Shape Reconstruction With Multiphase Conductivity for Electrical Impedance Tomography Using Improved Convolutional Neural Network Method

Wu, Yang; Chen, Bai; Li, Kai; Zhu, Chengjun; Pan, Huaping; Jia, Jiabin; Wu, Hongtao; Yao, Jiafeng

doi:10.1109/jsen.2021.3050845

Cited by 62 publications

(31 citation statements)

References 36 publications

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“…Notably, the results of the studies related to impedance distribution reconstruction using machine learning in the last few years indicate that the ICC is generally between 0.5 and 0.9. 27,36,37 The abovementioned results quantitatively validate the feasibility of using the EIT strategy for hydrogel pressure distribution sensing.…”

Section: ■ Results and Discussionsupporting

confidence: 59%

Hydrogel Pressure Distribution Sensors Based on an Imaging Strategy and Machine Learning

Liu

Zhang

Yang

et al. 2021

ACS Appl. Electron. Mater.

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A flexible hydrogel pressure distribution sensor has promising application prospects. However, the current hydrogel pressure distribution sensors are based on an array-type structure with complicated wires and extremely low resolution, which greatly reduce the flexibility of hydrogel and limit their applications. To overcome these limitations, we proposed and designed a hydrogel pressure distribution sensor that is able to obtain pressure distribution inside a whole piece of hydrogel. This is specifically done by arranging electrodes only around the hydrogel, which employs the strategy of electrical impedance tomography (EIT). Meanwhile, PAAm/PAA-Fe3+ double-network hydrogels were prepared as hydrogel pressure-sensitive substrates, and the feasibility of PAAm/PAA-Fe3+ hydrogels as sensitive elements for hydrogel pressure distribution sensors was confirmed through mechanical and electrical tests. Furthermore, based on the hydrogel pressure distribution sensor, a pressure distribution reconstruction model was obtained with a machine learning method. To verify the feasibility of the hydrogel pressure distribution sensor based on the EIT strategy, the hydrogel sensor was applied with forces of known location and magnitude. Then, the actual sensor acquisition data were reconstructed and compared with the applied force.

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Section: ■ Results and Discussionsupporting

confidence: 59%

Hydrogel Pressure Distribution Sensors Based on an Imaging Strategy and Machine Learning

Liu

Zhang

Yang

et al. 2021

ACS Appl. Electron. Mater.

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“…In this work, we focus on the design of a general method of response acquisition, but we maintain the tomographic imaging static. The EIT measurements in an electrolyte tank system is an important phase in the development of tomographic devices and methods and it is an element continuously present in the EIT literature, for example, in the recent works [ 36 , 37 ]. The use of tanks is fundamental for modeling and testing tomographic algorithms, given that the shape of the surface and the electrode locations are stable.…”

Section: Resultsmentioning

confidence: 99%

Electrical Tomography Reconstruction Using Reconfigurable Waveforms in a FPGA

Vejar

Rymarczyk

2021

Sensors

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The principal objective of this research is to conceive a mobile system based on electrical tomography for subsurface imaging and monitoring in order to enable simultaneous recording of electrical potentials of cardiac and pulmonary activity. For an exploration of excitation waveforms in electrical tomography, specialized hardware is required. As the main principle of tomography is the measurement of electrical perturbations on an unknown object, it is crucial to synchronize excitation and sensing processes in a very precise way for the purpose of acquiring meaningful data. To cope with this problem, an FPGA device is used, with an architecture that allows us to trigger excitation signals and to read sensed data simultaneously via independent processes that share the same clock. In this way, waveform reconfiguration on frequency and shape can be provided and studied. The system is connected to a standard microcontroller SoC with a simple API that allows for IoT capabilities for on-line operation and tracking, given that the design is targeted for in vivo medical monitoring. As a result of the research work, a measuring device was developed, the surface data analyzed and the image was reconstructed using the selected configuration.

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“…The solver adopts the adaptive moment estimation (ADAM) algorithm to update the network weights instead of the classical stochastic gradient descent algorithm. The ADAM algorithm uses the momentum and adaptive learning rate to speed up the network convergence with good robustness [ 29 ].…”

Section: Principles and Methodsmentioning

confidence: 99%

A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction

Yang

Wang

Bin

et al. 2021

Entropy

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Arterial stenosis will reduce the blood flow to various organs or tissues, causing cardiovascular diseases. Although there are mature diagnostic techniques in clinical practice, they are not suitable for early cardiovascular disease prediction and monitoring due to their high cost and complex operation. In this paper, we studied the electromagnetic effect of arterial blood flow and proposed a method based on the deep neural network for arterial blood flow profile reconstruction. The potential difference and weight matrix are used as inputs to the method, and its output is an estimate of the internal blood flow velocity distribution for arterial blood flow profile reconstruction. Firstly, the weight matrix is input into the convolutional auto-encode (CAE) network to extract its features. Then, the weight matrix features and potential difference are combined to obtain the features of the blood velocity distribution. Finally, the velocity features are reconstructed into blood flow velocity distribution by a convolution neural network (CNN). All data sets are obtained from a model of the carotid artery with different rates of stenosis in a uniform magnetic field by COMSOL. The results show that the average root mean square error of the reconstruction results obtained by the proposed method is 0.0333, and the average correlation coefficient is 0.9721, which is better than the corresponding indicators of the Tikhonov, back propagation (BP) and CNN methods. The simulation results show that the proposed method can achieve high accuracy in blood flow profile reconstruction and is of great significance for the early diagnosis of arterial stenosis and other vessel diseases.

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Shape Reconstruction With Multiphase Conductivity for Electrical Impedance Tomography Using Improved Convolutional Neural Network Method

Cited by 62 publications

References 36 publications

Hydrogel Pressure Distribution Sensors Based on an Imaging Strategy and Machine Learning

Hydrogel Pressure Distribution Sensors Based on an Imaging Strategy and Machine Learning

Electrical Tomography Reconstruction Using Reconfigurable Waveforms in a FPGA

A Deep Neural Network Method for Arterial Blood Flow Profile Reconstruction

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