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Time-resolved angiography with interleaved stochastic trajectories (TWIST)has been widely used for dynamic contrast enhanced MRI (DCE-MRI). To achievehighly accelerated acquisitions, TWIST combines the periphery of the k-spacedata 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 aspecific generalized autocalibrating partial parallel acquisition (GRAPPA)factor so that it is not possible to reduce the number of view-sharing once thek-data is acquired. To address these issues, this paper proposes a novelk-space deep learning approach for parallel MRI. In particular, we havedesigned our neural network so that accurate k-space interpolations areperformed simultaneously for multiple coils by exploiting the redundanciesalong the coils and images. Reconstruction results using in vivo TWIST data setconfirm that the proposed method can immediately generate high-qualityreconstruction results with various choices of view- sharing, allowing us toexploit the trade-off between spatial and temporal resolution in time-resolvedMR angiography.

k-Space Deep Learning for Parallel MRI: Application to Time-Resolved MR Angiography