Iterative segmentation and motion correction for dynamic PET images based on radioactive tracer kinetics

We propose a novel motion correction approach for dynamic emission tomography images that takes advantage of the underlying compartmental models of tracer kinetics. Our algorithm uses a simultaneous segmentation registration paradigm. The key idea of our approach is that, unlike the standard frame-by-frame (FbF) based registration methods, we avoid choosing a reference frame and essentially create a reference frame for each time step. The references are generated by assigning time activity curves based on the estimated kinetic-modeling parameters to the different regions of the segmentation output. We evaluate the goodness of our method compared with the FbF-based registration approach using medical images with known motion corruption parameters. The results indicate superior performance of our method in terms of the accurate estimation of the motion and the kinetic parameters.