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Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction.


ABSTRACT: 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.

SUBMITTER: Lahiri A 

PROVIDER: S-EPMC8672324 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction.

Lahiri Anish A   Wang Guanhua G   Ravishankar Saiprasad S   Fessler Jeffrey A JA  

IEEE transactions on medical imaging 20211027 11


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-b  ...[more]

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