EIT image reconstruction with four dimensional regularization

A new damage estimation method for carbon fiber reinforced polymer based on electrical impedance tomography

Damage estimation is vital for monitoring the remaining life of carbon fiber reinforced plastic/polymer (CFRP). As a non-invasive, non-radiative, and low-cost method, electrical impedance tomography (EIT) is increasingly investigated for the purpose of structural health monitoring of CFRP. The commonly used EIT method is limited by the image accuracy since it estimates the damage just through a reconstructed image. In this paper, a damage estimation method (DEM) is proposed to quantify the damage location and area. First, each damage is fitted into a two-dimensional Gaussian function through edge fitting. Then, the parameters of the Gaussian function are optimized with the two-norm regularization method. Finally, the damage location and area are estimated with the parameters of the Gaussian function. The accuracy of the DEM is directly evaluated in terms of location error and area error. Both simulation and experimental results demonstrated the potential of the proposed method in providing damage estimation information.

Beneficial techniques for spatio-temporal imaging in electrical impedance tomography

OBJECTIVE

Electrical impedance tomography (EIT) typically reconstructs individual images from electrical voltage measurements at pairs of electrodes due to current driven through other electrode pairs on a body. EIT images have low spatial resolution, but excellent temporal resolution. There are four methods for integrating temporal data into an EIT reconstruction: filtering over measurements, filtering over images, combined spatial and temporal (spatio-temporal) regularization, and Kalman filtering. These spatio-temporal methods have not been directly compared, making it difficult to evaluate relative performance and choose an appropriate method for particular use cases.

APPROACH

We (i) develop a common framework, (ii) develop comparison metrics, (iii) perform simulation and tank studies which directly compare algorithms, and (iv) report on relative advantages of the different algorithms.

MAIN RESULTS

Temporal filtering is well understood, but often not considered as part of the imaging process despite a direct impact on image reconstruction quality. Spatio-temporal regularized techniques are not yet efficient but offer tantalizing advantages. Kalman filtering enables adaptive filtering for time-varying measurement/image noise at the cost of often over-regularized (sub-optimal) images which can now be understood in the same framework as the other techniques. Further research into efficient implementations of Gauss-Newton spatio-temporal regularization will allow temporal and spatial covariance to be explicitly defined for longer time series (n > 10 frames) where temporal regularization can be more effective. For the immediate analysis of temporally varying images, we recommend the use of adaptive (time-varying) temporal filtering of measurements followed by adaptive spatial regularization (hyperparameter selection) as the most computationally efficient and effective approach currently available.

SIGNIFICANCE

The analysis of variation within regions of an EIT image to extract physiological measures (functional imaging), has become an important EIT technique where temporal and spatial aspects of analysis are tightly integrated. This work gives guidance on available methods and suggests directions for future research.

Modified sparse regularization for electrical impedance tomography

Electrical impedance tomography (EIT) aims to estimate the electrical properties at the interior of an object from current-voltage measurements on its boundary. It has been widely investigated due to its advantages of low cost, non-radiation, non-invasiveness, and high speed. Image reconstruction of EIT is a nonlinear and ill-posed inverse problem. Therefore, regularization techniques like Tikhonov regularization are used to solve the inverse problem. A sparse regularization based on L1 norm exhibits superiority in preserving boundary information at sharp changes or discontinuous areas in the image. However, the limitation of sparse regularization lies in the time consumption for solving the problem. In order to further improve the calculation speed of sparse regularization, a modified method based on separable approximation algorithm is proposed by using adaptive step-size and preconditioning technique. Both simulation and experimental results show the effectiveness of the proposed method in improving the image quality and real-time performance in the presence of different noise intensities and conductivity contrasts.

Functional validation and comparison framework for EIT lung imaging

INTRODUCTION

Electrical impedance tomography (EIT) is an emerging clinical tool for monitoring ventilation distribution in mechanically ventilated patients, for which many image reconstruction algorithms have been suggested. We propose an experimental framework to assess such algorithms with respect to their ability to correctly represent well-defined physiological changes. We defined a set of clinically relevant ventilation conditions and induced them experimentally in 8 pigs by controlling three ventilator settings (tidal volume, positive end-expiratory pressure and the fraction of inspired oxygen). In this way, large and discrete shifts in global and regional lung air content were elicited.

METHODS

We use the framework to compare twelve 2D EIT reconstruction algorithms, including backprojection (the original and still most frequently used algorithm), GREIT (a more recent consensus algorithm for lung imaging), truncated singular value decomposition (TSVD), several variants of the one-step Gauss-Newton approach and two iterative algorithms. We consider the effects of using a 3D finite element model, assuming non-uniform background conductivity, noise modeling, reconstructing for electrode movement, total variation (TV) reconstruction, robust error norms, smoothing priors, and using difference vs. normalized difference data.

RESULTS AND CONCLUSIONS

Our results indicate that, while variation in appearance of images reconstructed from the same data is not negligible, clinically relevant parameters do not vary considerably among the advanced algorithms. Among the analysed algorithms, several advanced algorithms perform well, while some others are significantly worse. Given its vintage and ad-hoc formulation backprojection works surprisingly well, supporting the validity of previous studies in lung EIT.

Frequency-domain reconstruction of signals in electrical bioimpedance spectroscopy

The use of an amplitude/phase retrieval algorithm in electrical bioimpedance spectroscopy (EIS) that allows a new technique to reconstruct the impedance spectrum in the frequency-domain is reported. To the authors' knowledge this is the first time the proposed algorithm has been used to calculate the modulus or phase of a bioimpedance in EIS from one of these two experimentally obtained parameters. The algorithmic technique is demonstrated in EIS, when wide-bandwidth amplifiers,phase-detectors, and high speed converters determine spectra over frequencies up to 500 kHz at isolated points in the frequency interval. Simulated data from bioimpedance models (Cole and 2R1C circuit impedance functions) and experimental data from a known electrical impedance are used to show the applicability and limitations of the technique with a phase retrieval and a modulus retrieval algorithm.Results comparing this technique with the Kramers-Kronig technique that retrieves the imaginary part of an impedance from its real part are also discussed.