System Parameter Estimation in Tomographic Inverse Problems

Inverse problems are typically solved under the assumption of known geometric system parameters describing the forward problem. Should such information be unavailable or inexact, the estimation of these parameters from only observed sensor data may be necessary prior to reconstruction of the desired signal. We demonstrate the feasibility of such estimation via maximum-likelihood methods for the system parameters with expectation-maximization as an optimization mechanism within a Bayesian estimation framework for the final reconstruction problem.