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Electrical Impedance Tomography (EIT) is a medical imaging technique that is primarily used to monitor the respiration of a patient. Because EIT is based on electrical measurements, it is a safe, non-invasive, and cost-effective imaging technique. However, the EIT image reconstruction is a severely ill-posed problem that gives low spatial resolution where only large variations in tissue conductivity can be visualized. Furthermore, widely used time difference EIT relies on a single frequency alternating current measurement which does not allow for discrimination of different tissues on their conductivity spectra. Here we show the application of a new EIT reconstruction algorithm which correlates measurements taken at different frequencies to include the spectral dependency of the tissue properties. The algorithm is tested on a simulated phantom using data for muscle and lung tissue from the literature. It shows that contrary to a standard EIT image reconstruction, the frequency dependence of the tissues is retained, which can be used to further improve distinguishability in EIT images.

The apnea test (AT) is a critical procedure in determining brain death. It temporarily disconnects the patient from the ventilator for 7 to 10 minutes. This study explores pulsatile Electrical Impedance Tomography (EIT) during AT in 10 patients suspected of brain death to discern physiological changes. Analysis revealed three distinct patterns of pulsatile EIT signal changes: increasing; fluctuating; and decreasing. These findings show the potential of pulsatile EIT monitoring to provide novel insight into cardio-pulmonary dynamics during AT.

Electrical Impedance Tomography (EIT) has shown promising results as a low-cost imaging method for visualizing ventilation distribution within the lungs. However, clinical interpretation of EIT images is often hindered by blurred anatomical alignment and reconstruction artifacts. Integrating structural priors into the EIT reconstruction process has the potential to enhance the interpretability of the EIT images. Thus, a patient-specific structural prior mask is introduced in this contribution, which restricts the reconstruction of conductivity changes within the lung regions.We conducted numerical simulations on four finite element models representing four different thorax excursions to investigate the impact of the structural prior mask on EIT images. Simulations were performed under four different ventilation statuses. EIT images were reconstructed using the Gauss-Newton and discrete cosine transform-based EIT algorithms.We conducted a quantitative analysis using figures of merit to evaluate the images of the two reconstruction algorithms. The results show the structural prior mask preserves the morphological structures of the lungs and limits reconstruction artifacts.

Electrical impedance tomography (EIT) has the potential for monitoring perfusion in addition to respiration on the bedside. Several separation methods were reported, e.g. Filtering, ECG-gating and PCA. However, the separation is not trivial, which is why harmonic analysis was introduced in EIT data analysis. It is based on several assumptions and therefore prove of plausibility is necessary. In this contribution a two-dimensional thorax simulation is introduced, which includes simplified lungs and heart with changing shapes and conductivities due to volume variations and lung perfusion. Harmonic analysis was applied on the simulated data. The separation results are in good agreement with the simulation settings. Further investigations will be required due to limitations of the simulation. Nevertheless, harmonic analysis delivered precise results in ideal and strictly defined conditions.

Electrical Impedance Tomography is used to image the cross-sectional conductivity of an object. It is clinically used for high-frame-rate imaging of lung ventilation. Most current systems use very limited current injection waveforms and patterns. We have developed a flexible system for researching alternatives. This paper covers our voltage conditioning module's design and characterisation. The device was shown to work up to the desired 10 MHz. The responses were uniform with minor gain mismatch between all channels after manufacturing variations. The low-frequency (<1 MHz) crosstalk is on the order -55 dB, and the worst cases around 8 MHz are around -35 dB. [ABSTRACT FROM AUTHOR]

Morphological prior information incorporated with the discrete cosine transformation (DCT) based electrical impedance tomography (EIT) algorithm can improve the interpretability of EIT reconstructions in clinical applications. However, an outdated structural prior can yield a misleading reconstruction compromising the accuracy of the clinical diagnosis and the appropriate treatment decision. In this contribution, we propose a redistribution index scaled between 0 and 1 to quantify the possible error in a DCT-based EIT reconstruction influenced by structural prior information. Two simulation models of different tissue atelectasis and collapsed ratios were investigated. Outdated and updated structural prior information were applied to obtain different EIT reconstructions using this simulated data, with which the redistribution index was calculated and compared. When the difference between prior and reality (the redistribution index) became larger and exceeded a threshold, this was considered as an indicator of an outdated prior information. The evaluation result shows the potential of the redistribution index to detect outdated prior information in a DCT-based EIT algorithm.

The COVID-19 is a viral infection that causes respiratory complications. Infected lungs often present ground glass opacities, thus suggesting that medical imaging technologies could provide useful information for the disease diagnosis, treatment, and posterior recovery. The Electrical Impedance Tomography (EIT) is a non-invasive, radiationfree, and continuous technology that generates images by using a sequence of current injections and voltage measurements around the body, making it very appropriate for the study to monitor the regional behaviour of the lung. Moreover, this tool could also be used for a preliminary COVID-19 phenotype classification of the patients. This study is based on the monitoring of lung compliances of two COVID-19-infected patients: the results indicate that one of them could belong to the H-type, while the other is speculated belongs to L-type. It has been concluded that the EIT is a useful tool to obtain information regarding COVID-19 patients and could also be used to classify different phenotypes.

