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Scan-Specific Accelerated Mri Reconstruction Using Recurrent Neural Networks In A Regularized Self-Consistent Framework.

, , , , , and . ISBI Workshops, page 1-4. IEEE, (2020)

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Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction., , , , , , and . CoRR, (2019)Self-Supervised Learning of Physics-Based Reconstruction Neural Networks without Fully-Sampled Reference Data., , , , , and . CoRR, (2019)High-Quality 0.5mm Isotropic fMRI: Random Matrix Theory Meets Physics-Driven Deep Learning., , , , , , , and . NER, page 1-6. IEEE, (2023)Automated Acquisition Planning for Magnetic Resonance Spectroscopy in Brain Cancer., , , , , , , , , and . MICCAI (7), volume 12267 of Lecture Notes in Computer Science, page 730-739. Springer, (2020)Efficient Training of 3D Unrolled Neural Networks for MRI Reconstruction Using Small Databases., , , , and . ACSCC, page 886-889. IEEE, (2021)Zero-Shot Self-Supervised Learning for MRI Reconstruction., , and . ICLR, OpenReview.net, (2022)Highly-Accelerated High-Resolution Multi-Echo fMRI Using Self-Supervised Physics-Driven Deep Learning Reconstruction., , , , , , and . CAMSAP, page 196-200. IEEE, (2023)Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms., , , , and . IEEE J. Sel. Top. Signal Process., 14 (6): 1280-1291 (2020)Dense Recurrent Neural Networks for Inverse Problems: History-Cognizant Unrolling of Optimization Algorithms., , , , and . CoRR, (2019)New Conditions for Sparse Phase Retrieval., and . CoRR, (2013)