Total fractional-order variation regularization based image reconstruction method for capacitively coupled electrical resistance tomography

Shi, Yanyan; Liao, Juanjuan; Wang, Meng; Li, Yating; Fu, Feng; Soleimani, Masoud

doi:10.1016/j.flowmeasinst.2021.102081

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…Hence in this case, (15) holds. This contradicts with the relation (15). Hence the only minimizer of ( 13) is z n+1 [k] = 0.…”

Section: Appendixmentioning

confidence: 91%

A nonlinear weighted anisotropic total variation regularization for electrical impedance tomography

Song¹,

Wang²,

Liu³

2022

Preprint

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This paper proposes a nonlinear weighted anisotropic total variation (NWATV) regularization technique for electrical impedance tomography (EIT). The key idea is to incorporate the internal inhomogeneity information (e.g., edges of the detected objects) into the EIT reconstruction process, aiming to preserve the conductivity profiles (to be detected). We study the NWATV image reconstruction by employing a novel soft thresholding based reformulation included in the alternating direction method of multipliers (ADMM). To evaluate the proposed approach, 2D and 3D numerical experiments and human EIT lung imaging are carried out. It is demonstrated that the properties of the internal inhomogeneity are well preserved and improved with the proposed regularization approach, in comparison to traditional total variation (TV) and recently proposed fidelity embedded regularization approaches. Owing to the simplicity of the proposed method, the computational cost is significantly decreased compared with the well established primal-dual algorithm. Meanwhile, it was found that the proposed regularization method is quite robust to the measurement noise, which is one of the main uncertainties in EIT.

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“…Hence in this case, (15) holds. This contradicts with the relation (15). Hence the only minimizer of ( 13) is z n+1 [k] = 0.…”

Section: Appendixmentioning

confidence: 91%

A nonlinear weighted anisotropic total variation regularization for electrical impedance tomography

Song¹,

Wang²,

Liu³

2022

Preprint

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show abstract

“…Additionally, the TV regularization may introduce significant artefacts in homogeneous conductivity regions due to the inclusion of high-frequency components. A recent effort to address the latter is the introduction of a fractional-order TV regularization prior scheme, applied in capacitively coupled EIT [135]. Finally, an improved convergence rate l 1 -norm optimization method is the fast iterative shrinkage/thresholding algorithm (FISTA).…”

Section: E Total Variation Regularizationmentioning

confidence: 99%

Advances in Electrical Impedance Tomography Inverse Problem Solution Methods: From Traditional Regularization to Deep Learning

Dimas,

Alimisis,

Uzunoglu

et al. 2024

IEEE Access

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Electrical Impedance Tomography (EIT) has emerged as a valuable medical imaging modality, which visualizes the conductivity distribution of a subject by performing multi-electrode impedance measurements. EIT finds applications in monitoring lung and cardiac function, brain imaging and the detection of malignant tissues. Its mobility, outstanding temporal resolution and the absence of ionizing radiation make it particularly suitable for repetitive real-time monitoring and diagnostics, especially in radiation-sensitive populations, such as neonates. This paper presents a methodological review of EIT image reconstruction approaches spanning from traditional linear regularization and back-projection to more recent techniques, including deep learning, sparse Bayesian learning and non-linear shape-driven reconstruction. Linear and non-linear reconstruction approaches are distinguished, as well as time, frequency difference and absolute reconstruction ones. The exposition includes a concise elaboration of the methodologies' mathematical foundations and algorithmic deployment, with particular attention to recent advancements. For each approach, an assessment of its merits and drawbacks is given, providing implementation considerations, imaging performance and relevant applications.

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“…For quantitative estimation of the quality of the reconstructed images, the relative reconstruction error (RRE), the correlation coefficient (CC) [45], and the structural similarity (SSIM) index [46] The results demonstrate that the EFGM forward solver is more reliable when used to reconstruct conductivity distribution.…”

Section: D Inverse Problem Solutionmentioning

confidence: 99%

Improvement of performance and sensitivity of 2D and 3D image reconstruction in EIT using EFG forward model

Hadinia¹,

Jafari²

2022

Biomed. Phys. Eng. Express

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This paper presents a pure element-free Galerkin method (EFGM) forward model for image reconstruction in 2D and 3D electrical impedance tomography (EIT) using an adaptive current injection method. In EIT systems with the adapting current injection method, both static and dynamic images can be reconstructed; however, determination of electrode contact impedances in the complete electrode model is difficult and the Gap model is used. In this paper, in the EIT forward problem a weak form functional based on the Gap model and a pure EFGM approach are developed, and in the EIT inverse problem, Jacobian matrix is computed by the EFGM, and a fast integration technique is introduced to calculate the entries of the Jacobian matrix within an adequate computation time. The influence of increasing the density of nodes at and near the electrodes with steep electric potential gradients on the accuracy of FEM and EFGM forward solutions is investigated, and the performance of the image reconstruction algorithm with the proposed fast integration technique is examined. The numerical results reveal that the proposed EFGM forward model with the fast integration technique has an efficient performance both in terms of mean relative imaging errors and computational time.

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Total fractional-order variation regularization based image reconstruction method for capacitively coupled electrical resistance tomography

Cited by 10 publications

References 35 publications

A nonlinear weighted anisotropic total variation regularization for electrical impedance tomography

A nonlinear weighted anisotropic total variation regularization for electrical impedance tomography

Advances in Electrical Impedance Tomography Inverse Problem Solution Methods: From Traditional Regularization to Deep Learning

Improvement of performance and sensitivity of 2D and 3D image reconstruction in EIT using EFG forward model

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