract
Stroke is one of the leading causes of disability worldwide. Early diagnosis and treatment of stroke are important for better clinical outcome. Rapid and accurate diagnosis of stroke subtypes is critical. This review discusses the advantages and disadvantages of the current diagnostic and assessment techniques used in clinical practice, particularly for diagnosing acute ischemic stroke. Alternative techniques for rapid detection of stroke utilizing blood based biomarkers and novel portable devices employing imaging methods such as volumetric impedance phase-shift spectroscopy, microwave tomography and Doppler ultrasound are also discussed. Current therapeutic approaches for treating acute ischemic stroke using thrombolytic drugs and endovascular thrombectomy are discussed, with a focus on devices and approaches recently developed to treat large cranial vessel occlusions.
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Affiliation(s)
- Smita Patil
- CÚRAM, SFI Research Centre for Medical Devices, National University of Ireland Galway, Galway, Ireland
- Department of Physiology, National University of Ireland Galway, Galway, Ireland
| | - Rosanna Rossi
- CÚRAM, SFI Research Centre for Medical Devices, National University of Ireland Galway, Galway, Ireland
- Department of Physiology, National University of Ireland Galway, Galway, Ireland
| | - Duaa Jabrah
- Department of Physiology, National University of Ireland Galway, Galway, Ireland
| | - Karen Doyle
- CÚRAM, SFI Research Centre for Medical Devices, National University of Ireland Galway, Galway, Ireland
- Department of Physiology, National University of Ireland Galway, Galway, Ireland
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17
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Paldanius A, Dekdouk B, Toivanen J, Kolehmainen V, Hyttinen J. Sensitivity Analysis Highlights the Importance of Accurate Head Models for Electrical Impedance Tomography Monitoring of Intracerebral Hemorrhagic Stroke. IEEE Trans Biomed Eng 2022; 69:1491-1501. [PMID: 34665718 DOI: 10.1109/tbme.2021.3120929] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Electrical impedance tomography (EIT) has been proposed as a novel tool for diagnosing stroke. However, so far, the clinical feasibility is unresolved. In this study, we aim to investigate the need for accurate head modeling in EIT and how the inhomogeneities of the head contribute to the EIT measurement and affect its feasibility in monitoring the progression of a hemorrhagic stroke. METHODS We compared anatomically detailed six- and three-layer finite element models of a human head and computed the resulting scalp electrode potentials and the lead fields of selected electrode configurations. We visualized the resulting EIT measurement sensitivity distributions, computed the scalp electrode potentials, and examined the inverse imaging with selected cases. The effect of accurate tissue geometry and conductivity values on the EIT measurement is assessed with multiple different hemorrhagic perturbation locations and sizes. RESULTS Our results show that accurate tissue geometries and conductivity values inside the cranial cavity, especially the highly conductive cerebrospinal fluid, significantly affect EIT measurement sensitivity distribution and measured potentials. CONCLUSIONS We can conclude that the three-layer head models commonly used in EIT literature cannot depict the current paths correctly in the head. Thus, our study highlights the need to consider the detailed geometry of the cerebrospinal fluid (CSF) in EIT. SIGNIFICANCE The results clearly show that the CSF should be considered in the head EIT calculations.