COVID-19 induced acute respiratory distress syndrome (ARDS) could have two different phenotypes, which might have different response and outcome to the traditional ARDS positive end-expiration pressure (PEEP) treatment. The identification of the different phenotypes in terms of the PEEP recruitment can help improve the patients’ outcome. In this contribution we reported a COVID-19 patient with seven-day electrical impedance tomography monitoring. From the conductivity distribution difference image analysis of the data, a clear course developing trend can be observed in addition to the phenotype identification. This case might suggest that EIT can be a practical tool to identify phenotypes and to provide progressive information of COVID-19 pneumonia.

Structural prior information can improve electrical impedance tomography (EIT) reconstruction. In this contribution, we introduce a discrete cosine transformation-based (DCT-based) EIT reconstruction algorithm to demonstrate a way to incorporate the structural prior with the EIT reconstruction process. Structural prior information is obtained from other available imaging methods, e.g., thorax-CT. The DCT-based approach creates a functional EIT image of regional lung ventilation while preserving the introduced structural information. This leads to an easier interpretation in clinical settings while maintaining the advantages of EIT in terms of bedside monitoring during mechanical ventilation. Structural priors introduced in the DCT-based approach are of two categories in terms of different levels of information included: a contour prior only differentiates lung and non-lung region, while a detail prior includes information, such as atelectasis, within the lung area. To demonstrate the increased interpretability of the EIT image through structural prior in the DCT-based approach, the DCT-based reconstructions were compared with reconstructions from a widely applied one-step Gauss-Newton solver with background prior and from the advanced GREIT algorithm. The comparisons were conducted both on simulation data and retrospective patient data. In the simulation, we used two sets of forward models to simulate different lung conditions. A contour prior and a detail prior were derived from simulation ground truth. With these two structural priors, the reconstructions from the DCT-based approach were compared with the reconstructions from both the one-step Gauss-Newton solver and the GREIT. The difference between the reconstructions and the simulation ground truth is calculated by the ℓ2-norm image difference. In retrospective patient data analysis, datasets from six lung disease patients were included. For each patient, a detail prior was derived from the patient's CT, respectively. The detail prior was used for the reconstructions using the DCT-based approach, which was compared with the reconstructions from the GREIT. The reconstructions from the DCT-based approach are more comprehensive and interpretable in terms of preserving the structure specified by the priors, both in simulation and retrospective patient data analysis. In simulation analysis, the ℓ2-norm image difference of the DCT-based approach with a contour prior decreased on average by 34% from GREIT and 49% from the Gauss-Newton solver with background prior; for reconstructions of the DCT-based approach with detail prior, on average the ℓ2-norm image difference is 53% less than GREIT and 63% less than the reconstruction with background prior. In retrospective patient data analysis, the reconstructions from both the DCT-based approach and GREIT can indicate the current patient status, but the DCT-based approach yields more interpretable results. However, it is worth noting that the preserved structure in the DCT-based approach is derived from another imaging method, not from the EIT measurement. If the structural prior is outdated or wrong, the result might be misleadingly interpreted, which induces false clinical conclusions. Further research in terms of evaluating the validity of the structural prior and detecting the outdated prior is necessary. [ABSTRACT FROM AUTHOR]

Electrical Impedance Tomography (EIT) is a low-cost imaging method which reconstructs two-dimensional cross-sectional images, visualising the impedance change within the thorax. However, the reconstruction of an EIT image is an ill-posed inverse problem. In addition, blurring, anatomical alignment, and reconstruction artefacts can hinder the interpretation of EIT images. In this contribution, we introduce a patient-specific structural prior mask into the EIT reconstruction process, with the aim of improving image interpretability. Such a prior mask ensures that only conductivity changes within the lung regions are reconstructed. To evaluate the influence of the introduced structural prior mask, we conducted numerical simulations with two scopes in terms of their different ventilation statuses and varying atelectasis scales. Quantitative analysis, including the reconstruction error and figures of merit, was applied in the evaluation procedure. The results show that the morphological structures of the lungs introduced by the mask are preserved in the EIT reconstructions and the reconstruction artefacts are decreased, reducing the reconstruction error by 25.9% and 17.7%, respectively, in the two EIT algorithms included in this contribution. The use of the structural prior mask conclusively improves the interpretability of the EIT images, which could facilitate better diagnosis and decision-making in clinical settings. [ABSTRACT FROM AUTHOR]

Structural priors incorporated with the discrete cosine transformation (DCT) based electrical impedance tomography (EIT) algorithm improves the interpretability of EIT results in a clinical setting. However, a false prior will produce a misleading result compromising clinical decisions. Detection of false priors is crucial. In our investigation, a redistribution index (RI) is proposed to quantify the potential error introduced by a false structural prior. Simulations in terms of different scales of atelectasis were conducted to evaluate the plausibility of the RI. The behaviour of RI makes it possible to detect a false prior with a defined threshold, which must be determined according to the requirements and tolerance of an application. The redistribution index depends also on the reconstruction method and different parameters like the regularization hyperparameter. Therefore, we investigated as well the sensitivity of the RI on the choice of the hyperparameter. From the evaluation, it seems plausible that the RI can be further developed into an indicator for false priors in EIT reconstruction. [ABSTRACT FROM AUTHOR]