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Ameen AA, Sack A, Pöschel T. TSS-ConvNet for electrical impedance tomography image reconstruction. Physiol Meas 2024; 45:045006. [PMID: 38565126 DOI: 10.1088/1361-6579/ad39c2] [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: 10/15/2023] [Accepted: 04/02/2024] [Indexed: 04/04/2024]
Abstract
Objective.The objective of this study was to propose a novel data-driven method for solving ill-posed inverse problems, particularly in certain conditions such as time-difference electrical impedance tomography for detecting the location and size of bubbles inside a pipe.Approach.We introduced a new layer architecture composed of three paths: spatial, spectral, and truncated spectral paths. The spatial path processes information locally, whereas the spectral and truncated spectral paths provide the network with a global receptive field. This unique architecture helps eliminate the ill-posedness and nonlinearity inherent in the inverse problem. The three paths were designed to be interconnected, allowing for an exchange of information on different receptive fields with varied learning abilities. Our network has a bottleneck architecture that enables it to recover signal information from noisy redundant measurements. We named our proposed model truncated spatial-spectral convolutional neural network (TSS-ConvNet).Main results.Our model demonstrated superior accuracy with relatively high resolution on both simulation and experimental data. This indicates that our approach offers significant potential for addressing ill-posed inverse problems in complex conditions effectively and accurately.Significance.The TSS-ConvNet overcomes the receptive field limitation found in most existing models that only utilize local information in Euclidean space. We trained the network on a large dataset covering various configurations with random parameters to ensure generalization over the training samples.
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Affiliation(s)
- Ayman A Ameen
- Physics Department, Faculty of Science, Sohag University, Egypt
| | - Achim Sack
- Institute for Multiscale Simulation, Department of Chemical and Biological Engineering, Friedrich-Alexander University of Erlangen-Nürnberg, Cauerstrae 3, D-91058 Erlangen, Germany
| | - Thorsten Pöschel
- Institute for Multiscale Simulation, Department of Chemical and Biological Engineering, Friedrich-Alexander University of Erlangen-Nürnberg, Cauerstrae 3, D-91058 Erlangen, Germany
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Zheng HY, Li Y, Wang N, Xiang Y, Liu JH, Zhang LD, Huang L, Wang ZY. A novel framework for three-dimensional electrical impedance tomography reconstruction of maize ear via feature reconfiguration and residual networks. PeerJ Comput Sci 2024; 10:e1944. [PMID: 38660147 PMCID: PMC11042020 DOI: 10.7717/peerj-cs.1944] [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: 10/03/2023] [Accepted: 02/27/2024] [Indexed: 04/26/2024]
Abstract
Electrical impedance tomography (EIT) provides an indirect measure of the physiological state and growth of the maize ear by reconstructing the distribution of electrical impedance. However, the two-dimensional (2D) EIT within the electrode plane finds it challenging to comprehensively represent the spatial distribution of conductivity of the intact maize ear, including the husk, kernels, and cob. Therefore, an effective method for 3D conductivity reconstruction is necessary. In practical applications, fluctuations in the contact impedance of the maize ear occur, particularly with the increase in the number of grids and computational workload during the reconstruction of 3D spatial conductivity. These fluctuations may accentuate the ill-conditioning and nonlinearity of the EIT. To address these challenges, we introduce RFNetEIT, a novel computational framework specifically tailored for the absolute imaging of the three-dimensional electrical impedance of maize ear. This strategy transforms the reconstruction of 3D electrical conductivity into a regression process. Initially, a feature map is extracted from measured boundary voltage via a data reconstruction module, thereby enhancing the correlation among different dimensions. Subsequently, a nonlinear mapping model of the 3D spatial distribution of the boundary voltage and conductivity is established, utilizing the residual network. The performance of the proposed framework is assessed through numerical simulation experiments, acrylic model experiments, and maize ear experiments. Our experimental results indicate that our method yields superior reconstruction performance in terms of root-mean-square error (RMSE), correlation coefficient (CC), structural similarity index (SSIM), and inverse problem-solving time (IPST). Furthermore, the reconstruction experiments on maize ears demonstrate that the method can effectively reconstruct the 3D conductivity distribution.