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18
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Ouypornkochagorn T, Terzija N, Wright P, Davidson JL, Polydorides N, McCann H. Scalp-Mounted Electrical Impedance Tomography of Cerebral Hemodynamics. IEEE SENSORS JOURNAL 2022; 22:4569-4580. [PMID: 35673527 PMCID: PMC9093315 DOI: 10.1109/jsen.2022.3145587] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 06/15/2023]
Abstract
An Electrical Impedance Tomography (EIT) system has been developed for dynamic three-dimensional imaging of changes in conductivity distribution in the human head, using scalp-mounted electrodes. We attribute these images to changes in cerebral perfusion. At 100 frames per second (fps), voltage measurement is achieved with full-scale signal-to-noise ratio of 105 dB and common-mode rejection ratio > 90 dB. A novel nonlinear method is presented for 3-D imaging of the difference in conductivity distribution in the head, relative to a reference time. The method achieves much reduced modelling error. It successfully localizes conductivity inclusions in experimental and simulation tests, where previous methods fail. For > 50 human volunteers, the rheoencephalography (REG) waveform is observed in EIT voltage measurements for every volunteer, with peak-to-peak amplitudes up to approx. 50 μVrms. Images are presented of the change in conductivity distribution during the REG/cardiac cycle, at 50 fps, showing maximum local conductivity change of approx. 1% in grey/white matter. A total of 17 tests were performed during short (typically 5s) carotid artery occlusions on 5 volunteers, monitored by Transcranial Doppler ultrasound. From EIT measurements averaged over complete REG/cardiac cycles, 13 occlusion tests showed consistently decreased conductivity of cerebral regions on the occluded side, and increased conductivity on the opposite side. The maximum local conductivity change during occlusion was approx. 20%. The simplicity of the carotid artery intervention provides a striking validation of the scalp-mounted measurement system in imaging cerebral hemodynamics, and the REG images indicate its unique combination of sensitivity and temporal resolution.
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Affiliation(s)
| | | | - Paul Wright
- Department of Electrical and Electronic EngineeringThe University of ManchesterManchesterM13 9PLU.K.
| | - John L. Davidson
- Department of Electrical and Electronic EngineeringThe University of ManchesterManchesterM13 9PLU.K.
| | - Nick Polydorides
- School of EngineeringThe University of EdinburghEdinburghEH9 3JLU.K.
| | - Hugh McCann
- School of EngineeringThe University of EdinburghEdinburghEH9 3JLU.K.
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19
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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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20
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Abboud T, Mielke D, Rohde V. Mini Review: Impedance Measurement in Neuroscience and Its Prospective Application in the Field of Surgical Neurooncology. Front Neurol 2022; 12:825012. [PMID: 35111132 PMCID: PMC8801870 DOI: 10.3389/fneur.2021.825012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 12/27/2021] [Indexed: 11/13/2022] Open
Abstract
Impedance measurement of human tissue can be performed either in vivo or ex vivo. The majority of the in-vivo approaches are non-invasive, and few are invasive. To date, there is no gold standard for impedance measurement of intracranial tissue. In addition, most of the techniques addressing this topic are still experimental and have not found their way into clinical practice. This review covers available impedance measurement approaches in the neuroscience in general and specifically addresses recent advances made in the application of impedance measurement in the field of surgical neurooncology. It will provide an understandable picture on impedance measurement and give an overview of limitations that currently hinders clinical application and require future technical and conceptual solutions.
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21
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Moura FS, Beraldo RG, Ferreira LA, Siltanen S. Anatomical atlas of the upper part of the human head for electroencephalography and bioimpedance applications. Physiol Meas 2021; 42. [PMID: 34673557 DOI: 10.1088/1361-6579/ac3218] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 10/21/2021] [Indexed: 11/11/2022]
Abstract
Objective.The objective of this work is to develop a 4D (3D+T) statistical anatomical atlas of the electrical properties of the upper part of the human head for cerebral electrophysiology and bioimpedance applications.Approach.The atlas was constructed based on 3D magnetic resonance images (MRI) of 107 human individuals and comprises the electrical properties of the main internal structures and can be adjusted for specific electrical frequencies. T1w+T2w MRI images were used to segment the main structures of the head while angiography MRI was used to segment the main arteries. The proposed atlas also comprises a time-varying model of arterial brain circulation, based on the solution of the Navier-Stokes equation in the main arteries and their vascular territories.Main results.High-resolution, multi-frequency and time-varying anatomical atlases of resistivity, conductivity and relative permittivity were created and evaluated using a forward problem solver for EIT. The atlas was successfully used to simulate electrical impedance tomography measurements indicating the necessity of signal-to-noise between 100 and 125 dB to identify vascular changes due to the cardiac cycle, corroborating previous studies. The source code of the atlas and solver are freely available to download.Significance.Volume conductor problems in cerebral electrophysiology and bioimpedance do not have analytical solutions for nontrivial geometries and require a 3D model of the head and its electrical properties for solving the associated PDEs numerically. Ideally, the model should be made with patient-specific information. In clinical practice, this is not always the case and an average head model is often used. Also, the electrical properties of the tissues might not be completely known due to natural variability. Anatomical atlases are important tools forin silicostudies on cerebral circulation and electrophysiology that require statistically consistent data, e.g. machine learning, sensitivity analyses, and as a benchmark to test inverse problem solvers.
