  This paper focuses on prior information for improved sparsity reconstruction
in electrical impedance tomography with partial data, i.e. data measured only
on subsets of the boundary. Sparsity is enforced using an $\ell_1$ norm of the
basis coefficients as the penalty term in a Tikhonov functional, and prior
information is incorporated by applying a spatially distributed regularization
parameter. The resulting optimization problem allows great flexibility with
respect to the choice of measurement boundaries and incorporation of prior
knowledge. The problem is solved using a generalized conditional gradient
method applying soft thresholding. Numerical examples show that the addition of
prior information in the proposed algorithm gives vastly improved
reconstructions even for the partial data problem. The method is in addition
compared to a total variation approach.
