A Nonlinear Variational Approach to Motion-Corrected Reconstruction of Density Images
This work addresses the challenging problem of motion-corrected image reconstruction for dynamic imaging modalities like PET, offering a theoretically grounded variational framework.
The paper proposes a nonlinear variational model for reconstructing moving density images from indirect dynamic measurements, incorporating hyperelastic deformation and mass preservation. It proves existence of a minimizer and demonstrates potential improvements over conventional methods in dynamic PET examples.
The aim of this paper is to establish a nonlinear variational approach to the reconstruction of moving density images from indirect dynamic measurements. Our approach is to model the dynamics as a hyperelastic deformation of an initial density including preservation of mass. Consequently we derive a variational regularization model for the reconstruction, which - besides the usual data fidelity and total variation regularization of the images - also includes a motion constraint and a hyperelastic regularization energy. Under suitable assumptions we prove the existence of a minimizer, which relies on the concept of weak diffeomorphisms for the motion. Moreover, we study natural parameter asymptotics and regularizing properties of the variational model. Finally, we develop a computational solution method based on alternating minimization and splitting techniques, with a particular focus on dynamic PET. The potential improvements of our approach compared to conventional reconstruction techniques are investigated in appropriately designed examples.