Electrical Impedance Tomography

Adler, Andy; Gaburro, Romina; Lionheart, William

doi:10.1007/978-1-4939-0790-8_14

Cited by 19 publications

(18 citation statements)

References 103 publications

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“…This fibre alignment, together with the fibre cytostructure, produces a fraction change in impedance during neural activity which is significantly higher in longitudinal direction, parallel to the fibres, than in the transverse direction, across fibres, as predicted by models of unmyelinated and myelinated fibres [1], and observed in in-vivo experiments [23]. Furthermore, the presence of anisotropy can produce boundary voltage data with non-unique solution [24], although numerical methods with some a-priori information have proven capable of reconstructing anisotropic anomalies in 2D simulations [24][25][26] and in 3D simulations to manage anisotropy of white matter in the brain [27,28]. Transverse current patterns in a nerve would largely eliminate the anisotropy by operating in a plane perpendicular to the axis containing the unique conductivity (the 'anisotropic axis'), an approach adopted by [29] on peripheral nerve and by [30] on muscle tissue.…”

Section: Introductionmentioning

confidence: 85%

Drive and measurement electrode patterns for electrode impedance tomography (EIT) imaging of neural activity in peripheral nerve

Hope¹,

Vanholsbeeck²,

McDaid³

2018

Biomed. Phys. Eng. Express

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Objective: To establish the performance of several drive and measurement patterns in EIT imaging of neural activity in peripheral nerve, which involves large impedance changes in the nerve's anisotropic length axis. Approach: Eight drive and measurement electrode patterns are compared using a finite element (FE) fourcylindrical shell model of a peripheral nerve and a 32 channel dual-ring nerve cuff. The central layer of the FE model contains impedance changes representative of neural activity of -0.30 in length axis and -8.8x10 -4 in the radial axis. Four of the electrode patterns generate longitudinal drive current, which runs parallel to the anisotropic axis, while the remaining four patterns generate transverse drive current, which runs perpendicular to the anisotropic axis.Main results: Transverse current patterns produce higher resolution than longitudinal current patterns but are also more susceptible to noise and errors, and exhibit poorer sensitivity to impedance changes in central sample locations. Three of the four longitudinal current patterns considered can reconstruct fascicle level impedance changes with up to 0.2 mV noise and error, which corresponds to between -5.5 and +0.18 dB of the normalised signal standard deviation. Reducing the spacing between the two electrode rings in all longitudinal current patterns reduced the signal to error ratio across all depth locations of the sample. Significance: Electrode patterns which target the large impedance change in the anisotropic length axis can provide improved robustness against noise and errors, which is a critical step towards real time EIT imaging of neural activity in peripheral nerve.

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Section: Introductionmentioning

confidence: 85%

Drive and measurement electrode patterns for electrode impedance tomography (EIT) imaging of neural activity in peripheral nerve

Hope¹,

Vanholsbeeck²,

McDaid³

2018

Biomed. Phys. Eng. Express

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“…where u solves the conductivity equation −∇ · (σ∇u) = 0 in Ω. For further details, the reader is referred to the review papers [17,15,35,1,36] and to the references therein.…”

Section: Introductionmentioning

confidence: 99%

Calderón's inverse problem with a finite number of measurements II: independent data

Alberti

Santacesaria

2020

Applicable Analysis

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We prove a local Lipschitz stability estimate for Gel'fand-Calderón's inverse problem for the Schrödinger equation. The main novelty is that only a finite number of boundary input data is available, and those are independent of the unknown potential, provided it belongs to a known finitedimensional subspace of L ∞ . A similar result for Calderón's problem is obtained as a corollary. This improves upon two previous results of the authors on several aspects, namely the number of measurements and the stability with respect to mismodeling errors. A new iterative reconstruction scheme based on the stability result is also presented, for which we prove exponential convergence in the number of iterations and stability with respect to noise in the data and to mismodeling errors.

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“…The generalized Tikhonov regularized solution to the Regularized Linear(RL) problem is given by boldxLR=(boldJTJ+λLTL)−1boldJTy. Other common choices for regularization penalty terms in EIT include truncated singular value decomposition, and Total Variation. For further details see Adler et al (2015), and the references therein, as well as other chapters in the same work.…”

Section: Methods: Reconstructionmentioning

confidence: 99%

“…Subsequently, reconstruction methods based on regularization techniques have become most widely used, and are distributed with EIT devices from Dräger, SenTec and Timpal. While in biomedical EIT difference imaging has been widely used mainly due to the difficulty in modeling body shape and electrode position, in geophysical applications of EIT difference data was typically not available and consequently absolute EIT reconstruction is common (Adler et al 2015). In this case, an accurate forward model is used and the absolute conductivity iteratively fitted to the data.…”

Section: Introductionmentioning

confidence: 99%

Comparing D-bar and common regularization-based methods for electrical impedance tomography

2019

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Objective:To compare D-bar difference reconstruction with regularized linear reconstruction in electrical impedance tomography.Approach:A standard regularized linear approach using a Laplacian penalty and the GREIT method for comparison to the D-bar difference images. Simulated data was generated using a circular phantom with small objects, as well as a ‘Pac-Man’ shaped conductivity target. An L-curve method was used for parameter selection in both D-bar and the regularized methods.Main results:We found that the D-bar method had a more position independent point spread function, was less sensitive to errors in electrode position and behaved differently with respect to additive noise than the regularized methods.Significance:The results allow a novel pathway between traditional and D-bar algorithm comparison.

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Electrical Impedance Tomography

Cited by 19 publications

References 103 publications

Drive and measurement electrode patterns for electrode impedance tomography (EIT) imaging of neural activity in peripheral nerve

Drive and measurement electrode patterns for electrode impedance tomography (EIT) imaging of neural activity in peripheral nerve

Calderón's inverse problem with a finite number of measurements II: independent data

Comparing D-bar and common regularization-based methods for electrical impedance tomography

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