Quantification of ventilation by time series FIT data

An established procedure in functional EIT (f-EIT) to image ventilation is the calculation of the standard deviation of the local impedance variation over a series of tomograms for each pixel. We present an improved approach based on the raw data. It saves computing time and is suitable for &EIT and absolute EIT (a-EIT) just as well. Furthermore, tidal volume, end-expiratory and end-inspiratory ventilatory level can be quantified separately. From the raw data we calculate the time course of the global relative impedance changes. It is used to determine separately the indices (frame number) of all frames that correspond to the maximum (inspiratory) and minimum (expiratory) level of ventilation. According to these indices two data sets of raw data are generated: one mean inspiratory and one mean expiratory frame. Based on these two frames functional images of ventilation or absolute images of lung tissue resistivity can be calculated representing the whole time series. These functional images of ventilation now correspond directly to the tidal volume of respiration which is more common in physiology than standard deviation. We have quantified ventilation of 10 healthy subjects ( 4 female, 6 male) during normal breathing and have found that the image quality of ventilatory tomograms is significantly ((p < 0.001) improved by the presented approach. This is achieved by suppression of non ventilatory impedance changes, artefacts and noise.