Electrical impedance tomography (EIT) has the potential to become a bedside tool for monitoring and guiding ventilator therapy as well as tracking the development of chronic lung diseases. This review article summarizes recent publications (from 2011) dealing with the applications of pulmonary EIT. Original papers on EIT lung imaging in clinical settings are analyzed and divided into several categories according to the lung pathology of the study subjects. Studies on children and infants are presented separately from studies on adult patients. Information on the study objectives and main results, the number of studied patients, the performed ventilatory maneuvers or interventions and the analyzed EIT information is given. Limitations that hinder EIT to become a routinely used tool in a clinical setting are also discussed.
Lung EIT is a functional imaging method that utilizes electrical currents to reconstruct images of conductivity changes inside the thorax. This technique is radiation free and applicable at the bedside, but lacks of spatial resolution compared to morphological imaging methods such as X-ray computed tomography (CT). In this article we describe an approach for EIT image reconstruction using morphologic information obtained from other structural imaging modalities. This leads to recon- structed images of lung ventilation that can easily be superimposed with structural CT or MRI images, which facilitates image interpretation. The approach is based on a Discrete Cosine Transformation (DCT) of an image of the considered transversal thorax slice. The use of DCT enables reduction of the dimensionality of the reconstruction and ensures that only conductivity changes of the lungs are reconstructed and displayed. The DCT based approach is well suited to fuse morphological image information with functional lung imaging at low computational costs. Results on simulated data indicate that this approach preserves the morphological structures of the lungs and avoids blurring of the solution. Images from patient measurements reveal the capabilities of the method and demonstrate benefits in possible applications.
Up to now, the impact of electrode positioning on electrical impedance tomography (EIT) had not been systematically analyzed due to the lack of a reference method. The aim of the study was to determine the impact of electrode positioning on EIT imaging in spontaneously breathing subjects at different ventilation levels with our novel lung function measurement setup combining EIT and body plethysmography. EIT measurements were conducted in three transverse planes between the 3rd and 4th intercostal space (ICS), at the 5th ICS and between the 6th and 7th ICS (named as cranial, middle and caudal) on 12 healthy subjects. Pulmonary function tests were performed simultaneously by body plethysmography to determine functional residual capacity (FRC), vital capacity (VC), tidal volume (VT), expiratory reserve volume (ERV), and inspiratory reserve volume (IRV). Ratios of impedance changes and body plethysmographic volumes were calculated for every thorax plane (ΔIERV/ERV, ΔIVT/VT and ΔIIRV/IRV). In all measurements of a subject, FRC values and VC values differed ≤5%, which confirmed that subjects were breathing at comparable end-expiratory levels and with similar efforts. In the cranial thorax plane the normalized ΔIERV/ERV ratio in all subjects was significantly higher than the normalized ΔIIRV/IRV ratio whereas the opposite was found in the caudal chest plane. No significant difference between the two normalized ratios was found in the middle thoracic plane. Depending on electrode positioning, impedance to volume ratios may either increase or decrease in the same lung condition, which may lead to opposite clinical decisions.
Electrical impedance tomography (EIT) attempts to reveal the conductivity distribution of a domain based on the electrical boundary condition. This is an ill-posed inverse problem; its solution is very unstable. Total variation (TV) regularization is one of the techniques commonly employed to stabilize reconstructions. However, it is well known that TV regularization induces staircase effects, which are not realistic in clinical applications. To reduce such artifacts, modified TV regularization terms considering a higher order differential operator were developed in several previous studies. One of them is called total generalized variation (TGV) regularization. TGV regularization has been successively applied in image processing in a regular grid context. In this study, we adapted TGV regularization to the finite element model (FEM) framework for EIT reconstruction. Reconstructions using simulation and clinical data were performed. First results indicate that, in comparison to TV regularization, TGV regularization promotes more realistic images. Graphical abstract Reconstructed conductivity changes located on selected vertical lines. For each of the reconstructed images as well as the ground truth image, conductivity changes located along the selected left and right vertical lines are plotted. In these plots, the notation GT in the legend stands for ground truth, TV stands for total variation method, and TGV stands for total generalized variation method. Reconstructed conductivity distributions from the GREIT algorithm are also demonstrated.
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