Recent developments in three-dimensional (3D) PET systems have enabled the spatial resolution to reach the 2- to 5-mm full-width-at-half-maximum (FWHM) range. With such improvements in spatial resolution, even small amounts of motion during PET imaging become a significant source of resolution degradation. In other words, increased spending on new-generation scanners can be fully justified only when appropriate motion correction methods are considered, to achieve the true resolution of the scanner. Motion correction methods developed for single photon emission CT (SPECT) are not necessarily applicable to PET because they may rely on the time-dependence of projections in SPECT (due to a rotating head or heads), which is not the case in PET. Nevertheless, a number of other methods implemented in SPECT are equally applicable to PET. In this work has been broadly categorized into the review and discussion of advanced correction methods for the cases of unwanted patient motion, motion due to cardiac cycles, and motion due to respiratory cycles. After reviewing some current methods, the model is introduced which was developed with the help of NCAT phantom and Sim SET. Two phantoms were extracted, male and female, from NCAT to see the differences between the results with the changes in the anatomy of these two phantoms. Then PET images were produced using Sim SET for all the phantoms available (with respiratory motion and without respiratory motion and for respiratory cycles of 4, 5 and 6 seconds for both male and female phantoms). The new model is introduced which is designed based on the respiratory cycle 5 seconds, using wavelet transforms. This model can track and compensate motion due to respiration. The results show that for the first frame and the last one because of very smooth and slight motions the images with motion are not that different from the images without motion, so the proposed model is not responding better than the images with motion. However, for the rest of the frames the model provides better images compare to the images with motion. Comparing to other methods, this model not only provides a good estimation for motion but also it doesn’t include the errors caused by markers and monitoring systems.