MPRAGE imaging has been widely used in clinical applications and various attempts have been made for its acceleration. This paper presents a new method to accelerate MPRAGE imaging using sparse and random sampling of k-space and constrained reconstruction incorporating image priors and subject-specific novel features. In our current implementation, the MPRAGE image priors were obtained using deep learning on data from the Human Connectome Project, and novel localized features were recovered by solving a sparsity-constrained reconstruction. In vivo experimental results demonstrated that the proposed method can produce high-quality whole-brain MPRAGE images in 0.7x0.7x0.7 mm3 nominal resolution from a 1.5-min scan.
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