Coupled Motion and Activity Estimation from PET and MR Data with Motion Model-Based Parameter Reduction

We propose a technique that estimates both the image and the respiratory motion states of all PET gates during PET reconstruction. This is done using a gradient analytically-derived from the Poisson log-likelihood, and involves a parameterised motion model. This is incorporated into reconstruction using the motion-compensated image reconstruction (MCIR) framework, coupled with optimisation of the analytical gradient. The use of the motion model in this way reduces the number of additional parameters to be optimised for motion correction to one per PET gate. This was tested using a 3D PET simulation derived from segmented UTE MR image volumes, warped using real MR-derived motion measurements.