PET Image Reconstruction with Correction for Non-periodic Deformable Motion

Image reconstruction techniques that use rectangular basis functions (pixels and voxels) may not be optimal when non-periodic, deformable motion correction is required. Here we propose a new approach to PET image reconstruction and non-rigid motion correction that is based on representing the imaged objects with regularized, spatially bounded sets of disconnected points. Motion correction is performed by explicitly incorporating the object motion into the reconstruction algorithm, though the dynamically adjusted coordinates of the points. Within the proposed approach, the images are reconstructed iteratively in list-mode, and the system matrix calculation is based on the localized estimation of the probabilistic weights for every point in the generated point set, using an optimized point search algorithm. To validate the motion correction, a digital phantom of a freely moving mouse was generated using mesh deformation operators such as armatures and curve modifiers. From the simulated PET list-mode data and a priori known motion trajectory, we reconstructed 3D images corrected for deformable, non-periodic motion without using the traditional gate-based methods. In addition, the stability of the reconstructed images with respect to the point set parameters and deformations was investigated.