  Time-resolved angiography with interleaved stochastic trajectories (TWIST)
has been widely used for dynamic contrast enhanced MRI (DCE-MRI). To achieve
highly accelerated acquisitions, TWIST combines the periphery of the k-space
data from several adjacent frames to reconstruct one temporal frame. However,
this view-sharing scheme limits the true temporal resolution of TWIST.
Moreover, the k-space sampling patterns have been specially designed for a
specific generalized autocalibrating partial parallel acquisition (GRAPPA)
factor so that it is not possible to reduce the number of view-sharing once the
k-data is acquired. To address these issues, this paper proposes a novel
k-space deep learning approach for parallel MRI. In particular, we have
designed our neural network so that accurate k-space interpolations are
performed simultaneously for multiple coils by exploiting the redundancies
along the coils and images. Reconstruction results using in vivo TWIST data set
confirm that the proposed method can immediately generate high-quality
reconstruction results with various choices of view- sharing, allowing us to
exploit the trade-off between spatial and temporal resolution in time-resolved
MR angiography.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning;Machine Learning