Accuracy of motion correction methods for PET brain imaging

Recently published methods for motion correction in neurological PET include the multiple acquisition frame (MAF) and LOR rebinning methods. The aim of the present work was to compare the accuracy of reconstructions obtained with these methods when multiple, arbitrary movements were applied to a Hoffman brain phantom during 3D list mode acquisition. A reflective target attached to the phantom enabled a Polaris optical motion tracking system to monitor the phantom position and orientation in the scanner coordinate frame. The motion information was used in the motion correction algorithms. The MAF method was applied to the list-mode data after sorting them into a series of dynamic frames, while the LOR rebinning method was applied directly to the list-mode data. A proportion of the list mode events had to be discarded during rebinning because the application of the corrective spatial transformation removed them from the 3D projection space. A correction for these ´lost´ events was implemented as a global post-reconstruction scale factor, based on the overall fraction of lost events. Reconstructions from both motion correction methods were compared with a motion-free reference scan of the same phantom. Motion correction produced a marked improvement in image clarity and reduced errors with respect to the reference scan. LOR rebinning with global loss correction was found to be more accurate than the MAF method