A nonlinear approach to difference imaging in EIT; assessment of the robustness in the presence of modelling errors

Liu, Dong; Kolehmainen, Ville; Siltanen, Samuli; Seppänen, Aku

doi:10.1088/0266-5611/31/3/035012

Cited by 63 publications

(46 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Framing the reconstruction approach in the context of linear difference imaging or nonlinear difference imaging would be another potential solution. This is because difference imaging tends to be quite robust to these sources of modeling errors, which are largely canceled out in the measurement subtraction and absorbed by the background estimation using the nonlinear approach [44], [45].…”

Section: E Limitations and Further Developmentsmentioning

confidence: 99%

B-Spline-Based Sharp Feature Preserving Shape Reconstruction Approach for Electrical Impedance Tomography

Liu

Smyl

et al. 2019

IEEE Trans. Med. Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

Section: E Limitations and Further Developmentsmentioning

confidence: 99%

B-Spline-Based Sharp Feature Preserving Shape Reconstruction Approach for Electrical Impedance Tomography

Liu

Smyl

et al. 2019

IEEE Trans. Med. Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…No matter which imaging technology, to some extent, the same basic approach is followed. If the problem is nonlinear, then the problem is linearized; if the problem is ill‐posed, then a priori information is added to find the nearby moderate problem by the regularization; if the linear approximation is not correct, the error is minimized by the iterative solution …”

Section: Eit For Artificial Sensitive Skinsmentioning

confidence: 99%

Artificial Sensitive Skin for Robotics Based on Electrical Impedance Tomography

Wang

et al. 2020

Advanced Intelligent Systems

View full text Add to dashboard Cite

Electrical impedance tomography (EIT) is a noninvasive measurement technique that estimates the internal resistivity distribution based on the boundary voltage–current data measured from the surface of the conductor. If a thin stretchable soft material with a certain piezoresistive property is used as the electrical conductor, EIT has the ability to reconstruct the position where the resistivity changes due to the inside pressure contact, so that a large‐scale artificial sensitive skin is provided for robotics. First, the different conduction principles and material types of artificial sensitive skins are discussed, which is next followed by the different driving modes and image reconstruction techniques. Then, details on how EIT is used for robotic skin applications are described. Finally, the development trends and future potentials of EIT‐based robotic skins are expounded.

show abstract

“…However, the modelling error is practically unknown, and it is usually ignored [3]. Therefore, the estimation ̂ can be determined by the absolute imaging approach with the regularization function R and the regularization parameter  as in (2).…”

Section: Errors In Difference Imaging Approachmentioning

confidence: 99%

“…In a homogeneous situation e.g. in [3], reconstruction using the standard approach -absolute imaging -is possible due to the small size of the error. However, in the case of head EIT this is not possible -the modelling error is too large.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tackling modelling error in the application of electrical impedance tomography to the head

Ouypornkochagorn

McCann

Polydorides

2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

In the head application of Electrical Impedance Tomography (EIT), reconstruction of voltage measurements for a conductivity distribution image using an ordinary method, the absolute imaging approach, is impossible due to the traditional ignorance of modelling error. The modelling error comes from the inaccuracy of geometry and structure, which are unable to be known accurately in practice, and are usually large in head application of EIT. Difference imaging is an alternative approach which is able to reduce the size of this error, but it introduces other kinds of error. In this work, we demonstrate that in situations like head EIT, the nonlinear difference imaging approach can reconstruct difference conductivity effectively: the reduced modelling error and the new errors arising are able to be ignored, because they are much smaller than the original modelling error. The magnitude of conductivity change in the head-like situation is also investigated, and a selection scheme for the initial guess in the reconstruction process is also proposed.

show abstract

A nonlinear approach to difference imaging in EIT; assessment of the robustness in the presence of modelling errors

Abstract: Inverse Problems 31 (2015) 035012 D Liu et al

Cited by 63 publications

References 62 publications

B-Spline-Based Sharp Feature Preserving Shape Reconstruction Approach for Electrical Impedance Tomography

B-Spline-Based Sharp Feature Preserving Shape Reconstruction Approach for Electrical Impedance Tomography

Artificial Sensitive Skin for Robotics Based on Electrical Impedance Tomography

Tackling modelling error in the application of electrical impedance tomography to the head

Contact Info

Product

Resources

About