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Affiliation(s)
- Fernando S Moura
- Engineering, modelling and Applied Social Sciences Center, Federal University of ABC São Bernardo do Campo, São Paulo, Brazil.,Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Roberto G Beraldo
- Engineering, modelling and Applied Social Sciences Center, Federal University of ABC São Bernardo do Campo, São Paulo, Brazil
| | - Leonardo A Ferreira
- Engineering, modelling and Applied Social Sciences Center, Federal University of ABC São Bernardo do Campo, São Paulo, Brazil
| | - Samuli Siltanen
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
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22
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Hamilton SJ, Isaacson D, Kolehmainen V, Muller PA, Toivanen J, Bray PF. 3D ELECTRICAL IMPEDANCE TOMOGRAPHY RECONSTRUCTIONS FROM SIMULATED ELECTRODE DATA USING DIRECT INVERSION t exp AND CALDERÓN METHODS. INVERSE PROBLEMS AND IMAGING (SPRINGFIELD, MO.) 2021; 15:1135-1169. [PMID: 35173824 PMCID: PMC8846426 DOI: 10.3934/ipi.2021032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The first numerical implementation of a t exp method in 3D using simulated electrode data is presented. Results are compared to Calderón's method as well as more common TV and smoothness regularization-based methods. The t exp method for EIT is based on tailor-made non-linear Fourier transforms involving the measured current and voltage data. Low-pass filtering in the non-linear Fourier domain is used to stabilize the reconstruction process. In 2D, t exp methods have shown great promise for providing robust real-time absolute and time-difference conductivity reconstructions but have yet to be used on practical electrode data in 3D, until now. Results are presented for simulated data for conductivity and permittivity with disjoint non-radially symmetric targets on spherical domains and noisy voltage data. The 3D t exp and Calderón methods are demonstrated to provide comparable quality to their 2D counterparts, and hold promise for real-time reconstructions due to their fast, non-optimized, computational cost.
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Affiliation(s)
- S J Hamilton
- Department of Mathematical and Statistical Sciences; Marquette University, Milwaukee, WI 53233 USA
| | - D Isaacson
- Department of Mathematical Sciences, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA
| | - V Kolehmainen
- Department of Applied Physics, University of Eastern Finland, FI-70210 Kuopio, Finland
| | - P A Muller
- Department of Mathematics & Statistics; Villanova University, Villanova, PA 19085 USA
| | - J Toivanen
- Department of Applied Physics, University of Eastern Finland, FI-70210 Kuopio, Finland
| | - P F Bray
- Department of Mathematics; Drexel University, Philadelphia, PA 19104 USA
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23
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Li S, Hua X, Zheng M, Wu J, Ma Z, Xing X, Ma J, Zhang J, Shan C, Xu J. PLXNA2 knockdown promotes M2 microglia polarization through mTOR/STAT3 signaling to improve functional recovery in rats after cerebral ischemia/reperfusion injury. Exp Neurol 2021; 346:113854. [PMID: 34474008 DOI: 10.1016/j.expneurol.2021.113854] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/08/2021] [Accepted: 08/26/2021] [Indexed: 01/04/2023]
Abstract
Ischemic stroke is an acute cerebrovascular disease characterized by high mortality, morbidity and disability rates. Ischemia/reperfusion is a critical pathophysiological basis of motor and cognitive dysfunction caused by ischemic stroke. Microglia, innate immune cells of the central nervous system, mediate the neuroinflammatory response to ischemia/reperfusion. PlexinA2 (PLXNA2) plays an important role in the regulation of neuronal axon guidance, the immune response and angiogenesis. However, it is not clear whether PLXNA2 regulates microglia polarization in ischemic stroke or the underlying mechanism. In the present study, we investigated the role of PLXNA2 in rats with middle cerebral artery occlusion/reperfusion (MCAO/R) and BV2 microglia cells with oxygen and glucose deprivation/reoxygenation (OGD/R). A battery of behavioral tests, including the beam balance test, forelimb placement test, foot fault test, cylinder test, CatWalk gait analysis and Morris water maze test were performed to evaluate sensorimotor function, locomotor activity and cognitive ability. The expression of M1/M2-specific markers in the ischemic penumbra and BV2 microglia cells was detected using immunofluorescence staining, quantitative real-time PCR analysis and Western blot analysis. Our study showed that PLXNA2 knockdown accelerated the recovery of motor function and cognitive ability after MCAO/R. In addition, PLXNA2 knockdown restrained proinflammatory cytokine release and promoted anti-inflammatory cytokine release, and the mammalian target of rapamycin (mTOR)/signal transducer and activator of transcription 3 (STAT3) pathway was involved in PLXNA2 regulated microglia polarization. Taken together, our results indicate that PLXNA2 knockdown reduces neuroinflammation by switching the microglia phenotype from M1 to M2 in the ischemic penumbra of MCAO/R-injured rats, which may be due to the inhibition of mTOR/STAT3 signaling. Treatments targeting PLXNA2 may be a promising therapeutic strategy for ischemic stroke.
