Statistical evaluation of PET motion correction methods using MR derived motion fields

Abstract

Although there have been various proposed methods for motion correction in PET, there is not sufficient evidence to answer which method is better in terms of image quality and quantification. This study aims to characterize the behavior of the two main motion correction methods in terms of convergence and image properties. During the first method, reconstruct-transform-average (RTA), independent reconstructions of each gate are transformed to a reference gate and averaged. In the second method, motion-compensated image reconstruction (MCIR), all data are used with the motion information within a single reconstruction. The two methods are studied based on the OSEM algorithm. In this investigation motion fields were obtained from real dynamic MR acquisitions and concurrent PET data were simulated from the dynamic MR images. The two motion correction approaches were assessed statistically. Results indicate that MCIR is successful in the recovery of the true values of all regions, whereas RTA has high bias due to the non-negativity constraints and interpolation errors during transformation. It has been demonstrated that whilst at low iterations (i.e. 46 sub-iterations) the two methods have similar noise characteristics; at high iterations MCIR has increased noise. Finally, for low iterations the two methods have marginally similar coefficient of variation (CoV) whereas for large number of iterations RTA has clearly better CoV than MCIR. This study indicates that MCIR may provide superior performance overall to RTA providing the noise can be minimized.