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Aggas JR, Abasi S, Ton C, Salehi S, Liu R, Brandacher G, Grayson WL, Guiseppi-Elie A. Real-Time Monitoring Using Multiplexed Multi-Electrode Bioelectrical Impedance Spectroscopy for the Stratification of Vascularized Composite Allografts: A Perspective on Predictive Analytics. Bioengineering (Basel) 2023; 10:bioengineering10040434. [PMID: 37106621 PMCID: PMC10135882 DOI: 10.3390/bioengineering10040434] [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: 02/06/2023] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 04/29/2023] Open
Abstract
Vascularized composite allotransplantation addresses injuries to complex anatomical structures such as the face, hand, and abdominal wall. Prolonged static cold storage of vascularized composite allografts (VCA) incurs damage and imposes transportation limits to their viability and availability. Tissue ischemia, the major clinical indication, is strongly correlated with negative transplantation outcomes. Machine perfusion and normothermia can extend preservation times. This perspective introduces multiplexed multi-electrode bioimpedance spectroscopy (MMBIS), an established bioanalytical method to quantify the interaction of the electrical current with tissue components, capable of measuring tissue edema, as a quantitative, noninvasive, real-time, continuous monitoring technique to provide crucially needed assessment of graft preservation efficacy and viability. MMBIS must be developed, and appropriate models explored to address the highly complex multi-tissue structures and time-temperature changes of VCA. Combined with artificial intelligence (AI), MMBIS can serve to stratify allografts for improvement in transplantation outcomes.
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Affiliation(s)
- John R Aggas
- Bioelectronics, Biosensors and Biochips (C3B®), Department of Biomedical Engineering, Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
- Test Development, Roche Diagnostics, 9115 Hague Road, Indianapolis, IN 46256, USA
| | - Sara Abasi
- Bioelectronics, Biosensors and Biochips (C3B®), Department of Biomedical Engineering, Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
- Media and Metabolism, Wildtype, Inc., 2325 3rd St., San Francisco, CA 94107, USA
| | - Carolyn Ton
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21231, USA
- Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Sara Salehi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21231, USA
- Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Renee Liu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21231, USA
- Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Gerald Brandacher
- Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD 21231, USA
- Department of Plastic & Reconstructive Surgery, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Warren L Grayson
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21231, USA
- Translational Tissue Engineering Center, Johns Hopkins University, Baltimore, MD 21231, USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Nanobiotechnology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Anthony Guiseppi-Elie
- Bioelectronics, Biosensors and Biochips (C3B®), Department of Biomedical Engineering, Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
- Department of Cardiovascular Sciences, Houston Methodist Institute for Academic Medicine and Houston Methodist Research Institute, 6670 Bertner Ave., Houston, TX 77030, USA
- ABTECH Scientific, Inc., Biotechnology Research Park, 800 East Leigh Street, Richmond, VA 23219, USA
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Xiang J, Dong Y, Yang Y. Multi-Frequency Electromagnetic Tomography for Acute Stroke Detection Using Frequency-Constrained Sparse Bayesian Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:4102-4112. [PMID: 32746151 DOI: 10.1109/tmi.2020.3013100] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Imaging the bio-impedance distribution of the brain can provide initial diagnosis of acute stroke. This paper presents a compact and non-radiative tomographic modality, i.e. multi-frequency Electromagnetic Tomography (mfEMT), for the initial diagnosis of acute stroke. The mfEMT system consists of 12 channels of gradiometer coils with adjustable sensitivity and excitation frequency. To solve the image reconstruction problem of mfEMT, we propose an enhanced Frequency-Constrained Sparse Bayesian Learning (FC-SBL) to simultaneously reconstruct the conductivity distribution at all frequencies. Based on the Multiple Measurement Vector (MMV) model in the Sparse Bayesian Learning (SBL) framework, FC-SBL can recover the underlying distribution pattern of conductivity among multiple images by exploiting the frequency constraint information. A realistic 3D head model was established to simulate stroke detection scenarios, showing the capability of mfEMT to penetrate the highly resistive skull and improved image quality with FC-SBL. Both simulations and experiments showed that the proposed FC-SBL method is robust to noisy data for image reconstruction problems of mfEMT compared to the single measurement vector model, which is promising to detect acute strokes in the brain region with enhanced spatial resolution and in a baseline-free manner.
