RCRC: A Deep Neural Network for Dynamic Image Reconstruction of Electrical Impedance Tomography

Ren, Shangjie; Guan, Ru; Liang, Guanghui; Dong, Feng

doi:10.1109/tim.2021.3092061

Cited by 23 publications

(19 citation statements)

References 31 publications

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“…Comparing the results with those obtained in other works [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ] in the field of EIT reconstruction using machine learning methods, it can be concluded that at least some of the models presented in this work (especially the CART model) dominate the published achievements in terms of the obtained measures of reconstruction quality.…”

Section: Resultssupporting

confidence: 71%

Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking

Baran¹,

Kozłowski

Majerek

et al. 2023

Sensors

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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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Section: Resultssupporting

confidence: 71%

Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking

Baran¹,

Kozłowski

Majerek

et al. 2023

Sensors

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“…Fan et al proposed a novel neural network architecture for the EIT problem, which combined a 2D CNN based on BCR-Net for EIT image reconstruction ( Fan and Ying, 2020 ). Considering that the measured voltages or target images in EIT dynamic imaging are spatiotemporally correlated, Ren et al (2021) proposed a RCRC DNN, comprising a reconstruction network, recurrent neural network model, CNN encoder, and CNN decoder, as shown in Figure 6 .…”

Section: Deep Learning In Eit Image Reconstructionmentioning

confidence: 99%

“…FIGURE 6Typical hybrid deep learning reconstruction network RCRC that can automatically learn prior spatial-temporal information from the training dataset and utilize it to enhance the conductivity reconstruction accuracy(Ren et al, 2021).…”

mentioning

confidence: 99%

Advances of deep learning in electrical impedance tomography image reconstruction

Zhang

Tian

Liu

et al. 2022

Front. Bioeng. Biotechnol.

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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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“…However, CT is radioactive while MRI is time-consuming, and neither of them is suitable for bedside monitoring. Comparatively, electrical impedance tomography (EIT) is a safe, low cost, reliable and fast method that has been successfully applied in various fields of medical imaging [7][8][9][10]. In the brain imaging, conductivity variation caused by hemorrhage can be also recovered by EIT [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance Tomography

et al. 2023

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Electrical impedance tomography is an emerging technique for brain disease detection. Generally, it requires that electrodes should be equidistantly placed around the detected region. However, this may be not possible for some patients who are undergoing post-surgical monitoring. Aiming at this problem, four kinds of non-uniform electrode arrangements are developed. To accurately detect the location of intracranial hemorrhage, a novel classification method based on a priori information of electrode arrangement is also proposed in this paper. According to the electrode arrangement information, the weight which corresponds to different kinds of electrode arrangement is separately determined during the training process. The proposed method is quantitatively evaluated with basic test dataset, test datasets under noise interruption, test datasets in the case of large contact impedance, test datasets with conductivity variation in different layers, and test datasets when considering modeling error and double inclusions. Comparisons with general classification methods are also conducted. The results show that the proposed method with residual network incorporated outperforms the classification methods of fully connected neural network and residual network. For all the test datasets, the results show that the accuracy is higher than 0.9 and the specificity reaches as high as 1 when the proposed method incorporating residual network is used.

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RCRC: A Deep Neural Network for Dynamic Image Reconstruction of Electrical Impedance Tomography

Cited by 23 publications

References 31 publications

Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking

Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking

Advances of deep learning in electrical impedance tomography image reconstruction

Classification of Hemorrhage Using Priori Information of Electrode Arrangement With Electrical Impedance Tomography

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