2D Electrical Impedance Tomography Brain Image Reconstruction Using Deep Image Prior

Electrical impedance tomography (EIT) is a medical imaging modality that has the potential to benefit diagnosing, monitoring, and understanding several pathological conditions. However, some regions of the body, such as the brain, are more challenging to reconstruct, demanding improvements before the technique can be used in clinical practice. In this study, we implemented and evaluated an algorithm for 2D static EIT image reconstruction based on the Deep Image Prior (DIP) method. The method was tested in measurements calculated from a computational human head model, where we included a region representing the occurrence of a stroke. The results showed that the DIP-based algorithm had some advantages compared to a more classical method, such as robustness to noise and independence of an initial solution. Therefore, this method could be better suited for real-life EIT image reconstructions.