Shape deformation in two-dimensional electrical impedance tomography

Model Selection Based Algorithm in Neonatal Chest EIT

This paper presents a new method for selecting a patient specific forward model to compensate for anatomical variations in electrical impedance tomography (EIT) monitoring of neonates. The method uses a combination of shape sensors and absolute reconstruction. It takes advantage of a probabilistic approach which automatically selects the best estimated forward model fit from pre-stored library models. Absolute/static image reconstruction is performed as the core of the posterior probability calculations. The validity and reliability of the algorithm in detecting a suitable model in the presence of measurement noise is studied with simulated and measured data from 11 patients. The paper also demonstrates the potential improvements on the clinical parameters extracted from EIT images by considering a unique case study with a neonate patient undergoing computed tomography imaging as clinical indication prior to EIT monitoring. Two well-known image reconstruction techniques, namely GREIT and tSVD, are implemented to create the final tidal images. The impacts of appropriate model selection on the clinical extracted parameters such as center of ventilation and silent spaces are investigated. The results show significant improvements to the final reconstructed images and more importantly to the clinical EIT parameters extracted from the images that are crucial for decision-making and further interventions.

Towards a thoracic conductive phantom for EIT

Phantom experiments are a crucial step for testing new hardware or imaging algorithms for electrical impedance tomography (EIT) studies. However, constructing an accurate phantom for EIT research remains critical; some studies have attempted to model the skull and breasts, and even fewer, as yet, have considered the thorax. In this study, a critical comparison between the electrical properties (impedance) of three materials is undertaken: a polyurethane foam, a silicone mixture and a thermoplastic polyurethane filament. The latter was identified as the most promising material and adopted for the development of a flexible neonatal torso. The validation is performed by the EIT image reconstruction of the air filled cavities, which mimic the lung regions. The methodology is reproducible for the creation of any phantom that requires a slight flexibility.

Noise-robust bioimpedance approach for cardiac output measurement

OBJECTIVE

Congestive heart failure is a problem affecting millions of Americans. A continuous, non-invasive, telemonitoring device that can accurately monitor cardiac metrics could greatly help this population, reducing unnecessary hospitalizations and cost.

APPROACH

Machine learning (ML) algorithms trained on electrical-impedance tomography (EIT) data are presented for portable cardiac monitoring. The approach was validated on a simulated thorax and a measured tank experiment. A highly detailed 4D chest model (finite element method mesh and conductivity profiles) was developed utilizing the 4D XCAT phantom to provide realistic data. The ML algorithms were trained using databases that assumed the presence of poorly contacting electrodes without any assumptions of knowing which electrodes would be bad in the experiment. The trained ML algorithms were compared to EIT evaluated with and without removing bad electrodes.

MAIN RESULTS

A regression support vector machine and a deep neural network (DNN) were found to be the most accurate and robust to poorly contacting electrodes while not needing to know which electrodes were in poor contact in the simulated and measured experiments, respectively.

SIGNIFICANCE

Although the ML algorithms are not always better than EIT (with bad electrodes removed), the comparable results without needing a priori knowledge of which electrodes are bad is seen as a very promising feature. An evaluation of computational costs showed that the DNN required comparable computational power to the other methods while requiring less memory, which could make the DNNs an attractive algorithm for a low-power, portable system. This work represents an important validation of the method using measured data, and model development, which is needed to apply this method on real clinical data. Additionally, the developed 4D simulated thorax model could be an important tool within the EIT community.

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

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.

A Parametric Level Set-Based Approach to Difference Imaging in Electrical Impedance Tomography

This paper presents a novel difference imaging approach based on the recently developed parametric level set (PLS) method for estimating the change in a target conductivity from electrical impedance tomography measurements. As in conventional difference imaging, the reconstruction of conductivity change is based on data sets measured from the surface of a body before and after the change. The key feature of the proposed approach is that the conductivity change to be reconstructed is assumed to be piecewise constant, while the geometry of the anomaly is represented by a shape-based PLS function employing Gaussian radial basis functions (GRBFs). The representation of the PLS function by using GRBF provides flexibility in describing a large class of shapes with fewer unknowns. This feature is advantageous, as it may significantly reduce the overall number of unknowns, improve the condition number of the inverse problem, and enhance the computational efficiency of the technique. To evaluate the proposed PLS-based difference imaging approach, results obtained via simulation, phantom study, and in vivo pig data are studied. We find that the proposed approach tolerates more modeling errors and leads to a significant improvement in image quality compared with the conventional linear approach.

Compensation for large thorax excursions in EIT imaging

Besides the application of EIT in the intensive care unit it has recently also been used in spontaneously breathing patients suffering from asthma bronchiole, cystic fibrosis (CF) or chronic obstructive pulmonary disease (COPD). In these cases large thorax excursions during deep inspiration, e.g. during lung function testing, lead to artifacts in the reconstructed images. In this paper we introduce a new approach to compensate for image artifacts resulting from excursion induced changes in boundary voltages. It is shown in a simulation study that boundary voltage change due to thorax excursion on a homogeneous model can be used to modify the measured voltages and thus reduce the impact of thorax excursion on the reconstructed images. The applicability of the method on human subjects is demonstrated utilizing a motion-tracking-system. The proposed technique leads to fewer artifacts in the reconstructed images and improves image quality without substantial increase in computational effort, making the approach suitable for real-time imaging of lung ventilation. This might help to establish EIT as a supplemental tool for lung function tests in spontaneously breathing patients to support clinicians in diagnosis and monitoring of disease progression.

Correcting electrode modelling errors in EIT on realistic 3D head models

Electrical impedance tomography (EIT) is a promising medical imaging technique which could aid differentiation of haemorrhagic from ischaemic stroke in an ambulance. One challenge in EIT is the ill-posed nature of the image reconstruction, i.e., that small measurement or modelling errors can result in large image artefacts. It is therefore important that reconstruction algorithms are improved with regard to stability to modelling errors. We identify that wrongly modelled electrode positions constitute one of the biggest sources of image artefacts in head EIT. Therefore, the use of the Fréchet derivative on the electrode boundaries in a realistic three-dimensional head model is investigated, in order to reconstruct electrode movements simultaneously to conductivity changes. We show a fast implementation and analyse the performance of electrode position reconstructions in time-difference and absolute imaging for simulated and experimental voltages. Reconstructing the electrode positions and conductivities simultaneously increased the image quality significantly in the presence of electrode movement.

Electrical impedance tomography imaging of the cardiopulmonary system

PURPOSE OF REVIEW

This review article summarizes the recent advances in electrical impedance tomography (EIT) related to cardiopulmonary imaging and monitoring on the background of the 30-year development of this technology.

RECENT FINDINGS

EIT is expected to become a bedside tool for monitoring and guiding ventilator therapy. In this context, several studies applied EIT to determine spatial ventilation distribution during different ventilation modes and settings. EIT was increasingly combined with other signals, such as airway pressure, enabling the assessment of regional respiratory system mechanics. EIT was for the first time used prospectively to define ventilator settings in an experimental and a clinical study. Increased neonatal and paediatric use of EIT was noted. Only few studies focused on cardiac function and lung perfusion. Advanced radiological imaging techniques were applied to assess EIT performance in detecting regional lung ventilation. New approaches to improve the quality of thoracic EIT images were proposed.

SUMMARY

EIT is not routinely used in a clinical setting, but the interest in EIT is evident. The major task for EIT research is to provide the clinicians with guidelines how to conduct, analyse and interpret EIT examinations and combine them with other medical techniques so as to meaningfully impact the clinical decision-making.