Reducing negative effects of quadratic norm regularization on image reconstruction in electrical impedance tomography

Javaherian, Ashkan; Amir, Mohammad; Faghihi, Reza

doi:10.1016/j.apm.2012.11.022

Cited by 13 publications

(10 citation statements)

References 32 publications

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“…In this work, we provide a simple method to improve the conductivity change detection when the time-course is previously known (13). However, other approaches could be used with this additional information.…”

Section: Zero Gain Constraints and Knowledge Of The Time-coursementioning

confidence: 99%

Linearly constrained minimum variance spatial filtering for localization of conductivity changes in electrical impedance tomography

Fernandez-Corazza

Ellenrieder

Muravchik

2015

Numer Methods Biomed Eng

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We localize dynamic electrical conductivity changes and reconstruct their time evolution introducing the spatial filtering technique to electrical impedance tomography (EIT). More precisely, we use the unit-noise-gain constrained variation of the distortionless-response linearly constrained minimum variance spatial filter. We address the effects of interference and the use of zero gain constraints. The approach is successfully tested in simulated and real tank phantoms. We compute the position error and resolution to compare the localization performance of the proposed method with the one-step Gauss-Newton reconstruction with Laplacian prior. We also study the effects of sensor position errors. Our results show that EIT spatial filtering is useful for localizing conductivity changes of relatively small size and for estimating their time-courses. Some potential dynamic EIT applications such as acute ischemic stroke detection and neuronal activity localization may benefit from the higher resolution of spatial filters as compared to conventional tomographic reconstruction algorithms.

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Section: Zero Gain Constraints and Knowledge Of The Time-coursementioning

confidence: 99%

Linearly constrained minimum variance spatial filtering for localization of conductivity changes in electrical impedance tomography

Fernandez-Corazza

Ellenrieder

Muravchik

2015

Numer Methods Biomed Eng

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“…In this way, the problem is stabilized at the cost of imposing some smoothness on the reconstructed image, so detecting sharp discontinuities over the conductivity field will be impossible [11,36]. There are, however, many organs that have well-defined boundaries, and thus represent sharp variations over the conductivity profile, e.g., interfaces between collapsed and ventilated regions of lung [10,11].…”

Section: Introductionmentioning

confidence: 98%

A fast time-difference inverse solver for 3D EIT with application to lung imaging

Javaherian

Soleimani

Möeller

2016

Med Biol Eng Comput

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A class of sparse optimization techniques that require solely matrix-vector products, rather than an explicit access to the forward matrix and its transpose, has been paid much attention in the recent decade for dealing with large-scale inverse problems. This study tailors application of the so-called Gradient Projection for Sparse Reconstruction (GPSR) to large-scale time-difference three-dimensional electrical impedance tomography (3D EIT). 3D EIT typically suffers from the need for a large number of voxels to cover the whole domain, so its application to real-time imaging, for example monitoring of lung function, remains scarce since the large number of degrees of freedom of the problem extremely increases storage space and reconstruction time. This study shows the great potential of the GPSR for large-size time-difference 3D EIT. Further studies are needed to improve its accuracy for imaging small-size anomalies.

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“…The extracted data are available on the EIDORS website for 34 frames of a breathing cycle of a male subject. The data have been embedded in an m-file, namely "montreal data-1995" [28]. A challenge arose since the actual conductivity distribution of the lung frames was not available.…”

Section: Human Lungmentioning

confidence: 99%

“…3-The third value has been calculated by employing the fixed noise figure method using the EIDORS software. The noise figure is a measure of noise amplification from the data to the reconstructed image; see [21,28] for more details. Graham and Adler [22] showed that the regularization parameter which leads to N F = 1 is the best approximation of the optimal regularization.…”

Section: Image Reconstructionmentioning

confidence: 99%

“…The amount of regularization in the PD-IPM algorithm is conducted by the MRPM matrix, as well as multiple specified scalar TV regularization parameters, i.e. the value selected in the EIDORS website, the optimal value calculated by objectively minimizing the RE over a wide range of regularization parameters, and the value calculated by the fixed noise figure method (N F = 1) (see [15,21,22,28,29]). The objective optimization yields the most accurate solution among all the scalar regularization parameters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving the performance of primal--dual interior-point method in inverse conductivity problems

Javaherian¹,

Amir²,

Faghihi³

et al. 2015

Turk J Elec Eng & Comp Sci

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This study improves the performance of primal-dual interior-point method in inverse conductivity problems via replacing the conventional, complicatedly calculated scalar regularization parameter with a diagonal matrix termed "multi-regularization parameter matrix" here. The solution of the PD-IPM depends considerably on the choice of the regularization parameter. Calculation of the optimal regularization parameter, which yields the most accurate solution, is not simple due to the long iterative nature of the algorithm. The objective optimization, which is implemented by minimizing error in the solutions over an extensive range of the regularization parameters, yields the most accurate solution that can be achieved, although this method is not applicable in reality due to lack of knowledge about the actual conductivity field. However, the modified algorithm not only solves the problem independently using the regularization parameter, but also increases the accuracy of the solution, as well as its sharpness in comparison to the objective optimization.

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Reducing negative effects of quadratic norm regularization on image reconstruction in electrical impedance tomography

Cited by 13 publications

References 32 publications

Linearly constrained minimum variance spatial filtering for localization of conductivity changes in electrical impedance tomography

Linearly constrained minimum variance spatial filtering for localization of conductivity changes in electrical impedance tomography

A fast time-difference inverse solver for 3D EIT with application to lung imaging

Improving the performance of primal--dual interior-point method in inverse conductivity problems

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