GREIT: − Graz consensus Reconstruction algorithm for Electrical Impedance Tomography

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Project Goal

Recently, electrical impedance tomography (EIT) has begun to see a significant clinical interest for monitoring of ventilated patients. The key capability of EIT is to provide real-time images of the distribution of ventilation in the patient's lungs. However, most clinical and physiological research in lung EIT is done using older and proprietary algorithms; this is an obstacle to interpretation of EIT results because the reconstructed images are not well characterized. To address this issue, we are developing a consensus linear reconstruction algorithm for lung EIT, called GREIT (Graz consensus Reconstruction algorithm for EIT). This algorithm is being developed in three phases: 1) selection of the "ingredients" and evaluation methodology (see our paper at EIT Conference, 2008), 2) evaluation and experience with GREIT variants, and 3) consensus and definition of the GREIT algorithm. Algorithms evaluation criteria are identified to be: a) quantitative output for all positions, b) reconstructed position error (low and uniform), c) resolution (small PSF, uniform, few artefacts), d) good noise performance, e) low sensitivity to electrode and boundary movement, f) good performance on clinical and experimental data. This approach represents the consensus of a large and representative group of experts in EIT algorithm and clinical applications. All software and data to implement and test GREIT will be made available under an open source license which allows free research and commercial use.

We have published a paper describing the GREIT algorithm framework GREIT: a unified approach to 2D linear EIT reconstruction of lung images Physiol. Meas, 30:S35-S55, 2009.

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