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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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Zhang Y, Ye J, Jiao Y, Zhang W, Zhang T, Tian X, Shi X, Fu F, Wang L, Xu C. A pilot study of contrast-enhanced electrical impedance tomography for real-time imaging of cerebral perfusion. Front Neurosci 2022; 16:1027948. [PMID: 36507353 PMCID: PMC9729948 DOI: 10.3389/fnins.2022.1027948] [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: 08/25/2022] [Accepted: 11/04/2022] [Indexed: 11/25/2022] Open
Abstract
Background Real-time detection of cerebral blood perfusion can prevent adverse reactions, such as cerebral infarction and neuronal apoptosis. Our previous clinical trial have shown that the infusion of therapeutic fluid can significantly change the impedance distribution in the brain. However, whether this alteration implicates the cerebral blood perfusion remains unclear. To explore the feasibility of monitoring cerebral blood perfusion, the present pilot study established a novel cerebral contrast-enhanced electrical impedance tomography (C-EIT) technique. Materials and methods Rabbits were randomly divided into two groups: the internal carotid artery non-occlusion (ICAN) and internal carotid artery occlusion (ICAO) groups. Both of groups were injected with glucose, an electrical impedance-enhanced contrast agent, through the right internal carotid artery under EIT monitoring. The C-EIT reconstruction images of the rabbits brain were analyzed according to the collected raw data. The paired and independent t-tests were used to analyze the remodeled impedance values of the left and right cerebral hemispheres within and between studied groups, respectively. Moreover, pathological examinations of brain were performed immediately after C-EIT monitoring. Results According to the reconstructed images, the impedance value of the left cerebral hemisphere in the ICAN group did not change significantly, whereas the impedance value of the right cerebral hemisphere gradually increased, reaching a peak at approximately 10 s followed by gradually decreased. In the ICAO group, the impedance values of both cerebral hemispheres increased gradually and then began to decrease after reaching the peak value. According to the paired t-test, there was a significant difference (P < 0.001) in the remodeling impedance values between the left and right hemispheres in the ICAN group, and there was also a significant difference (P < 0.001) in the ICAO group. According to the independent t-test, there was a significant difference (P < 0.001) of the left hemispheres between the ICAN and ICAO groups. Conclusion The cerebral C-EIT proposed in this pilot study can reflect cerebral blood perfusion. This method has potential in various applications in the brain in the future, including disease progression monitoring, collateral circulation judgment, tumor-specific detection, and brain function research.
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Affiliation(s)
- Yuyan Zhang
- College of Life Sciences, Northwest University, Xi’an, China
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - Jian’an Ye
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - Yang Jiao
- Department of Neurosurgery, Tangdu Hospital of Fourth Military Medical University, Xi’an, China
| | - Weirui Zhang
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - Tao Zhang
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - Xiang Tian
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - Xuetao Shi
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - Feng Fu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - Liang Wang
- Department of Neurosurgery, Tangdu Hospital of Fourth Military Medical University, Xi’an, China
| | - Canhua Xu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi’an, China
- Shaanxi Provincial Key Laboratory of Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
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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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Shi Y, Tian Z, Wang M, Fu F, Wu Y. Arrangement of boundary electrodes for detection of frontal lobe disease with electrical impedance tomography. J Med Imaging (Bellingham) 2021; 8:044501. [PMID: 34250199 DOI: 10.1117/1.jmi.8.4.044501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/21/2021] [Indexed: 11/14/2022] Open
Abstract
Purpose: Caused by brain trauma or blood vessel abnormality, intracerebral hemorrhage and secondary ischemia have become prevalent and severe neurological diseases. The timely and accurate detection of disease is essential for the recovery of patients. As an emerging visualization technique, electrical impedance tomography (EIT) offers an alternative. It is able to reconstruct the conductivity distribution that reflects the pathological variation of human tissue. Approach: In the EIT-based detection, electrodes are usually in uniform arrangement, which may be not suitable in some conditions. To enhance sensitivity in the region of interest, EIT with a novel offset arrangement of boundary electrodes is proposed to image a simulated frontal lobe hemorrhage and secondary ischemia. To cope with the ill-posed inverse problem, the L1 regularization method is developed during the reconstruction. In addition, the impact of noise with a signal-to-noise ratio of 56 dB is studied. Results: Compared with the traditional uniform electrode arrangement, the results demonstrate that EIT with the proposed offset arrangement of electrodes is more advantageous for imaging frontal lobe disease. Conclusions: The proposed offset arrangement of electrodes is superior to the traditional uniform arrangement in imaging frontal lobe disease, especially under the impact of noise.
