This research aims to develop a new feature guided motion estimation method for the left ventricular wall in gated cardiac imaging. The guiding feature is the "footprint" of one of the papillary muscles, which is the attachment of the papillary muscle on the endocardium. Myocardial perfusion (MP) PET images simulated from the 4-D XCAT phantom, which features papillary muscles, realistic cardiac motion with known motion vector field (MVF), were employed in the study. The 4-D MVF of the heart model of the XCAT phantom was used as a reference. For each MP PET image, the 3- D "footprint" surface of one of the papillary muscles was extracted and its centroid was calculated. The motion of the centroid of the "footprint" throughout a cardiac cycle was tracked and analyzed in 4-D. This motion was extrapolated to throughout the entire heart to build a papillary muscle guided initial estimation of the 4-D cardiac MVF. A previous motion estimation algorithm was applied to the simulated gated myocardial PET images to estimate the MVF. Three different initial MVF estimates were used in the estimation, including zero initial (0-initial), the papillary muscle guided initial (P-initial), and the true MVF from phantom (T-initial). Qualitative and quantitative comparison between the estimated MVFs and the true MVF showed the P-initial provided more accurate motion estimation in longitudinal motion than the 0-initial with over 70% improvement and comparable accuracy with that of the T-initial. We concluded that when the footprint can be tracked accurately, this feature guided approach will significantly improve the accuracy and robustness of traditional optical flow based motion estimation method.