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Liu J, Chen L, Xiong H, Zhang L. A multi-scale attention residual-based U-Net network for stroke electrical impedance tomography. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2024; 95:033702. [PMID: 38526440 DOI: 10.1063/5.0176494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 03/02/2024] [Indexed: 03/26/2024]
Abstract
Electrical impedance tomography (EIT), a non-invasive, radiation-free, and convenient imaging technique, has been widely used in the diagnosis of stroke. However, due to soft-field nonlinearity and the ill-posed inverse problem, EIT images always suffer from low spatial resolution. Therefore, a multi-scale convolutional attention residual-based U-Net (MARU-Net) network is proposed for stroke reconstruction. Based on the U-Net network, a residual module and a multi-scale convolutional attention module are added to the concatenation layer. The multi-scale module extracts feature information of different sizes, the attention module strengthens the useful information, and the residual module improves the performance of the network. Based on the above advantages, the network is used in the EIT system for stroke imaging. Compared with convolutional neural networks and one-dimensional convolutional neural networks, the MARU-Net network has fewer artifacts, and the reconstructed image is clear. At the same time, the reduction of noisy artifacts in the MARU-Net network is verified. The results show that the image correlation coefficient of the reconstructed image with noise is greater than 0.87. Finally, the practicability of the network is verified by a model physics experiment.
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Affiliation(s)
- Jinzhen Liu
- The School of Control Science and Engineering, Tiangong University, Tianjin 300387, People's Republic of China
- Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, People's Republic of China
| | - Liming Chen
- The School of Control Science and Engineering, Tiangong University, Tianjin 300387, People's Republic of China
- Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, People's Republic of China
| | - Hui Xiong
- The School of Control Science and Engineering, Tiangong University, Tianjin 300387, People's Republic of China
- Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, People's Republic of China
| | - Liying Zhang
- The School of Control Science and Engineering, Tiangong University, Tianjin 300387, People's Republic of China
- Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, People's Republic of China
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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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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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Wang M, Zheng S, Shi Y, Lou Y. Hybrid method for improving Tikhonov-based reconstruction quality in electrical impedance tomography. J Med Imaging (Bellingham) 2022; 9:054503. [PMID: 36267548 PMCID: PMC9574320 DOI: 10.1117/1.jmi.9.5.054503] [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: 05/09/2022] [Accepted: 09/23/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose Electrical impedance tomography (EIT) has shown its potential in the field of medical imaging. Physiological or pathological variation would cause the change of conductivity. EIT is favorable in reconstructing conductivity distribution inside the detected area. However, due to its ill-posed and nonlinear characteristics, reconstructed images suffer from low spatial resolution. Approach Tikhonov regularization method is a popular and effective approach for image reconstruction in EIT. Nevertheless, excessive smoothness is observed when reconstruction is conducted based on Tikhonov method. To improve Tikhonov-based reconstruction quality in EIT, an innovative hybrid iterative optimization method is proposed. An efficient alternating minimization algorithm is introduced to solve the optimization problem. Results To verify image reconstruction performance and anti-noise robustness of the proposed method, a series of simulation work and phantom experiments is carried out. Meanwhile, comparison is made with reconstruction results based on Landweber, Newton-Raphson, and Tikhonov methods. The reconstruction performance is also verified by quantitative comparison of blur radius and structural similarity values which further demonstrates the excellent performance of the proposed method. Conclusions In contrast to Landweber, Newton-Raphson, and Tikhonov methods, it is found that images reconstructed by the proposed method are more accurate. Even under the impact of noise, the proposed method outperforms comparison methods.
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Affiliation(s)
- Meng Wang
- Henan Normal University, College of Electronic and Electrical Engineering, Henan Key Laboratory of Optoelectronic Sensing Integrated Application, Xinxiang, China
| | - Shuo Zheng
- Henan Normal University, College of Electronic and Electrical Engineering, Henan Key Laboratory of Optoelectronic Sensing Integrated Application, Xinxiang, China
| | - Yanyan Shi
- Henan Normal University, College of Electronic and Electrical Engineering, Henan Key Laboratory of Optoelectronic Sensing Integrated Application, Xinxiang, China
- Fourth Military Medical University, School of Biomedical Engineering, Xian, China
| | - Yajun Lou
- Henan Normal University, College of Electronic and Electrical Engineering, Henan Key Laboratory of Optoelectronic Sensing Integrated Application, Xinxiang, China
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