A electrical impedance tomography system
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Abstract:
A system for the electrical impedance tomography is described in the paper. A digital signal processing (DSP) chip is used to control the operation of every module. As DSP is easy for high-speed control and flexible for embed algorithm and data preprocessing, the system can sample the data and reconstruct the impedance variation distribution quickly that makes the real time image monitoring possible. At here, the principle, implementation and performance of the system are illustrated. And the experimental result, the reconstructed impedance variation distribution image, shows the efficiency of the system.Keywords:
Electrical Impedance Tomography
SIGNAL (programming language)
Image reconstruction is a key step in electrical impedance tomography (EIT). The inverse problem of EIT is a nonlinear, highly morbid and under-qualitative problem, the resolution of EIT image reconstructed by traditional methods is relatively low. In order to apply electrical impedance tomography to practice successfully, it is urgent to find an effective image reconstruction algorithm. After the advantage of deep learning in self-learning nonlinear mapping between input and output was found, it was used in electrical impedance tomography image reconstruction. In this paper, convolution neural network (CNN) is used to solve the problem of image reconstruction. The data set and the 16-electrode EIT model are obtained through the joint simulation of MATLAB and COMSOL. The data set is divided into training set and test set, and the expected result is obtained after training the network.
Electrical Impedance Tomography
Convolution (computer science)
Electrical Resistivity Tomography
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Electrical impedance tomography (EIT) is a tomographic imaging modality for reconstructing the conductivity distribution through boundary current injection and induced voltage measurement. High-quality image is of great significant for improving the qualitative imaging performance in biomedical application. In this paper, the structured sparsity algorithm is proposed to incorporate with the underlying structure of the conductivity on the basis of the sparse priors. The structured sparsity is integrated into the iterative process of the Symkaczmarz algorithm for EIT image reconstruction. Both simulation and experiment results indicate that the proposed method has feasibility for pulmonary ventilation imaging and great potential for improving the image quality.
Electrical Impedance Tomography
Tomographic reconstruction
Modality (human–computer interaction)
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In electrical impedance tomography (EIT), different currents are applied to electrodes on the surface of an object and the resulting voltages are measured. The image of impedance (resitivity, conductivity) distribution or changes in the object is reconstructed based on these boundary measurements. EIT is a functional imaging technique, which may reveal the physiological and pathological information by human body's impedance properties. The advantages such as the non-invasive modality and the relative low cost make EIT become a research hot in medical imaging. However, the image reconstruction in EIT is a high ill-posed, non-linear, inverse problem, and it becomes a key and difficult point in EIT. In dynamic EIT, the image of impedance changes in the object is reconstructed. On the other hand, the image of impedance distribution in the object is reconstructed in static EIT, which is more difficult than in dynamic EIT. We study the reconstruction of static EIT in this paper, due to the static EIT has better clinical application prospect than the dynamic EIT.
Electrical Impedance Tomography
Modality (human–computer interaction)
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The problem of the image reconstruction in Electrical Impedance Tomography (EIT) is a highly ill-posed inverse problem. There are mainly two categories of image reconstruction algorithms, the direct algorithm and the iterative algorithm which was used in this publication. The representation of the shape of the boundary and its evolution during an iterative reconstruction process is achieved by the level set function and the Chan-Vese model. The forward problem was solved by the finite element method.
Electrical Impedance Tomography
Representation
Reconstruction algorithm
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The reconstruction of electrical impedance tomography (EIT) images poses an inverse problem that requires iterative solution. However, due to its severe ill-conditioning the EIT problem often fails to converge toward a physically meaningful minimum. In this paper, we present an estimator that tracks the convergence of an iterative EIT image reconstruction process by comparing the trajectories of the image conductivities and the objective function. In a computer simulation, this criterion was fulfilled for all reconstructions that converged to a meaningful image.
