  This paper examines a combined supervised-unsupervised framework involving
dictionary-based blind learning and deep supervised learning for MR image
reconstruction from under-sampled k-space data. A major focus of the work is to
investigate the possible synergy of learned features in traditional shallow
reconstruction using adaptive sparsity-based priors and deep prior-based
reconstruction. Specifically, we propose a framework that uses an unrolled
network to refine a blind dictionary learning-based reconstruction. We compare
the proposed method with strictly supervised deep learning-based reconstruction
approaches on several datasets of varying sizes and anatomies. We also compare
the proposed method to alternative approaches for combining dictionary-based
methods with supervised learning in MR image reconstruction. The improvements
yielded by the proposed framework suggest that the blind dictionary-based
approach preserves fine image details that the supervised approach can
iteratively refine, suggesting that the features learned using the two methods
are complementary
