  Time-resolved MR angiography (tMRA) has been widely used for dynamic contrast
enhanced MRI (DCE-MRI) due to its highly accelerated acquisition. In tMRA, the
periphery of the k-space data are sparsely sampled so that neighbouring frames
can be merged to construct one temporal frame. However, this view-sharing
scheme fundamentally limits the temporal resolution, and it is not possible to
change the view-sharing number to achieve different spatio-temporal resolution
trade-off. Although many deep learning approaches have been recently proposed
for MR reconstruction from sparse samples, the existing approaches usually
require matched fully sampled k-space reference data for supervised training,
which is not suitable for tMRA. This is because high spatio-temporal resolution
ground-truth images are not available for tMRA. To address this problem, here
we propose a novel unsupervised deep learning using optimal transport driven
cycle-consistent generative adversarial network (cycleGAN). In contrast to the
conventional cycleGAN with two pairs of generator and discriminator, the new
architecture requires just a single pair of generator and discriminator, which
makes the training much simpler and improves the performance. Reconstruction
results using in vivo tMRA data set confirm that the proposed method can
immediately generate high quality reconstruction results at various choices of
view-sharing numbers, allowing us to exploit better trade-off between spatial
and temporal resolution in time-resolved MR angiography.