Electrical Impedance Tomography
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Electrical Impedance Tomography (EIT) is a low-cost imaging method which reconstructs two-dimensional cross-sectional images, visualising the impedance change within the thorax. However, the reconstruction of an EIT image is an ill-posed inverse problem. In addition, blurring, anatomical alignment, and reconstruction artefacts can hinder the interpretation of EIT images. In this contribution, we introduce a patient-specific structural prior mask into the EIT reconstruction process, with the aim of improving image interpretability. Such a prior mask ensures that only conductivity changes within the lung regions are reconstructed. To evaluate the influence of the introduced structural prior mask, we conducted numerical simulations with two scopes in terms of their different ventilation statuses and varying atelectasis scales. Quantitative analysis, including the reconstruction error and figures of merit, was applied in the evaluation procedure. The results show that the morphological structures of the lungs introduced by the mask are preserved in the EIT reconstructions and the reconstruction artefacts are decreased, reducing the reconstruction error by 25.9% and 17.7%, respectively, in the two EIT algorithms included in this contribution. The use of the structural prior mask conclusively improves the interpretability of the EIT images, which could facilitate better diagnosis and decision-making in clinical settings.
Electrical Impedance Tomography
Interpretability
Figure of Merit
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Electrical impedance tomography (EIT) is a new medical imaging technology that is used to estimate changes in the internal conductivity based on measurements of the border voltage disturbance. However, the generalized inverse operator of image reconstruction for EIT is ill-posed and ill-conditioned. In order to improve reconstruction quality, the structured sparse representation is integrated into the iterative process of the Symkaczmarz algorithm for EIT image reconstruction in this paper. The sparsity prior and the underlying structure characteristics of conductivity reconstruction are considered in the proposed algorithm. Both simulation and experiment results indicate that the proposed method has feasibility for pulmonary ventilation imaging and great potential for improving the image quality.
Electrical Impedance Tomography
Representation
Reconstruction algorithm
Tomographic reconstruction
Operator (biology)
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In electrical impedance tomography (EIT), the internal resistivity distribution of the unknown object is computed using the boundary voltage data induced by different current patterns with various reconstruction algorithms. The paper presents an image reconstruction algorithm based on a genetic algorithm (GA) via a two-step approach for the solution of the EIT inverse problem, in particular for the reconstruction of "static" images. Computer simulations with the 32 channels synthetic data show that the spatial resolution of reconstructed images by the proposed scheme is improved compared to that of the modified Newton-Raphson algorithm at the expense of increased computational burden.
Electrical Impedance Tomography
Reconstruction algorithm
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Electrical Impedance Tomography (EIT) is a fast and non-invasive imaging technology that reconstructs the internal electrical properties of a subject. However, its functionality is limited by low spatial resolution arising from an ill-posed and ill-conditioned inverse problem. Several sparsity-promoting regularization methods have been applied to improve the quality of EIT image reconstruction, including various ℓ 0 and ℓ 1 -based analytical models (TV, TwIST, etc.), and a patch-based sparse representation via a learned dictionary (using the K-SVD algorithm), dubbed CS-EIT. To further exploit the potential of compressed sensing in Electrical Impedance Tomography, this paper incorporates the recent novel method of transform learning for EIT image reconstruction. We propose a blind compressed sensing algorithm, dubbed TL-EIT, which simultaneously optimizes the sparsifying transform and updates the reconstructed image. We demonstrate using both synthetic and in vivo data that the proposed TL-EIT is more effective than other sparsity-based algorithms for reconstructing high-quality EIT images. In addition, TL-EIT also accelerates the reconstruction process in comparison to other learning-based algorithms like CS-EIT.
Electrical Impedance Tomography
Regularization
Reconstruction algorithm
Electrical Resistivity Tomography
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A new reconstruction algorithm for Electrical Impedance Tomography (EIT) and the results of the reconstruction using 2D EIT system are presented. The analysis of the images of the physical and mathematical phantoms allowed conclusions to be made about spatial and contrast resolution in EIT. The successful imaging of the explanted canine heart makes an optimistic prognosis about utilization of this technology and proposed reconstruction algorithm in medicine.
Electrical Impedance Tomography
Reconstruction algorithm
Electrical Resistivity Tomography
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