Temporal Regularization for Robust Motion Compensation in Reduced Dose Cardiac-Gated Spect Images

Motion compensation is an effective approach for reducing motion blur and suppressing noise in cardiac gated imaging. In this work, we propose to introduce a temporal regularization measure in optimization of a deep learning (DL) network for motion compensation in cardiac-gated SPECT images. This introduced measure is used to exploit the temporal consistency in the physical motion of the myocardium among consecutive phases of the cardiac cycle. In the experiment we demonstrated this approach on a set of 197 clinical acquisitions with imaging dose reduced by $50 \%$. The results demonstrate that the proposed approach can lead to improved motion compensation accuracy among all individual gates by the DL network, with an average reduction by $6 \%$ in the mean-squared-error of the motioncompensated myocardium, indicating that the proposed approach can be more robust in combating the excessive noise level in reduced dose imaging.