• Bartłomiej Grychtol and Andy Adler.
• German Cancer Research Center (DKFZ), Heidelberg, Germany. b.grychtol@dkfz.de
• Physiol Meas. 2013 Jun 1; 34 (6): 579-93.

AbstractElectrical impedance tomography (EIT) estimates an image of conductivity change within a body from stimulation and measurement at body surface electrodes. There is significant interest in EIT for imaging the thorax, as a monitoring tool for lung ventilation. To be useful in this application, we require an understanding of if and when EIT images can produce inaccurate images. In this paper, we study the consequences of the homogeneous background assumption, frequently made in linear image reconstruction, which introduces a mismatch between the reference measurement and the linearization point. We show in simulation and experimental data that the resulting images may contain large and clinically significant errors. A 3D finite element model of thorax conductivity is used to simulate EIT measurements for different heart and lung conductivity, size and position, as well as different amounts of gravitational collapse and ventilation-associated conductivity change. Three common linear EIT reconstruction algorithms are studied. We find that the asymmetric position of the heart can cause EIT images of ventilation to show up to 60% undue bias towards the left lung and that the effect is particularly strong for a ventilation distribution typical of mechanically ventilated patients. The conductivity gradient associated with gravitational lung collapse causes conductivity changes in non-dependent lung to be overestimated by up to 100% with respect to the dependent lung. Eliminating the mismatch by using a realistic conductivity distribution in the forward model of the reconstruction algorithm strongly reduces these undesirable effects. We conclude that subject-specific anatomically accurate forward models should be used in lung EIT and extra care is required when analysing EIT images of subjects whose background conductivity distribution in the lungs is known to be heterogeneous or exhibiting large changes.

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