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Affiliation(s)
- Sisi Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Xuyun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Mouxiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Jiajia Wu
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China
| | - Zhenzhen Ma
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Xiangxin Xing
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jie Ma
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Junpeng Zhang
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Chunlei Shan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai 201203, China
| | - Jianguang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai 201203, China.
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24
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Yang L, Dai M, Cao Q, Ding S, Zhao Z, Cao X, Wen Z, Wang H, Xie M, Fu F. Real-time monitoring hypoxia at high altitudes using electrical bioimpedance technique: an animal experiment. J Appl Physiol (1985) 2021; 130:952-963. [PMID: 33270508 DOI: 10.1152/japplphysiol.00712.2020] [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] [Indexed: 11/22/2022] Open
Abstract
Hypoxia poses a serious threat to pilots. The aim of this study was to examine the efficacy of electrical bioimpedance (EBI) in detecting the onset of hypoxia in real time in a rabbit hypoxia model. Thirty-two New Zealand rabbits were divided equally into four groups (control group and three hypoxia groups, i.e., mild, moderate, and severe). Hypoxia was induced by simulating various altitudes in the hypobaric oxygen chamber (3,000 m, 5,000 m, and 8,000 m). Both cerebral impedance and blood oxygen (SpO2) were monitored continuously. Results showed that the cerebral impedance increased immediately during the period of increasing altitude and decreased quickly to the initial baseline at the phase of descending altitude. Moreover, the change of cerebral impedance in the mild hypoxia group (3,000 m) was significantly smaller than those in the other two groups (5,000 m and 8,000 m, P < 0.05). The changes in cerebral impedance and SpO2 were significantly correlated based on the total of measurement data (r2 = 0.628, P < 0.001). Furthermore, the agreement analysis performed with Bland-Altman and standardized residual plots exhibited high concordance between cerebral impedance and SpO2. Receiver operator characteristic analysis manifested that the sensitivity, specificity, and area under the curve using cerebral impedance for changes in SpO2 >10% were 0.735, 0.826, and 0.845, respectively. These findings demonstrated that EBI could sensitively and accurately monitor changes of cerebral impedance induced by hypoxia, which might provide a potential tool for the real-time and noninvasive monitoring of hypoxic condition of pilots in flight for early identification of hypoxia.NEW & NOTEWORTHY This study is the first to examine the efficacy of electrical bioimpedance (EBI) in detecting the onset of high-altitude hypoxia in real time. The novelty of this research includes three aspects. First, the cerebral impedance of rabbits increased immediately during the rising of altitude and decreased quickly to the initial baseline at the phase of descending altitude. Second, there was a significant correlation and high concordance between cerebral impedance and SpO2. Third, cerebral impedance could determine the change of SpO2 resulting from hypoxia.
