Recently, convolutional neural network (CNN) based fast cardiac image reconstruction techniques have shown the potential to produce rapid, high quality reconstructions from under-sampled data. However, the relationship between the k-space sampling strategy, image content, training process and reconstruction performance has not been extensively studied. To address this, our study trained different CNN based cardiac image reconstruction models for different image content and various sampling patterns. We showed that better reconstruction results were achieved when using mixed image content as training data and distributing more energy at the center of k-space. Radial acquisition showed the lowest RMSE suggesting potential improvement of CNN performance with non-Cartesian acquisition.