k-Space Deep Learning for Parallel MRI: Application to Time-Resolved MR Angiography
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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. In addition, since the k-space sampling patterns have been specially designed for a specific generalized autocalibrating partial parallel acquisition (GRAPPA) factor, it is not possible to reduce the number of views in order to reconstruct images with a better temporal resolution. To address these issues, this paper proposes a novel k-space deep learning approach for parallel MRI. In particular, inspired by the recent mathematical discovery that links Hankel matrix decomposition to deep learning, we have implemented our neural network so that accurate k-space interpolations are performed simultaneously for multiple coils by exploiting the redundancies along the coils and images. In addition, the proposed method can immediately generate reconstruction results with different numbers of view-sharing, allowing us to exploit the trade-off between spatial and temporal resolution. Reconstruction results using in vivo TWIST data set confirm the accuracy and the flexibility of the proposed method.
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