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Abstract
Ordinary projection screen is not sensitive to interaction, it cannot meet the demands of teaching, virtual reality, and other applications. Due to the fact that people always use hands to complete a variety of human–computer interaction, the finger-based interactive projection technology is worth being researched. In this paper, an ordinary monocular camera is used to acquire video frame on projection screen, and the touch signal of finger in frame is used as the input of interactive projection system. Because the differences between spatial frequency of common digital camera and the projection screen is small, the frame obtained from camera will contain moire fringe, which needs to be filtered in image frequency domain. Then the difference between current frame edge and previous frame edge is calculated to obtain moving object edge clues. According to these clues, the most possible contour curve is searched in current frame edge, and the curve is fitted by polynomial approximation method. Its curvature integration is used to match with the curvature integration of finger template curve. After that the fingers in the curve are recognized. Because color information is not needed, this method can be used to recognize gloved fingers. Finally, finger shadow is used to judge whether the finger touches projection screen to complete interactive process. The experiments of writing and collaboratively rotating picture on projector screen show that this method can effectively complete interactive operation with the projection screen and can realize the multi-user operation.
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Affiliation(s)
- Guangqiang Shu
- College of Computer Science, Sichuan University, Chengdu, P. R. China
- College of Computer Science, Sichuan Normal University, Chengdu, P. R. China
| | - Tao Lin
- College of Computer Science, Sichuan University, Chengdu, P. R. China
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de Castro Martins T, Sato AK, de Moura FS, de Camargo EDLB, Silva OL, Santos TBR, Zhao Z, Möeller K, Amato MBP, Mueller JL, Lima RG, de Sales Guerra Tsuzuki M. A Review of Electrical Impedance Tomography in Lung Applications: Theory and Algorithms for Absolute Images. ANNUAL REVIEWS IN CONTROL 2019; 48:442-471. [PMID: 31983885 PMCID: PMC6980523 DOI: 10.1016/j.arcontrol.2019.05.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Electrical Impedance Tomography (EIT) is under fast development, the present paper is a review of some procedures that are contributing to improve spatial resolution and material properties accuracy, admitivitty or impeditivity accuracy. A review of EIT medical applications is presented and they were classified into three broad categories: ARDS patients, obstructive lung diseases and perioperative patients. The use of absolute EIT image may enable the assessment of absolute lung volume, which may significantly improve the clinical acceptance of EIT. The Control Theory, the State Observers more specifically, have a developed theory that can be used for the design and operation of EIT devices. Electrode placement, current injection strategy and electrode electric potential measurements strategy should maximize the number of observable and controllable directions of the state vector space. A non-linear stochastic state observer, the Unscented Kalman Filter, is used directly for the reconstruction of absolute EIT images. Historically, difference images were explored first since they are more stable in the presence of modelling errors. Absolute images require more detailed models of contact impedance, stray capacitance and properly refined finite element mesh where the electric potential gradient is high. Parallelization of the forward program computation is necessary since the solution of the inverse problem often requires frequent solutions of the forward problem. Several reconstruction algorithms benefit by the Bayesian inverse problem approach and the concept of prior information. Anatomic and physiologic information are used to form the prior information. An already tested methodology is presented to build the prior probability density function using an ensemble of CT scans and in vivo impedance measurements. Eight absolute EIT image algorithms are presented.
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Affiliation(s)
| | - André Kubagawa Sato
- Computational Geometry Laboratory, Escola Politécnica da Universidade de São Paulo, Brazil
| | - Fernando Silva de Moura
- Universidade Federal do ABC, Center of Engineering, Modeling and Applied Social Sciences, Brazil
| | | | - Olavo Luppi Silva
- Universidade Federal do ABC, Center of Engineering, Modeling and Applied Social Sciences, Brazil
| | | | - Zhanqi Zhao
- Institute of Technical Medicine, Furtwangen University, Germany
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Knut Möeller
- Institute of Technical Medicine, Furtwangen University, Germany
| | - Marcelo Brito Passos Amato
- Respiratory Intensive Care Unit, Pulmonary Division, Hospital das Clínicas, Universidade de São Paulo, Brazil
| | - Jennifer L Mueller
- Department of Mathematics, and School of Biomedical Engineering, Colorado State University, United States of America
| | - Raul Gonzalez Lima
- Department of Mechanical Engineering, Escola Politécnica da Universidade de São Paulo, Brazil
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Goren N, Avery J, Dowrick T, Mackle E, Witkowska-Wrobel A, Werring D, Holder D. Multi-frequency electrical impedance tomography and neuroimaging data in stroke patients. Sci Data 2018; 5:180112. [PMID: 29969115 PMCID: PMC6029572 DOI: 10.1038/sdata.2018.112] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 04/16/2018] [Indexed: 11/26/2022] Open
Abstract
Electrical Impedance Tomography (EIT) is a non-invasive imaging technique, which has the potential to expedite the differentiation of ischaemic or haemorrhagic stroke, decreasing the time to treatment. Whilst demonstrated in simulation, there are currently no suitable imaging or classification methods which can be successfully applied to human stroke data. Development of these complex methods is hindered by a lack of quality Multi-Frequency EIT (MFEIT) data. To address this, MFEIT data were collected from 23 stroke patients, and 10 healthy volunteers, as part of a clinical trial in collaboration with the Hyper Acute Stroke Unit (HASU) at University College London Hospital (UCLH). Data were collected at 17 frequencies between 5 Hz and 2 kHz, with 31 current injections, yielding 930 measurements at each frequency. This dataset is the most comprehensive of its kind and enables combined analysis of MFEIT, Electroencephalography (EEG) and Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) data in stroke patients, which can form the basis of future research into stroke classification.
