Curve Evolution Methods for Dynamic Tomography with Unknown Dynamic Models

摘要

In this paper, we propose a variational framework for tomographic reconstruction of dynamic objects with unknown dynamic models. This is an extension of our previous work on dynamic tomography using curve evolution methods where the shape dynamics are known a priori. We assume the dynamic model of the shape is a parameterized affine transform and propose a variational framework that incorporates information from observed data, intensity dynamics, spatial smoothness prior, and the dynamical shape model. A coordinate descent algorithm based on a curve evolution method is then proposed for the joint estimation of .the intensities, object boundary sequences, and the unknown dynamic model parameters. For implementation of the curve evolution and parameter estimation process, we use efficient level set methods.