Split Bregman iteration based image reconstruction algorithm for electrical capacitance tomography

Tong, Guowei; Shi, Liwei; Guo, Hongbo; Chen, Hongyan

doi:10.1016/j.flowmeasinst.2019.02.003

Cited by 9 publications

(5 citation statements)

References 35 publications

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“…With the introduction of compressed sensing theory, L 1 norm sparse constrained problems have received considerable attention (Donoho 2006). The sparse constrained algorithm enables the construction of images from small amounts of data (Tong et al 2019). Goldstein and Osher showed that the split Bregman (SB) method could be used to solve L 1 norm sparse constrained problems and applied it to a compressed sensing problem in magnetic resonance imaging (Goldstein and Osher 2009).…”

Section: Introductionmentioning

confidence: 99%

Adaptive threshold split Bregman algorithm based on magnetic induction tomography for brain injury monitoring imaging

et al. 2021

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Objective. Traditional magnetic induction tomography (MIT) algorithms have problems in reconstruction, such as large area error (AE), blurred boundaries of reconstructed targets, and considerable image noise (IN). As the size and boundary of a lesion greatly affect the treatment plan, more accurate algorithms are necessary to meet clinical needs. Approach. In this study, adaptive threshold split Bregman (ATSB) is proposed for brain injury monitoring imaging in MIT. We established a 3D brain MIT simulation model with the actual anatomical structure and a phantom model and obtained the reconstructed images of single targets in different positions and multiple targets, using the Tikhonov, eigenvalue threshold regularisation (ETR), split Bregman (SB), and ATSB algorithms. Main results. Compared with the Tikhonov and ETR algorithms, the ATSB algorithm reduced the AE by 95% and the IN by 17% in a simulation and reduced the AE by 87% and IN by 6% in phantom experiments. Compared with the SB algorithm, the ATSB algorithm can reduce the difficulty of adjusting parameters and is easier to use in clinical practice. The simulation and phantom experiments results showed that the ATSB algorithm could reconstruct the target size more accurately and could distinguish multiple targets more effectively than the other three algorithms. Significance. The ATSB algorithm could improve the image quality of MIT and better meet the needs of clinical applications and is expected to promote brain injury monitoring imaging via MIT.

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Section: Introductionmentioning

confidence: 99%

Adaptive threshold split Bregman algorithm based on magnetic induction tomography for brain injury monitoring imaging

et al. 2021

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“…The TV optimization problem defined by ( 25) and ( 32) can be solved using several techniques, such as the primal-dual interior point method (PD-IPM) [23]- [25], the alternating direction method of multipliers (ADMM) [25], [32] and the split-Bregman distance method [33]- [35], [132]- [134]. Since the problem is non-linear, these techniques require iterative procedures without the existence of a closed-form solution.…”

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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“…The Split Bregman (SB) method can be used to solve the unconstrained optimization problem, which splits the complex optimization problem into several simple sub-tasks. Each subtask can be solved by proper method [22,23]. The SB method can be applied to solve the following minimization problem:…”

Section: Split Bregman Frameworkmentioning

confidence: 99%

A Sparsity Reconstruction Algorithm of Electromagnetic Tomography Technique for High Conductivity Medium Imaging

Liu

Wang

et al. 2023

Sens Imaging

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Electromagnetic tomography (EMT) is a versatile tomographic imaging technique for reconstruction of conductivity and/or permeability distribution due to the advantages of non-contact, non-intrusive, lowcost, simple structure and fast imaging. However, the ill-posed and ill-conditioned features of EMT make it difficult to obtain high quality reconstructed images. To improve the spatial resolution of the high conductivity medium imaging, the L1-L1 framework objective function is presented, which uses L1 norm as both data fidelity term and regularization term to weaken the influence of the data outliers and impose the sparsity feature of the measured objects. An improved Split Bregman method is proposed to solve the complicated optimization problem efficiently, which splits it into several simple sub-tasks.Each subtask can be solved by adopting the proper method. Besides, an acceleration strategy is introduced to improve the convergence rate. Numerical simulations are used to verify the effectiveness and competitive performance of the proposed improved method. The experiments are carried out by the designed modularized EMT system to further verify the effectiveness of the proposed method. The reconstructed images can precisely show the number and positions of the measured objects.

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Split Bregman iteration based image reconstruction algorithm for electrical capacitance tomography

Cited by 9 publications

References 35 publications

Adaptive threshold split Bregman algorithm based on magnetic induction tomography for brain injury monitoring imaging

Adaptive threshold split Bregman algorithm based on magnetic induction tomography for brain injury monitoring imaging

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

A Sparsity Reconstruction Algorithm of Electromagnetic Tomography Technique for High Conductivity Medium Imaging

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