Integrating Spatial and Temporal Correlations into a Deep Neural Network for Low-delay Reconstruction of Highly Undersampled Radial Dynamic Images

This paper proposes a novel method for the reconstruction of dynamic images from highly undersampled radial k-space data. In order to take advantage of spatial and temporal correlations and reducing the reconstruction time delay, a deep neural network (DNN) was trained with additional input images displaying the aforementioned correlations. It is shown that the image quality from the proposed method is superior to that of the method based on the conventional DNN reconstruction scheme from a single input to a single output.