Evaluation of two PET motion correction techniques for simultaneous real-time PET-MR acquisitions using an MR-derived motion model

Respiratory motion during PET imaging causes the resulting images to become corrupted by artifacts. Respiratory motion models have found application in estimating and correcting for the effects of motion in a wide range of applications. In this work we evaluate the performance of an MR-derived motion model for motion-correction of PET data that is suitable for use in a simultaneous PET-MR imaging system. The technique is based on the formation of a subject-specific respiratory motion model from near real-time dynamic MR images and is capable of making real-time motion estimates based on an MR navigator signal. This approach has an advantage over gating based approaches in that individual motion corrections can be applied at each time point, avoiding the need to combine large amounts of PET data for correction by a single transformation for each gate. In our investigation, we evaluate the effects of the MR-derived motion model and PET motion compensation using dynamic PET simulations based on dynamic MR data (105 real-time frames, 0.7s each) of the thorax acquired from a healthy volunteer. This large amount of data, acquired during different breathing patterns, allows us to more accurately simulate the complex respiratory motion variation that is observed in real PET acquisitions. PET images are corrected for motion using two approaches: reconstruct-transform-average (RTA) and motion compensated image reconstruction (MCIR). We compare the methods in terms of quantification and visualization of three tumors that were artificially added to the emission and attenuation maps of the volunteer close to the diaphragm. It is demonstrated that our motion model can be used to correct successfully for motion in real-time dynamic PET-MR acquisitions. The correction is feasible using either RTA or MCIR, although the latter provides