Factorized Projection-Domain Spatio-Temporal Regularization for Dynamic Tomography

Abstract

Dynamic tomography is an ill-posed inverse problem where the object evolves during the sequential acquisition of projections. The goal is to reconstruct the object for each time instant. However, performing a direct reconstruction using this inconsistent set of projections is impossible. In this paper, we propose an object-domain recovery algorithm using a variational formulation that combines a partially separable spatio-temporal prior with a basic total-variation spatial regularization for improved performance, while preserving full interpretability. Numerical experiments on data derived from real object CT data demonstrate the advantages of the proposed algorithm over recent projection-domain and deep-prior-based methods.