Regional-ventilation-delay can be noninvasively measured by electrical impedance tomography during a slow inflation of 12 mL/kg of body weight and visualized using ventilation delay maps. Our experimental data suggest that the impedance tomography-based analysis of regional-ventilation-delay inhomogeneity provides a good estimate of the amount of tidal recruitment and may be useful to individualize ventilatory settings.

EIT is suitable for monitoring the dynamic effects of PEEP variations on the regional change of tidal volume. It is superior to global ventilation parameters in assessing the beginning of alveolar recruitment and lung collapse.

Dynamic thoracic EIT is capable of detecting changes of the ventilation distribution in the lung. Nevertheless, it has yet to become an established clinical tool. Therefore, it is necessary to consider application scenarios wherein fast and distinct changes of the tissue conductivities are to be found and also have a clear diagnostic significance. One such a scenario is the artificial ventilation of patients suffering from the acute respiratory distress syndrome (ARDS). New protective ventilation strategies involving recruitment manoeuvres are associated with noticeable shifts of body fluids and regional ventilation, which can quite easily be detected by EIT. The bedside assessment of these recruitment manoeuvres will help the attending physician to optimize treatment. Hence, we performed an animal study of lavage-induced lung failure and investigated if EIT is capable of qualitatively as well as quantitatively monitoring lung recruitment during a stepwise PEEP trial. Additionally, we integrated EIT into a fuzzy controller-based ventilation system which allows one to perform automated recruitment manoeuvres (open lung concept) based on online PaO2 measurements. We found that EIT is a useful tool to titrate the proper PEEP level after fully recruiting the lung. Furthermore, EIT seems to be able to determine the status of recruitment when combining it with other physiological parameters. These results suggest that EIT may play an important role in the individualization of protective ventilation strategies.

Background: Anesthesia per se and pneumoperitoneum during laparoscopic surgery lead to atelectasis and impairment of oxygenation. We hypothesized that a ventilation with positive end-expiratory pressure (PEEP) during general anesthesia and laparoscopic surgery leads to a more homogeneous ventilation distribution as determined by electrical impedance tomography (EIT). Furthermore, we supposed that PEEP ventilation in lung-healthy patients would improve the parameters of oxygenation and respiratory compliance. Methods: Thirty-two patients scheduled to undergo laparoscopic cholecystectomy were randomly assigned to be ventilated with ZEEP (0 cmH 2 O) or with PEEP (10 cmH 2 O) and a subsequent recruitment maneuver. Differences in regional ventilation were analyzed by the EIT-based center-of-ventilation index (COV), which quantifies the distribution of ventilation and indicates ventilation shifts. Results: Higher amount of ventilation was examined in the dorsal parts of the lungs in the PEEP group. Throughout the application of PEEP, a lower shift of ventilation was found, whereas after the induction of anesthesia, a remark-

In spontaneously breathing or ventilated subjects, it is difficult to image cardiac-related conductivity changes using electrical impedance tomography (EIT) due to the high amplitude of the ventilation component. Previous attempts to separate these components included either electrocardiogram-gated averaging, frequency domain filtering or holding the breath while performing the measurements. However, such methods are either not able to produce continuous real-time images or to fully separate cardiac and pulmonary changes. The aim of this work was to develop a new dynamic filtering method for the online separation of pulmonary and cardiac changes avoiding the drawbacks of the previous attempts. The approach is based on estimating template functions for the pulmonary and cardiac components by means of principal component analysis and frequency domain filtering. Then, these templates are fitted into the input signals. The new method enables an observer to examine the variation of the cardiac signal beat-by-beat after a one-time setup period of 20 s. Preliminary in vivo results of two healthy subjects are presented. The results are superior to frequency domain filtering and in good agreement with signals averaged over several cardiac cycles. The method does not depend on ECG or other a priori knowledge. The apparent validity of the method's ability to separate cardiac and pulmonary changes in EIT images was shown and has to be confirmed in future studies. The algorithm opens up new possibilities for future clinical trials on continuous monitoring by means of EIT and for the examination of the relation between the cardiac component and lung perfusion.