• G Hahn, J Dittmar, A Just, and G Hellige.
• Department of Anaesthesiological Research, University of Göttingen, Göttingen, Germany. ghahn@gwdg.de
• Physiol Meas. 2008 Jun 1;29(6):S51-61.

AbstractWe present an improved approach to image ventilation in functional electrical impedance tomography (f-EIT). It combines the advantages of the two established procedures of calculating standard deviation as a functional parameter of ventilation (SD method) and the so-called filling capacity (FC method). The SD method quantifies the local impedance variation over a series of tomograms for each pixel; the FC method is based on the slope of a linear fit of regional versus the global impedance change. Tidal volume V(T) is displayed linearly by the SD method in f-EIT; it is, however, sensitive to noisy data. The FC method is much more robust with respect to noise but does not display the tidal volume V(T). We combined the advantages of both techniques in a new VT method which is based on raw data. It saves computing time and is suitable for both f-EIT and absolute EIT (a-EIT). We separated the raw data into two representative sets: end expiratory and end inspiratory. This was accomplished by calculating the global time course of the relative impedance changes from the raw data. In this time course, we determined all frame numbers (indices) of end expiration and end inspiration. These frame numbers were used to calculate one mean expiratory and one mean inspiratory raw data frame. Reconstruction by difference imaging directly reflects the mean tidal volume V(T) during the acquired frame series. The effect of the improvement by the VT method was investigated at different noise levels by adding artificial noise from 0 to 100 microV(rms) to a real raw dataset. The robustness with regard to noise of the VT method was similar to that of the FC method. The practical value of suppression of non-ventilatory impedance changes, artefacts and noise was tested by studying ten healthy subjects (four females, six males) during normal breathing. We found a highly significant improvement in the image quality (p < 0.001) of ventilation for this group of volunteers.

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