Different approaches for quantifying ventilation distribution and lung tissue properties by functional EIT

We investigated five different methods which can be applied to quantitatively construct functional tomograms of the lungs. The focus was on the sensitivity of functional tomograms to errors in acquired data. To quantify this sensitivity, theoretical, error-free data sets of well-known properties were artificially generated based on a 'living thorax model'. Physiological time courses and a typical distribution of errors caused by a typical Goe-MF II EIT system were used for the calculations which encompassed a range up to 50 times greater than the initial error level (4 µV_{rms max}–400 µV_{rms max}). Additionally, low-pass filtering and principal component analysis (PCA) were used to quantify the effect of preprocessing the raw data. The results demonstrate that all methods based on fitting the local to the global time course were superior to the common functional tomograms utilizing standard deviation or maximum and minimum detection. Ventilation distribution was best quantified by the so-called VT methods. Filling capacity—a lung tissue property—was least dependent on increasing error levels. The errors introduced by filtering are significant with respect to a quantitative analysis of ventilation distribution. A preprocessing of raw data by applying a PCA performed well on the data sets which had been constructed but were, nonetheless, realistic. This approach appears to be highly promising for application on real data which is known to be erroneous.