Motion compensation is effective for reducing motion blur in cardiac gated imaging. In this work, we investigate the potential benefit of incorporating an anatomical similarity measure in training a deep learning (DL) network for motion compensation on cardiac gated SPECT images, which are known to suffer from limited data counts and exhibit image intensity distortion (due to partial-volume effect) associated with cardiac motion. In this similarity measure we utilize the spatial image gradient to characterize the correspondence of boundary points on the left-ventricular wall between two gate frames. In the experiment we demonstrated this approach on a set of 197 clinical acquisitions, and the results show that with the proposed approach the DL network can improve the anatomical similarity among the gate frames upon motion compensation.