Fisher information analysis and preconditioning in electrical impedance tomography

In this contribution, it is described how the Fisher information can be computed by using adjoint field techniques, and integrated with the gradient calculations used for optimization in electrical impedance tomography. In particular, the Fisher information can be used as a preconditioner to obtain improved convergence properties and a regularization for quasi-Newton optimization algorithms in electrical impedance tomography. Experimental data have been used to study the possibility of combining a good reconstruction quality with a low system complexity, which is achieved by using a four-electrode measurement technique to avoid the need of high-precision electrode modeling and allowing a very coarse FEM grid. Here, the Fisher information based preconditioning implies a regularization and an algorithm that works well on a coarse FEM grid with very small electrodes modeled as point sources