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Updated: Jun 29, 2026
Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit
Published on: September 6, 2024
Performance of electrical impedance tomography in detecting regional tidal volumes during one-lung ventilation
1Department of Anaesthesiology and Intensive Care Medicine, University Medical Center of Schleswig-Holstein, Campus Kiel, Kiel, Germany. pulletz@anaesthesie.uni-kiel.de
Background:
Electrical impedance tomography (EIT) is becoming a new medical imaging modality for continuous monitoring of regional lung function in the intensive care unit or operating room. The aim of our study was to evaluate the performance of EIT in detecting regional tidal volumes in patients during volume-controlled mechanical ventilation of one or both lungs.
Methods:
Ten adult patients undergoing elective thoracic surgery were included. EIT measurements were performed with the Goe-MF II EIT system. Data were collected before surgery during ventilation of both, the right and left lungs. Tidal volumes of 800 and 400 ml were applied during bilateral and unilateral ventilation, respectively.
Results:
Ventilation-related impedance changes determined in the whole chest cross-section during the right and left lung ventilation did not significantly differ from each other and were equal to 47.6+/-5.6% and 48.5+/-7.8% (mean+/-SD) of the value determined during bilateral ventilation. During unilateral ventilation, EIT clearly separated the ventilated and non-ventilated lung regions; nevertheless, ventilation-related impedance changes were also detected at the non-ventilated sides in areas corresponding to 3.4+/-4.1% and 12.4+/-6.9% of the scan halves during ventilation of the left and right lung, respectively. Changes in global tidal volumes were adequately detected by EIT during both bilateral and unilateral lung ventilation.
Conclusion:
Although good separation of the ventilated and non-ventilated sides of the chest was possible, the data indicate that reliable quantification of regional tidal volumes during asymmetric or inhomogeneous distribution patterns requires regions-of-interest analysis.
