Approximate 4D Reconstruction of Cardiac-Gated Spect Images Using a Residual Convolutional Neural Network

4D image reconstruction can significantly improve the image quality in cardiac-gated imaging using single-photon emission computed tomography (SPECT). However, it is also associated with increased computation complexity which prevents it from being widely used in the clinic. In this study, we investigate a post-processing approach for cardiac-gated SPECT images by using a 3D residual convolutional neural network (CNN). In our formulation, the network is trained to produce images that are comparable to 4D reconstruction. In the experiments, we demonstrated this approach on a set of 197 clinical acquisitions. The results show that the proposed CNN approach can effectively suppress the noise level in the reconstructed myocardium. It also outperforms two alternative post-processing methods, including a non-local means (NLM) filter previously developed for gated SPECT images.