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Affiliation(s)
- Nir Goren
- Medical Physics & Biomedical Engineering, University College London, London WC1E 6BT, UK
| | - James Avery
- Medical Physics & Biomedical Engineering, University College London, London WC1E 6BT, UK
| | - Thomas Dowrick
- Medical Physics & Biomedical Engineering, University College London, London WC1E 6BT, UK
| | - Eleanor Mackle
- Medical Physics & Biomedical Engineering, University College London, London WC1E 6BT, UK
| | - Anna Witkowska-Wrobel
- Medical Physics & Biomedical Engineering, University College London, London WC1E 6BT, UK
| | - David Werring
- Stroke Research Centre, Department of Brain repair and Rehabilitation, University College London Institute of Neurology, London WC1N 3BG, UK
| | - David Holder
- Medical Physics & Biomedical Engineering, University College London, London WC1E 6BT, UK
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In Vivo Bioimpedance Spectroscopy Characterization of Healthy, Hemorrhagic and Ischemic Rabbit Brain within 10 Hz-1 MHz. SENSORS 2017; 17:s17040791. [PMID: 28387710 PMCID: PMC5422064 DOI: 10.3390/s17040791] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Revised: 03/30/2017] [Accepted: 04/04/2017] [Indexed: 12/13/2022]
Abstract
Acute stroke is a serious cerebrovascular disease and has been the second leading cause of death worldwide. Conventional diagnostic modalities for stroke, such as CT and MRI, may not be available in emergency settings. Hence, it is imperative to develop a portable tool to diagnose stroke in a timely manner. Since there are differences in impedance spectra between normal, hemorrhagic and ischemic brain tissues, multi-frequency electrical impedance tomography (MFEIT) shows great promise in detecting stroke. Measuring the impedance spectra of healthy, hemorrhagic and ischemic brain in vivo is crucial to the success of MFEIT. To our knowledge, no research has established hemorrhagic and ischemic brain models in the same animal and comprehensively measured the in vivo impedance spectra of healthy, hemorrhagic and ischemic brain within 10 Hz–1 MHz. In this study, the intracerebral hemorrhage and ischemic models were established in rabbits, and then the impedance spectra of healthy, hemorrhagic and ischemic brain were measured in vivo and compared. The results demonstrated that the impedance spectra differed significantly between healthy and stroke-affected brain (i.e., hemorrhagic or ischemic brain). Moreover, the rate of change in brain impedance following hemorrhagic and ischemic stroke with regard to frequency was distinct. These findings further validate the feasibility of using MFEIT to detect stroke and differentiate stroke types, and provide data supporting for future research.
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Avery J, Dowrick T, Faulkner M, Goren N, Holder D. A Versatile and Reproducible Multi-Frequency Electrical Impedance Tomography System. SENSORS (BASEL, SWITZERLAND) 2017; 17:E280. [PMID: 28146122 PMCID: PMC5336119 DOI: 10.3390/s17020280] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Accepted: 01/25/2017] [Indexed: 11/16/2022]
Abstract
A highly versatile Electrical Impedance Tomography (EIT) system, nicknamed the ScouseTom, has been developed. The system allows control over current amplitude, frequency, number of electrodes, injection protocol and data processing. Current is injected using a Keithley 6221 current source, and voltages are recorded with a 24-bit EEG system with minimum bandwidth of 3.2 kHz. Custom PCBs interface with a PC to control the measurement process, electrode addressing and triggering of external stimuli. The performance of the system was characterised using resistor phantoms to represent human scalp recordings, with an SNR of 77.5 dB, stable across a four hour recording and 20 Hz to 20 kHz. In studies of both haeomorrhage using scalp electrodes, and evoked activity using epicortical electrode mats in rats, it was possible to reconstruct images matching established literature at known areas of onset. Data collected using scalp electrode in humans matched known tissue impedance spectra and was stable over frequency. The experimental procedure is software controlled and is readily adaptable to new paradigms. Where possible, commercial or open-source components were used, to minimise the complexity in reproduction. The hardware designs and software for the system have been released under an open source licence, encouraging contributions and allowing for rapid replication.
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Affiliation(s)
- James Avery
- Department Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
| | - Thomas Dowrick
- Department Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
| | - Mayo Faulkner
- Department Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
| | - Nir Goren
- Department Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
| | - David Holder
- Department Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK.