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Affiliation(s)
- Lin Yang
- Department of Aerospace Medicine, Fourth Military Medical University, Xi'an, China
| | - Meng Dai
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Qinglin Cao
- Department of Aerospace Medicine, Fourth Military Medical University, Xi'an, China
| | - Shuai Ding
- School of Preclinical Medicine, Fourth Military Medical University, Xi'an, China
| | - Zhanqi Zhao
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China.,Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Xinsheng Cao
- Department of Aerospace Medicine, Fourth Military Medical University, Xi'an, China
| | - Zhihong Wen
- Department of Aerospace Medicine, Fourth Military Medical University, Xi'an, China
| | - Hang Wang
- Department of Aerospace Medicine, Fourth Military Medical University, Xi'an, China
| | - Manjiang Xie
- Department of Aerospace Medicine, Fourth Military Medical University, Xi'an, China
| | - Feng Fu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
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Self-Abrading Servo Electrode Helmet for Electrical Impedance Tomography. SENSORS 2020; 20:s20247058. [PMID: 33317181 PMCID: PMC7763319 DOI: 10.3390/s20247058] [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: 11/16/2020] [Revised: 12/02/2020] [Accepted: 12/04/2020] [Indexed: 11/17/2022]
Abstract
Electrical Impedance Tomography (EIT) is a medical imaging technique which has the potential to reduce time to treatment in acute stroke by rapidly differentiating between ischaemic and haemorrhagic stroke. The potential of these methods has been demonstrated in simulation and phantoms, it has not yet successfully translated to clinical studies, due to high sensitivity to errors in scalp electrode mislocation and poor electrode-skin contact. To overcome these limitations, a novel electrode helmet was designed, bearing 32 independently controlled self-abrading electrodes. The contact impedance was reduced through rotation on an abrasive electrode on the scalp using a combined impedance, rotation and position feedback loop. Potentiometers within each unit measure the electrode tip displacement within 0.1 mm from the rigid helmet body. Characterisation experiments on a large-scale test rig demonstrated that approximately 20 kPa applied pressure and 5 rotations was necessary to achieve the target 5 kΩ contact impedance at 20 Hz. This performance was then replicated in a simplified self-contained unit where spring loaded electrodes are rotated by servo motors. Finally, a 32-channel helmet and controller which sequentially minimised contact impedance and simultaneously located each electrode was built which reduced the electrode application and localisation time to less than five minutes. The results demonstrated the potential of this approach to rapidly apply electrodes in an acute setting, removing a significant barrier for imaging acute stroke with EIT.
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Analog Realization of Fractional-Order Skin-Electrode Model for Tetrapolar Bio-Impedance Measurements. TECHNOLOGIES 2020. [DOI: 10.3390/technologies8040061] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This work compares two design methodologies, emulating both AgCl electrode and skin tissue Cole models for testing and verification of electrical bio-impedance circuits and systems. The models are based on fractional-order elements, are implemented with active components, and capture bio-impedance behaviors up to 10 kHz. Contrary to passive-elements realizations, both architectures using analog filters coupled with adjustable transconductors offer tunability of the fractional capacitors’ parameters. The main objective is to build a tunable active integrated circuitry block that is able to approximate the models’ behavior and can be utilized as a Subject Under Test (SUT) and electrode equivalent in bio-impedance measurement applications. A tetrapolar impedance setup, typical in bio-impedance measurements, is used to demonstrate the performance and accuracy of the presented architectures via Spectre Monte-Carlo simulation. Circuit and post-layout simulations are carried out in 90-nm CMOS process, using the Cadence IC suite.
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Padilha Leitzke J, Zangl H. A Review on Electrical Impedance Tomography Spectroscopy. SENSORS 2020; 20:s20185160. [PMID: 32927685 PMCID: PMC7571205 DOI: 10.3390/s20185160] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 09/04/2020] [Accepted: 09/05/2020] [Indexed: 11/24/2022]
Abstract
Electrical Impedance Tomography Spectroscopy (EITS) enables the reconstruction of material distributions inside an object based on the frequency-dependent characteristics of different substances. In this paper, we present a review of EITS focusing on physical principles of the technology, sensor geometries, existing measurement systems, reconstruction algorithms, and image representation methods. In addition, a novel imaging method is proposed which could fill some of the gaps found in the literature. As an example of an application, EITS of ice and water mixtures is used.
