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Abstract #3456

Combining Supervised and semi-Blind Dictionary (Super-BReD) Learning for MRI Reconstruction

Anish Lahiri1, Saiprasad Ravishankar2, and Jeffrey A Fessler1
1Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, United States, 2Computational Mathematics, Science and Engineering, and Biomedical Engineering, Michigan State University, East Lansing, MI, United States

Regularization in MRI reconstruction often involves sparse representation of signals using linear combinations of dictionary atoms. In 'blind' settings, these dictionaries are learned during reconstruction from the corrupt/aliased images, using no training data. In contrast, 'Fully supervised' dictionary learning (DL) requires uncorrupted/fully sampled training images, and the learned dictionary is used to regularize image reconstruction from undersampled data. We combine the aforementioned DL frameworks to learn two separate dictionaries in a residual fashion to jointly reconstruct an undersampled image. Our algorithm, Super-BReD Learning, shows promising results on reconstruction from retrospectively undersampled data, and outperforms recent DL schemes.

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