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Abstract #0279

Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction

Anish Lahiri1, Guanhua Wang2, Saiprasad Ravishankar3, and Jeffrey A. Fessler1
1Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, United States, 2Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, 3Computational Mathematics, Science and Engineering, and Biomedical Engineering, Michigan State University, East Lansing, MI, United States

This work 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 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.

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