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McDermott B, Elahi A, Santorelli A, O'Halloran M, Avery J, Porter E. Multi-frequency symmetry difference electrical impedance tomography with machine learning for human stroke diagnosis. Physiol Meas 2020; 41:075010. [PMID: 32554876 DOI: 10.1088/1361-6579/ab9e54] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Multi-frequency symmetry difference electrical impedance tomography (MFSD-EIT) can robustly detect and identify unilateral perturbations in symmetric scenes. Here, an investigation is performed to assess if the algorithm can be successfully applied to identify the aetiology of stroke with the aid of machine learning. METHODS Anatomically realistic four-layer finite element method models of the head based on stroke patient images are developed and used to generate EIT data over a 5 Hz-100 Hz frequency range with and without bleed and clot lesions present. Reconstruction generates conductivity maps of each head at each frequency. Application of a quantitative metric assessing changes in symmetry across the sagittal plane of the reconstructed image and over the frequency range allows lesion detection and identification. The algorithm is applied to both simulated and human (n = 34 subjects) data. A classification algorithm is applied to the metric value in order to differentiate between normal, haemorrhage and clot values. MAIN RESULTS An average accuracy of 85% is achieved when MFSD-EIT with support vector machines (SVM) classification is used to identify and differentiate bleed from clot in human data, with 77% accuracy when differentiating normal from stroke in human data. CONCLUSION Applying a classification algorithm to metrics derived from MFSD-EIT images is a novel and promising technique for detection and identification of perturbations in static scenes. SIGNIFICANCE The MFSD-EIT algorithm used with machine learning gives promising results of lesion detection and identification in challenging conditions like stroke. The results imply feasible translation to human patients.
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Affiliation(s)
- Barry McDermott
- Translational Medical Device Lab, National University of Ireland, Galway, Ireland
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Abstract
A wide range of medical devices have significant electronic components. Compared to open-source medical software, open (and open-source) electronic hardware has been less published in peer-reviewed literature. In this review, we explore the developments, significance, and advantages of using open platform electronic hardware for medical devices. Open hardware electronics platforms offer not just shorter development times, reduced costs, and customization; they also offer a key potential advantage which current commercial medical devices lack—seamless data sharing for machine learning and artificial intelligence. We explore how various electronic platforms such as microcontrollers, single board computers, field programmable gate arrays, development boards, and integrated circuits have been used by researchers to design medical devices. Researchers interested in designing low cost, customizable, and innovative medical devices can find references to various easily available electronic components as well as design methodologies to integrate those components for a successful design.
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Zhang G, Li W, Ma H, Liu X, Dai M, Xu C, Li H, Dong X, Sun X, Fu F. An on-line processing strategy for head movement interferences removal of dynamic brain electrical impedance tomography based on wavelet decomposition. Biomed Eng Online 2019; 18:55. [PMID: 31072348 PMCID: PMC6509801 DOI: 10.1186/s12938-019-0668-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 04/04/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Head movement interferences are a common problem during prolonged dynamic brain electrical impedance tomography (EIT) clinical monitoring. Head movement interferences mainly originate from body movements of patients and nursing procedures performed by medical staff, etc. These body movements will lead to variation in boundary voltage signals, which affects image reconstruction. METHODS This study employed a data preprocessing method based on wavelet decomposition to inhibit head movement interferences in brain EIT data. Mixed Gaussian models were applied to describe the distribution characteristics of brain EIT data. We identified head movement signal through the differences in distribution characteristics of corresponding wavelet decomposition coefficients between head movement artifacts and normal signals, and then managed the contaminated data with improved on-line wavelet processing methods. RESULTS To validate the efficacy of the method, simulated signal experiments and human data experiments were performed. In the simulation experiment, the simulated movement artifact was significantly reduced and data quality was improved with indicators' increase in PRD and correlation coefficient. Human data experiments demonstrated that this method effectively suppressed head movement in signals and reduce artifacts resulting from head movement artifacts in images. CONCLUSION In this paper, we proposed an on-line strategy to manage the head movement interferences from the brain EIT data based on the distribution characteristics of wavelet coefficients. Our strategy is capable of reducing the movement interference in the data and improving the reconstructed images. This work would improve the clinical practicability of brain EIT and contribute to its further promotion.
