Comparison of total variation algorithms for electrical impedance tomography

Zhou, Zhou; Santos, Gustavo Sato dos; Dowrick, Thomas; Avery, James; Sun, Zhaolin; Xu, Hui; Holder, David

doi:10.1088/0967-3334/36/6/1193

Cited by 48 publications

(34 citation statements)

References 28 publications

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“…However, the objective function of TV regularization (equations ( 20 ) and ( 21 )) can not be effectively solved by traditional linearization techniques because it is non-differential. For this problem, previous research works have verified that the split Bregman (SB) iterative algorithm was effective to split the data fidelity term and the non-differential l 1 -norm penalty term of the objective function to a sequence of unconstrained problems which can be easily solved 19 . So, the SB method is used in this work.…”

Section: Methodsmentioning

confidence: 99%

Capacitively Coupled Phase-based Dielectric Spectroscopy Tomography

Jiang

Soleimani

2018

Sci Rep

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Impedance imaging is an effective approach for non-intrusively reconstruct the distribution of dielectric parameters inside the region of interest. Most common form of impedance imaging is electrical impedance tomography (EIT), which requires direct contact with the medium via electrodes. In this work we present a novel impedance imaging using capacitive coupling which provides a contactless method, totally non-invasive and non-intrusive, by measuring the phase. There are less attentions in many prior works to the phase information of the voltage/current measurements. This work studies for the first time the capacitively coupled electrical phase spectroscopy for contactless dielectric parameter imaging. A 12-electrode capacitively coupled test phantom and a measurement system were used to obtain the phase measurements within a wideband frequency range from 200 kHz to 15 MHz. Background data with different conductivity levels were investigated in the experiments to show a broad application possibility. The forward modelling was implemented by simulation and the image reconstruction based on phase measurements was implemented with the total variation algorithm. The potentials, possibilities and challenges of such capacitively coupled dielectric spectroscopy tomography with phase data are discussed in this work.

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Section: Methodsmentioning

confidence: 99%

Capacitively Coupled Phase-based Dielectric Spectroscopy Tomography

Jiang

Soleimani

2018

Sci Rep

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“…The algebraic reconstruction technique (ART) is a simple and effective image reconstruction method for computerized tomography that can be applied to EIT [16], [17]. Other important methods include regularization via the total variation (TV) functional [18], [19], which allows image reconstruction with edge preservation.…”

Section: Introductionmentioning

confidence: 99%

Image Reconstruction in Electrical Impedance Tomography Based on Structure-Aware Sparse Bayesian Learning

Liu

Jia

Zhang

et al. 2018

IEEE Trans. Med. Imaging

176

116

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Electrical impedance tomography (EIT) is developed to investigate the internal conductivity changes of an object through a series of boundary electrodes, and has become increasingly attractive in a broad spectrum of applications. However, the design of optimal tomography image reconstruction algorithms has not achieved the adequate level of progress and matureness. In this paper, we propose an efficient and high-resolution EIT image reconstruction method in the framework of sparse Bayesian learning. Significant performance improvement is achieved by imposing structure-aware priors on the learning process to incorporate the prior knowledge that practical conductivity distribution maps exhibit clustered sparsity and intra-cluster continuity. The proposed method not only achieves high-resolution estimation and preserves the shape information even in low signal-to-noise ratio scenarios but also avoids the time-consuming parameter tuning process. The effectiveness of the proposed algorithm is validated through comparisons with state-of-the-art techniques using extensive numerical simulation and phantom experiment results.

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“…However, most algorithms would give a result with blurred edges of the object boundary, and lead to reconstruction error because Tikhonov would over-smooth the images. In the past years, another method called total variation (TV) with different TV functions, such as, [ 4 , 5 ] has been proposed. TV has attracted attention, as higher-quality images can be obtained.…”

Section: Introductionmentioning

confidence: 99%

Extended Joint Sparsity Reconstruction for Spatial and Temporal ERT Imaging

Chen

Abascal

Soleimani

2018

Sensors

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Electrical resistance tomography (ERT) is an imaging technique to recover the conductivity distribution with boundary measurements via attached electrodes. There are a wide range of applications using ERT for image reconstruction or parameter calculation due to high speed data collection, low cost, and the advantages of being non-invasive and portable. Although ERT is considered a high temporal resolution method, a temporally regularized method can greatly enhance such a temporal resolution compared to frame-by-frame reconstruction. In some of the cases, especially in the industrial applications, dynamic movement of an object is critical. In practice, it is desirable for monitoring and controlling the dynamic process. ERT can determine the spatial conductivity distribution based on previous work, and ERT potentially shows good performance in exploiting temporal information as well. Many ERT algorithms reconstruct images frame by frame, which is not optimal and would assume that the target is static during collection of each data frame, which is inconsistent with the real case. Although spatiotemporal-based algorithms can account for the temporal effect of dynamic movement and can generate better results, there is not that much work aimed at analyzing the performance in the time domain. In this paper, we discuss the performance of a novel spatiotemporal total variation (STTV) algorithm in both the spatial and temporal domain, and Temporal One-Step Tikhonov-based algorithms were also employed for comparison. The experimental results show that the STTV has a faster response time for temporal variation of the moving object. This robust time response can contribute to a much better control process which is the main aim of the new generation of process tomography systems.

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Comparison of total variation algorithms for electrical impedance tomography

Cited by 48 publications

References 28 publications

Capacitively Coupled Phase-based Dielectric Spectroscopy Tomography

Capacitively Coupled Phase-based Dielectric Spectroscopy Tomography

Image Reconstruction in Electrical Impedance Tomography Based on Structure-Aware Sparse Bayesian Learning

Extended Joint Sparsity Reconstruction for Spatial and Temporal ERT Imaging

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