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We propose a statistical learning approach to enhance contrast in EIT imaging of the brain. The conductivity is modelled as a combination of two unknowns, spatially distributed background and the skull layer. The discrepancy between the forward models with and without the skull layer is treated as a modelling error which is handled by the Bayesian approximation error approach. The approach is evaluated with simulations and phantom data.
This presentation is part of Minisymposium “MS20 - Advances in Reconstruction Methods for Electrical Impedance Tomography (3 parts)”
organized by: Melody Alsaker (Gonzaga University) , Samuli Siltanen (University of Helsinki) .