Determination of lung area in electrical impedance tomography images

Introduction

Electrical impedance tomography (EIT) has the potential for bedside monitoring of regional lung function. Evaluation of EIT imaging requires the identification of the lung area in the images. A functional EIT-based method (fEIT) has been proposed to identify the lung area in EIT images for patients with healthy lungs [1, 2]. However, in patients with certain lung diseases, the fEIT method will fail to include those lung regions where a low ventilation change is present. Besides, identified lung regions may include the cardiac-related area. A method to estimate the lung area accurately is missing. The aim of this study was to develop an improved method for lung area estimation in EIT images (LAE), which is suitable for both healthy subjects and patients with serious pulmonary diseases.

Methods

In our LAE method, the lung area as determined by fEIT is mirrored and the cardiac-related area, which is distinguished in the frequency domain, is subtracted. Forty-nine mechanically ventilated patients were investigated (test group: 39 patients, thoracic surgery; control group: 10 patients, orthopedic surgery without pulmonary disease). An EIT video sequence of 5 minutes duration comprising about 60 breathing cycles from every participant was recorded and subsequently analyzed. Statistical analysis was performed by one-way ANOVA. P < 0.01 was considered statistically significant. Data are presented as means and standard deviations.

Results

It is assumed that the fraction of the lung in the thorax for different people should be more or less in the same range, in spite of the state of the lung. The sizes of the lung area determined with fEIT are in control group S_C, fEIT = 361 ± 35.1 and in test group S_T, fEIT = 299 ± 60.8 (P < 0.01). On the contrary, the sizes estimated with the LAE method are in control group S_C, LAE = 353 ± 27.2 and in test group S_T, LAE = 353 ± 61.1 (P = 0.41).

Conclusion

The result demonstrates that the novel LAE method can better access the lung region in EIT images, from which the analysis of regional lung ventilation will benefit. Further validation will be pursued by comparing the results with anatomic computed tomography image of the chest morphology.