  In this work we reduce undersampling artefacts in two-dimensional ($2D$)
golden-angle radial cine cardiac MRI by applying a modified version of the
U-net. We train the network on $2D$ spatio-temporal slices which are previously
extracted from the image sequences. We compare our approach to two $2D$ and a
$3D$ Deep Learning-based post processing methods and to three iterative
reconstruction methods for dynamic cardiac MRI. Our method outperforms the $2D$
spatially trained U-net and the $2D$ spatio-temporal U-net. Compared to the
$3D$ spatio-temporal U-net, our method delivers comparable results, but with
shorter training times and less training data. Compared to the Compressed
Sensing-based methods $kt$-FOCUSS and a total variation regularised
reconstruction approach, our method improves image quality with respect to all
reported metrics. Further, it achieves competitive results when compared to an
iterative reconstruction method based on adaptive regularization with
Dictionary Learning and total variation, while only requiring a small fraction
of the computational time. A persistent homology analysis demonstrates that the
data manifold of the spatio-temporal domain has a lower complexity than the
spatial domain and therefore, the learning of a projection-like mapping is
facilitated. Even when trained on only one single subject without
data-augmentation, our approach yields results which are similar to the ones
obtained on a large training dataset. This makes the method particularly
suitable for training a network on limited training data. Finally, in contrast
to the spatial $2D$ U-net, our proposed method is shown to be naturally robust
with respect to image rotation in image space and almost achieves
rotation-equivariance where neither data-augmentation nor a particular network
design are required.