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Affiliation(s)
- Ge Zhang
- Department of Radiology, Bethune International Peace Hospital, Shijiazhuang, China.,Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Weichen Li
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Hang Ma
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Xuechao Liu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Meng Dai
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Canhua Xu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Haoting Li
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Xiuzhen Dong
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Xingwang Sun
- Department of Radiology, Bethune International Peace Hospital, Shijiazhuang, China.
| | - Feng Fu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
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McDermott B, Avery J, O'Halloran M, Aristovich K, Porter E. Bi-frequency symmetry difference electrical impedance tomography-a novel technique for perturbation detection in static scenes. Physiol Meas 2019; 40:044005. [PMID: 30786267 DOI: 10.1088/1361-6579/ab08ba] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE A novel method for the imaging of static scenes using electrical impedance tomography (EIT) is reported with implementation and validation using numerical and phantom models. The technique is applicable to regions featuring symmetry in the normal case, asymmetry in the presence of a perturbation, and where there is a known, frequency-dependent change in the electrical conductivity of the materials in the region. APPROACH The stroke diagnostic problem is used as a motivating sample application. The head is largely symmetrical across the sagittal plane. A haemorrhagic or ischaemic lesion located away from the sagittal plane will alter this natural symmetry, resulting in a symmetrical imbalance that can be detected using EIT. Specifically, application of EIT stimulation and measurement protocols at two distinct frequencies detects deviations in symmetry if an asymmetrically positioned lesion is present, with subsequent identification and localisation of the perturbation based on known frequency-dependent conductivity changes. Anatomically accurate computational models are used to demonstrate the feasibility of the proposed technique using different types, sizes, and locations of lesions with frequency-dependent (or independent) conductivity. Further, a realistic experimental head phantom is used to validate the technique using frequency-dependent perturbations emulating the key numerical simulations. MAIN RESULTS Lesion presence, type, and location are detectable using this novel technique. Results are presented in the form of images and corresponding robust quantitative metrics. Better detection is achieved for larger lesions, those further from the sagittal plane, and when measurements have a higher signal-to-noise ratio. SIGNIFICANCE Bi-frequency symmetry difference EIT is an exciting new modality of EIT with the ability to detect deviations in the symmetry of a region that occur due to the presence of a lesion. Notably, this modality does not require a time change in the region and thus may be used in static scenarios such as stroke detection.
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Affiliation(s)
- Barry McDermott
- Translational Medical Device Lab, National University of Ireland Galway, Galway, Ireland
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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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Optimal combination of electrodes and conductive gels for brain electrical impedance tomography. Biomed Eng Online 2018; 17:186. [PMID: 30572888 PMCID: PMC6302411 DOI: 10.1186/s12938-018-0617-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 12/12/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Electrical impedance tomography (EIT) is an emerging imaging technology that has been used to monitor brain injury and detect acute stroke. The time and frequency properties of electrode-skin contact impedance are important for brain EIT because brain EIT measurement is performed over a long period when used to monitor brain injury, and is carried out across a wide range of frequencies when used to detect stroke. To our knowledge, no study has simultaneously investigated the time and frequency properties of both electrode and conductive gel for brain EIT. METHODS In this study, the contact impedance of 16 combinations consisting of 4 kinds of clinical electrode and five types of commonly used conductive gel was measured on ten volunteers' scalp for a period of 1 h at frequencies from 100 Hz to 1 MHz using the two-electrode method. And then the performance of each combination was systematically evaluated in terms of the magnitude of contact impedance, and changes in contact impedance with time and frequency. RESULTS Results showed that combination of Ag+/Ag+Cl- powder electrode and low viscosity conductive gel performed best overall (Ten 20® in this study); it had a relatively low magnitude of contact impedance and superior performance regarding contact impedance with time (p < 0.05) and frequency (p < 0.05). CONCLUSIONS Experimental results indicates that the combination of Ag+/Ag+Cl- powder electrode and low viscosity conductive gel may be the best choice for brain EIT.
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