  Two algorithms for solving misalignment issues in penalized PET/CT
reconstruction using anatomical priors are proposed. Both approaches are based
on a recently published joint motion estimation and image reconstruction
method. The first approach deforms the anatomical image to align it with the
functional one while the second approach deforms both images to align them with
the measured data. Our current implementation alternates between image
reconstruction and alignment estimation. To evaluate the potential of these
approaches, we have chosen Parallel Level Sets (PLS) as a representative
anatomical penalty, incorporating a spatially-variant penalty strength to
achieve uniform local contrast. The performance was evaluated using simulated
non-TOF data generated with an XCAT phantom in the thorax region. We used the
attenuation image in the anatomical prior. The results demonstrated that both
methods can estimate the misalignment and deform the anatomical image
accordingly. However, the performance of the first approach depends highly on
the workflow of the alternating process. The second approach shows a faster
convergence rate to the correct alignment and is less sensitive to the
workflow. Interestingly, the presence of anatomical information can improve the
convergence rate of misalignment estimation for the second approach but slow it
down for the first approach.
