Time-resolved angiography with interleaved stochastic trajectories(TWIST) has been widely used for dynamic contrast enhanced (DCE) MRI. To achieve highly accelerated acquisitions for improved temporal and spatial resolution, the high frequency region is randomly sub-sampled at each time frame. Therefore, the periphery of the k-space data from multiple time frames are combined to obtain the uniformly sub-sampled k-space data so that the temporal resolution of TWIST is limited. The purpose of this research is to improve the temporal resolution of TWIST by reducing the view-sharing. Furthermore, we proposed the algorithm that can reconstruct the imagesat various number of view sharing using k-space deep learning.
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