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Affiliation(s)
- Yanyan Shi
- Henan Normal University, College of Electronic and Electrical Engineering, Xinxiang, China.,Fourth Military Medical University, School of Biomedical Engineering, Xi'an, China
| | - Zhiwei Tian
- Henan Normal University, College of Electronic and Electrical Engineering, Xinxiang, China
| | - Meng Wang
- Henan Normal University, College of Electronic and Electrical Engineering, Xinxiang, China
| | - Feng Fu
- Fourth Military Medical University, School of Biomedical Engineering, Xi'an, China
| | - Yuehui Wu
- Henan Normal University, College of Electronic and Electrical Engineering, Xinxiang, China
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Shi Y, Wu Y, Wang M, Tian Z, Kong X, He X. Sparse image reconstruction of intracerebral hemorrhage with electrical impedance tomography. J Med Imaging (Bellingham) 2021; 8:014501. [PMID: 33457443 DOI: 10.1117/1.jmi.8.1.014501] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 12/22/2020] [Indexed: 12/12/2022] Open
Abstract
Purpose: Intracerebral hemorrhage (ICH) is a common disease that is known for its high morbidity, high mortality, and high disability. The fast and accurate detection of ICH is essential for the acute care of patients. Electrical impedance tomography (EIT) offers an alternative with which pathological tissues can be detected by reconstructing conductivity variation. Nevertheless, the sensitive field of EIT is greatly affected by medium distribution, which is referred to as soft-field effect. In addition, the image reconstruction is a severely ill-posed inverse problem. Furthermore, due to the low conductivity of skull, the sensitivity in the sensing area is extremely low. Therefore, the reconstruction of ICH with EIT is great challenge. Approach: A sparse image reconstruction method is proposed for EIT to visualize the conductivity variation caused by ICH. To reduce the impact of soft-field effect, the normalization of sensitivity distribution is conducted for monolayer and three-layer head model. In addition, a constrained sparse L 1 -norm minimization model is developed for the image reconstruction. Augmented Lagrangian multiplier method and alternating minimization scheme are adopted to solve the proposed model. Results: The results show that the sensitivity in the sensing area is largely enhanced. Numerical simulation based on monolayer head model and three-layer head model is respectively carried out. Both the reconstructed images and the quantitative evaluations show that image reconstructed by the proposed method is much better than that reconstructed by traditional Tikhonov method. The reconstructions evaluated under the impact of noise also show that the proposed method has superior anti-noise performance. Conclusions: With the proposed method, the quality of the reconstructed image would be greatly improved. It is an effective approach for imaging ICH with EIT technique.
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Affiliation(s)
- Yanyan Shi
- Henan Normal University, Department of Electronic and Electrical Engineering, Xinxiang, China.,Fourth Military Medical University, College of Biomedical Engineering, Xi'an, China
| | - Yuehui Wu
- Henan Normal University, Department of Electronic and Electrical Engineering, Xinxiang, China
| | - Meng Wang
- Henan Normal University, Department of Electronic and Electrical Engineering, Xinxiang, China
| | - Zhiwei Tian
- Henan Normal University, Department of Electronic and Electrical Engineering, Xinxiang, China
| | - Xiaolong Kong
- Henan Normal University, Department of Electronic and Electrical Engineering, Xinxiang, China
| | - Xiaoyue He
- Henan Normal University, Department of Electronic and Electrical Engineering, Xinxiang, China
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