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Yang L, Dai M, Xu C, Zhang G, Li W, Fu F, Shi X, Dong X. The Frequency Spectral Properties of Electrode-Skin Contact Impedance on Human Head and Its Frequency-Dependent Effects on Frequency-Difference EIT in Stroke Detection from 10Hz to 1MHz. PLoS One 2017; 12:e0170563. [PMID: 28107524 PMCID: PMC5249181 DOI: 10.1371/journal.pone.0170563] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Accepted: 01/08/2017] [Indexed: 11/18/2022] Open
Abstract
Frequency-difference electrical impedance tomography (fdEIT) reconstructs frequency-dependent changes of a complex impedance distribution. It has a potential application in acute stroke detection because there are significant differences in impedance spectra between stroke lesions and normal brain tissues. However, fdEIT suffers from the influences of electrode-skin contact impedance since contact impedance varies greatly with frequency. When using fdEIT to detect stroke, it is critical to know the degree of measurement errors or image artifacts caused by contact impedance. To our knowledge, no study has systematically investigated the frequency spectral properties of electrode-skin contact impedance on human head and its frequency-dependent effects on fdEIT used in stroke detection within a wide frequency band (10 Hz-1 MHz). In this study, we first measured and analyzed the frequency spectral properties of electrode-skin contact impedance on 47 human subjects’ heads within 10 Hz-1 MHz. Then, we quantified the frequency-dependent effects of contact impedance on fdEIT in stroke detection in terms of the current distribution beneath the electrodes and the contact impedance imbalance between two measuring electrodes. The results showed that the contact impedance at high frequencies (>100 kHz) significantly changed the current distribution beneath the electrode, leading to nonnegligible errors in boundary voltages and artifacts in reconstructed images. The contact impedance imbalance at low frequencies (<1 kHz) also caused significant measurement errors. We conclude that the contact impedance has critical frequency-dependent influences on fdEIT and further studies on reducing such influences are necessary to improve the application of fdEIT in stroke detection.
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Affiliation(s)
- Lin Yang
- 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
| | - Ge Zhang
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Weichen Li
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Feng Fu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Xuetao Shi
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Xiuzhen Dong
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
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Yang L, Xu C, Dai M, Fu F, Shi X, Dong X. A novel multi-frequency electrical impedance tomography spectral imaging algorithm for early stroke detection. Physiol Meas 2016; 37:2317-2335. [PMID: 27897152 DOI: 10.1088/1361-6579/37/12/2317] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Multi-frequency electrical impedance tomography (MFEIT) reconstructs the image of conductivity inside the human body based on the dependence of tissue conductivity on frequency. As there exist differences in the conductivity over frequency between blood, ischemic cortical tissue and normal cortical tissue, MFEIT has potential application in the detection of acute stroke. However, because the conductivity distribution of the human head is highly inhomogeneous and the conductivities of normal head tissue and stroke lesion tissue both change with frequency, the anomaly and normal head tissues are often mixed together in the reconstructed image, which makes it difficult to discern the anomaly. Here we present a spectral decomposition frequency-difference (SD-FD) imaging algorithm in an attempt to address this issue: firstly, we reconstruct so-called EIT spectral images according to the conductivity spectra of tissues; secondly, we obtain the EIT image of the anomaly from the spectral images by using independent component analysis. The results show that the proposed algorithm is capable of detecting the anomaly in a numerical head phantom, as well as in a realistic human head tank with frequency-dependent and heterogeneous conductivities distribution. The proposed SD-FD algorithm may support MFEIT use for human stroke imaging in the future.
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Orschulik J, Petkau R, Wartzek T, Hochhausen N, Czaplik M, Leonhardt S, Teichmann D. Improved electrode positions for local impedance measurements in the lung-a simulation study. Physiol Meas 2016; 37:2111-2129. [PMID: 27811407 DOI: 10.1088/0967-3334/37/12/2111] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Impedance spectroscopy can be used to analyze the dielectric properties of various materials. In the biomedical domain, it is used as bioimpedance spectroscopy (BIS) to analyze the composition of body tissue. Being a non-invasive, real-time capable technique, it is a promising modality, especially in the field of lung monitoring. Unfortunately, up to now, BIS does not provide any regional lung information as the electrodes are usually placed in hand-to-hand or transthoracic configurations. Even though transthoracic electrode configurations are in general capable of monitoring the lung, no focusing to specific regions is achieved. In order to resolve this issue, we use a finite element model (FEM) of the human body to study the effect of different electrode configurations on measured BIS data. We present evaluation results and show suitable electrode configurations for eight lung regions. We show that, using these optimized configurations, BIS measurements can be focused to desired regions allowing local lung analysis.
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Affiliation(s)
- Jakob Orschulik
- Philips Chair for Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
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