Electrical impedance tomography (EIT) is a medical technology allowing continuous chest imaging without the use of radiation directly at the patients´ bedside. It is currently considered to be the only method that could potentially produce lung images in a remote setting (e.g. in patients´ homes) because it can be integrated into wearable technology. The aim of the WELMO project is to develop and validate such a wearable with integrated EIT.
EIT can generate chest images at very high scan rates. The series of primary EIT images that is acquired during the examination reflects the continuous changes in electrical properties of the lung tissue that are related to ventilation. The distribution of air in the lungs is not homogeneous. It is negatively impacted by lung diseases but it may also change in response to therapy. How can the recorded EIT images be used to characterise the distribution of inspired air in the lungs?
Three different approaches can be identified: 1) visualisation of all images in form of a movie, 2) generation of functional EIT images and 3) calculation of lung-function specific quantitative EIT measures. EIT movie gives an overall impression of how air is distributed in the lungs but it is not quite suitable for quantitative assessment of regional lung function and clinical decision-making. With this respect, the functional EIT images and measures derived from such images are far more relevant.
Several types of functional EIT images can be generated from the same sequence of primary EIT images allowing the characterisation of distinct features of regional lung function. For instance, a functional EIT image presenting the tidal variation of the EIT signal in all image pixels can be applied to describe the distribution of air in the lungs during single breaths. Based on these images, quantitative parameters can be generated that characterise the overall degree of ventilation inhomogeneity like the coefficient of variation (CV) calculated from all image pixel values (Figure 1) or the global inhomogeneity index. Other parameters, like the fraction of ventilation in different regions-of-interest (Figure 1) or the centre of ventilation describe the ventilation distribution among predefined lung areas. The example functional EIT image in the left panel of Figure 1 shows more homogeneously distributed ventilation with a lower value of CV than in the image in the right panel where, moreover, ventilation is preferentially distributed to the ventral (top) part of the image. This is clearly reflected by the other EIT measure, the dorsal fraction of ventilation.
The presented EIT measures acquired during quiet tidal breathing but also during ventilation manoeuvres that are typically performed by the patients during conventional lung function examination can be applied to describe the lung function status on a regional level, assess its deterioration during disease exacerbations, follow the natural disease history, and determine the effects of therapy.
Christian-Albrechts-Universität zu Kiel