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Affiliation(s)
- Hai-Ying Zheng
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, Beijing, China
| | - Yang Li
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, Beijing, China
| | - Nan Wang
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, Beijing, China
| | - Yang Xiang
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, Beijing, China
| | - Jin-Hang Liu
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, Beijing, China
| | - Liu-Deng Zhang
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, Beijing, China
| | - Lan Huang
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, Beijing, China
| | - Zhong-Yi Wang
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
- Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, Beijing, China
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Baran B, Kozłowski E, Majerek D, Rymarczyk T, Soleimani M, Wójcik D. Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking. SENSORS (BASEL, SWITZERLAND) 2023; 23:1553. [PMID: 36772593 PMCID: PMC9918926 DOI: 10.3390/s23031553] [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: 12/13/2022] [Revised: 01/27/2023] [Accepted: 01/29/2023] [Indexed: 06/18/2023]
Abstract
The article presents the implementation of artificial intelligence algorithms for the problem of discretization in Electrical Impedance Tomography (EIT) adapted for urinary tract monitoring. The primary objective of discretization is to create a finite element mesh (FEM) classifier that will separate the inclusion elements from the background. In general, the classifier is designed to detect the area of elements belonging to an inclusion revealing the shape of that object. We show the adaptation of supervised learning methods such as logistic regression, decision trees, linear and quadratic discriminant analysis to the problem of tracking the urinary bladder using EIT. Our study focuses on developing and comparing various algorithms for discretization, which perfectly supplement methods for an inverse problem. The innovation of the presented solutions lies in the originally adapted algorithms for EIT allowing for the tracking of the bladder. We claim that a robust measurement solution with sensors and statistical methods can track the placement and shape change of the bladder, leading to effective information about the studied object. This article also shows the developed device, its functions and working principle. The development of such a device and accompanying information technology came about in response to particularly strong market demand for modern technical solutions for urinary tract rehabilitation.
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Affiliation(s)
- Bartłomiej Baran
- Research & Development Centre Netrix S.A., 20-704 Lublin, Poland
| | - Edward Kozłowski
- Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
| | - Dariusz Majerek
- Faculty of Fundamentals of Technology, Lublin University of Technology, 20-618 Lublin, Poland
| | - Tomasz Rymarczyk
- Research & Development Centre Netrix S.A., 20-704 Lublin, Poland
- WSEI University, 20-209 Lublin, Poland
| | - Manuchehr Soleimani
- Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
| | - Dariusz Wójcik
- Research & Development Centre Netrix S.A., 20-704 Lublin, Poland
- WSEI University, 20-209 Lublin, Poland
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Liu X, Zhang T, Ye J, Tian X, Zhang W, Yang B, Dai M, Xu C, Fu F. Fast Iterative Shrinkage-Thresholding Algorithm with Continuation for Brain Injury Monitoring Imaging Based on Electrical Impedance Tomography. SENSORS (BASEL, SWITZERLAND) 2022; 22:9934. [PMID: 36560297 PMCID: PMC9783778 DOI: 10.3390/s22249934] [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: 11/14/2022] [Revised: 12/07/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
Electrical impedance tomography (EIT) is low-cost and noninvasive and has the potential for real-time imaging and bedside monitoring of brain injury. However, brain injury monitoring by EIT imaging suffers from image noise (IN) and resolution problems, causing blurred reconstructions. To address these problems, a least absolute shrinkage and selection operator model is built, and a fast iterative shrinkage-thresholding algorithm with continuation (FISTA-C) is proposed. Results of numerical simulations and head phantom experiments indicate that FISTA-C reduces IN by 63.2%, 47.2%, and 29.9% and 54.4%, 44.7%, and 22.7%, respectively, when compared with the damped least-squares algorithm, the split Bergman, and the FISTA algorithms. When the signal-to-noise ratio of the measurements is 80-50 dB, FISTA-C can reduce IN by 83.3%, 72.3%, and 68.7% on average when compared with the three algorithms, respectively. Both simulation and phantom experiments suggest that FISTA-C produces the best image resolution and can identify the two closest targets. Moreover, FISTA-C is more practical for clinical application because it does not require excessive parameter adjustments. This technology can provide better reconstruction performance and significantly outperforms the traditional algorithms in terms of IN and resolution and is expected to offer a general algorithm for brain injury monitoring imaging via EIT.
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Affiliation(s)
- Xuechao Liu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
- Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an 710032, China
| | - Tao Zhang
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
- Drug and Instrument Supervision and Inspection Station, Xining Joint Logistics Support Center, Lanzhou 730050, China
| | - Jian’an Ye
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
| | - Xiang Tian
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
| | - Weirui Zhang
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
| | - Bin Yang
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
- Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an 710032, China
| | - Meng Dai
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
- Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an 710032, China
| | - Canhua Xu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
- Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an 710032, China
| | - Feng Fu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an 710032, China
- Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an 710032, China
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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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