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Wang Z, Li J, Sun Y. Layered Fusion Reconstruction Based on Fuzzy Features for Multi-Conductivity Electrical Impedance Tomography. SENSORS (BASEL, SWITZERLAND) 2024; 24:3380. [PMID: 38894168 PMCID: PMC11175079 DOI: 10.3390/s24113380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 05/17/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024]
Abstract
In medical imaging, detecting tissue anomalies is vital for accurate diagnosis and effective treatment. Electrical impedance tomography (EIT) is a non-invasive technique that monitors the changes in electrical conductivity within tissues in real time. However, the current challenge lies in simply and accurately reconstructing multi-conductivity distributions. This paper introduces a layered fusion framework for EIT to enhance imaging in multi-conductivity scenarios. The method begins with pre-imaging and extracts the main object from the fuzzy image to form one layer. Then, the voltage difference in the other layer, where the local anomaly is located, is estimated. Finally, the corresponding conductivity distribution is established, and multiple layers are fused to reconstruct the multi-conductivity distribution. The simulation and experimental results demonstrate that compared to traditional methods, the proposed method significantly improves multi-conductivity separation, precise anomaly localization, and robustness without adding uncertain parameters. Notably, the proposed method has demonstrated exceptional accuracy in local anomaly detection, with positional errors as low as 1% and size errors as low as 33%, which significantly outperforms the traditional method with respective minimum errors of 9% and 228%. This method ensures a balance between the simplicity and accuracy of the algorithm. At the same time, it breaks the constraints of traditional linear methods, struggling to identify multi-conductivity distributions, thereby providing new perspectives for clinical EIT.
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Affiliation(s)
- Zeying Wang
- School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Jiaqing Li
- School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yixuan Sun
- School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
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Rahman H, Khan AR, Sadiq T, Farooqi AH, Khan IU, Lim WH. A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction. Tomography 2023; 9:2158-2189. [PMID: 38133073 PMCID: PMC10748093 DOI: 10.3390/tomography9060169] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/27/2023] [Accepted: 12/01/2023] [Indexed: 12/23/2023] Open
Abstract
Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.
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Affiliation(s)
- Hameedur Rahman
- Department of Computer Games Development, Faculty of Computing & AI, Air University, E9, Islamabad 44000, Pakistan;
| | - Abdur Rehman Khan
- Department of Creative Technologies, Faculty of Computing & AI, Air University, E9, Islamabad 44000, Pakistan;
| | - Touseef Sadiq
- Centre for Artificial Intelligence Research, Department of Information and Communication Technology, University of Agder, Jon Lilletuns vei 9, 4879 Grimstad, Norway
| | - Ashfaq Hussain Farooqi
- Department of Computer Science, Faculty of Computing AI, Air University, Islamabad 44000, Pakistan;
| | - Inam Ullah Khan
- Department of Electronic Engineering, School of Engineering & Applied Sciences (SEAS), Isra University, Islamabad Campus, Islamabad 44000, Pakistan;
| | - Wei Hong Lim
- Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia;
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Wang Y, Ping X, Chen X, Wang D. Flexible Electrodes as a Measuring System of Electrical Impedance Imaging. MATERIALS (BASEL, SWITZERLAND) 2023; 16:1901. [PMID: 36903016 PMCID: PMC10004451 DOI: 10.3390/ma16051901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/17/2023] [Accepted: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Electrical Impedance Tomography (EIT) is a detection imaging technology developed 30 years ago. When the conventional EIT measurement system is used, the electrode and the excitation measurement terminal are connected with a long wire, which is easily affected by external interference, and the measurement result is unstable. In this paper, we developed a flexible electrode device based on flexible electronics technology, which can be softly attached to the skin surface for real-time physiological monitoring. The flexible equipment includes an excitation measuring circuit and electrode, which eliminates the adverse effects of connecting long wires and improves the effectiveness of measuring signals. At the same time, the design also uses flexible electronic technology to make the system structure achieve ultra-low modulus and high tensile strength so that the electronic equipment has soft mechanical properties. Experiments have shown that when the flexible electrode is deformed, its function is completely unaffected, the measurement results remain stable, and the static and fatigue performances are satisfactory. The flexible electrode has high system accuracy and good anti-interference.
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Affiliation(s)
- Yi Wang
- College of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, China
- Tianjin Key Laboratory of Integrated Design and On-Line Monitoring for Light Industry & Food Machinery and Equipment, Tianjin 300222, China
| | - Xuecheng Ping
- College of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, China
- Tianjin Key Laboratory of Integrated Design and On-Line Monitoring for Light Industry & Food Machinery and Equipment, Tianjin 300222, China
| | - Xiaoyan Chen
- College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300222, China
| | - Di Wang
- College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300222, China
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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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Liu Z, Bagnaninchi P, Yang Y. Impedance-Optical Dual-Modal Cell Culture Imaging With Learning-Based Information Fusion. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:983-996. [PMID: 34797763 DOI: 10.1109/tmi.2021.3129739] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
While Electrical Impedance Tomography (EIT) has found many biomedicine applications, better image quality is needed to provide quantitative analysis for tissue engineering and regenerative medicine. This paper reports an impedance-optical dual-modal imaging framework that primarily targets at high-quality 3D cell culture imaging and can be extended to other tissue engineering applications. The framework comprises three components, i.e., an impedance-optical dual-modal sensor, the guidance image processing algorithm, and a deep learning model named multi-scale feature cross fusion network (MSFCF-Net) for information fusion. The MSFCF-Net has two inputs, i.e., the EIT measurement and a binary mask image generated by the guidance image processing algorithm, whose input is an RGB microscopic image. The network then effectively fuses the information from the two different imaging modalities and generates the final conductivity image. We assess the performance of the proposed dual-modal framework by numerical simulation and MCF-7 cell imaging experiments. The results show that the proposed method could improve the image quality notably, indicating that impedance-optical joint imaging has the potential to reveal the structural and functional information of tissue-level targets simultaneously.
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