A Rotational Invariant Neural Network for Electrical Impedance Tomography Imaging without Reference Voltage: RF-REIM-NET.
Diagnostics (Basel)
; 12(4)2022 Mar 22.
Article
em En
| MEDLINE
| ID: mdl-35453825
ABSTRACT
Background:
Electrical Impedance Tomography (EIT) is a radiation-free technique for image reconstruction. However, as the inverse problem of EIT is non-linear and ill-posed, the reconstruction of sharp conductivity images poses a major problem. With the emergence of artificial neural networks (ANN), their application in EIT has recently gained interest.Methodology:
We propose an ANN that can solve the inverse problem without the presence of a reference voltage. At the end of the ANN, we reused the dense layers multiple times, considering that the EIT exhibits rotational symmetries in a circular domain. To avoid bias in training data, the conductivity range used in the simulations was greater than expected in measurements. We also propose a new method that creates new data samples from existing training data.Results:
We show that our ANN is more robust with respect to noise compared with the analytical Gauss-Newton approach. The reconstruction results for EIT phantom tank measurements are also clearer, as ringing artefacts are less pronounced. To evaluate the performance of the ANN under real-world conditions, we perform reconstructions on an experimental pig study with computed tomography for comparison.Conclusions:
Our proposed ANN can reconstruct EIT images without the need of a reference voltage.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Diagnostics (Basel)
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Alemanha