Noise effect analysis and pulmonary perfusion estimation in electrical impedance tomography

Electrical Impedance Tomography (EIT) is a non-invasive imaging technique used to monitor mechanically ventilated patients at the bedside. This study focuses on evaluating the estimation of pulmonary perfusion and the effects of two common noise sources on the outcomes of a hypertonic saline contrast bolus procedure. We used the first-pass kinetics modeling in EIT data to estimate purely lung and hybrid pixels. Furthermore, we analyzed how signal drift and cardiac partial volume effect can yield misleading outcomes. If left uncompensated, the drift noise showed an overestimation of 33%, while the cardiac partial volume effect showed an underestimation of 13.9% in the maximum slope value. We evaluated the model performance using simulated and actual data within physiologically feasible limits.