Bayesian Shape Reconstruction Using B-Spline Level Set in Electrical Impedance Tomography

Wu, Yang; Zhou, Tong; Chen, Bai; Liu, Kai; Wang, Rui-Qiang; Pan, Huaping; Yao, Jiafeng

doi:10.1109/jsen.2022.3199436

Cited by 8 publications

(2 citation statements)

References 36 publications

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“…Among these methods, regularization methods and iterative methods are very popular for image reconstruction [14][15][16]. In recent years, Bayesian learning based reconstruction methods have been presented [17][18][19]. With a structural prior incorporated, the computational complexity of the inverse problem is reduced and the accuracy of the reconstructed image is improved.…”

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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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 (EIT) is a potential non-destructive imaging technique with which physiology-or pathology-related conductivity variation can be detected [8][9][10]. In EIT-based medical imaging, a safe current is injected into a pair of electrodes attached to the body and the boundary voltage across the remaining electrode pairs is measured [11,12]. Based on the measured data, a conductivity distribution within the detection region can be restored.…”

Section: Introductionmentioning

confidence: 99%

Imaging of intracerebral hemorrhage with adaptive genetic algorithm in brain electrical impedance tomography

Shi

Liu

et al. 2023

Meas. Sci. Technol.

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Intracerebral hemorrhage refers to bleeding caused by spontaneous rupture of blood vessels. Accurate diagnosis of hemorrhage is vital in the treatment of a patient. As a new medical imaging technique, electrical impedance tomography (EIT) is able to offer images of conductivity distribution variation caused by pathological change. However, image reconstruction of EIT suffers from the problem of serious ill-posedness. Especially in the brain imaging, irregular and multi-layered head structure together with the low conductivity skull further aggravate the problem. In order to address the problem, a new image reconstruction method is proposed for imaging of hemorrhage in this work. With the solution solved by Tikhonov regularization method as the original conductivity distribution, the proposed method tends to enhance the reconstruction quality by introducing adaptive genetic algorithm. To test the performance of the proposed method, simulation work is conducted. A three-layer head model is established and an inclusion which simulates hemorrhage is placed at six different locations in the brain layer. Images reconstructed by Tikhonov method, Newton-Raphson method and traditional Genetic Algorithm method are used for comparisons. Quantitative evaluation is also performed. The anti-noise performance of the proposed method is estimated by considering noise with signal-to-noise ratio of different levels. Aside from the simulation, phantom experiments are carried out to further verify the performance of the proposed method. The results show that the proposed method performs well in the reconstruction of simulated intracerebral hemorrhage. With the proposed method, the inclusion can be more accurately reconstructed and the background is much clearer than other three traditional methods.

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Electrical impedance tomography image reconstruction for lung monitoring based on ensemble learning algorithms1

Al‐Bashir,

Al‐Bataiha,

Hafsa

et al. 2024

Healthcare Tech Letters

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Electrical impedance tomography (EIT) is a promising non‐invasive imaging technique that visualizes the electrical conductivity of an anatomic structure to form based on measured boundary voltages. However, the EIT inverse problem for the image reconstruction is nonlinear and highly ill‐posed. Therefore, in this work, a simulated dataset that mimics the human thorax was generated with boundary voltages based on given conductivity distributions. To overcome the challenges of image reconstruction, an ensemble learning method was proposed. The ensemble method combines several convolutional neural network models, which are the simple Convolutional Neural Network (CNN) model, AlexNet, AlexNet with residual block, and the modified AlexNet model. The ensemble models’ weights selection was based on average technique giving the best coefficient of determination (R2 score). The reconstruction quality is quantitatively evaluated by calculating the root mean square error (RMSE), the coefficient of determination (R2 score), and the image correlation coefficient (ICC). The proposed method's best performance is an RMSE of 0.09404, an R2 score of 0.926186, and an ICC of 0.95783 using an ensemble model. The proposed method is promising as it can construct valuable images for clinical EIT applications and measurements compared to previous studies.

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Bayesian Shape Reconstruction Using B-Spline Level Set in Electrical Impedance Tomography

Cited by 8 publications

References 36 publications

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

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

Imaging of intracerebral hemorrhage with adaptive genetic algorithm in brain electrical impedance tomography

Electrical impedance tomography image reconstruction for lung monitoring based on ensemble learning algorithms1

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