Cycle-Consistent Self-Supervised Learning for Improved Highly-Accelerated MRI Reconstruction
Author:
Affiliation:
1. University of Minnesota,Electrical and Computer Engineering,Minneapolis,MN,United States
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635895.pdf?arnumber=10635895
Reference25 articles.
1. Learning a variational network for reconstruction of accelerated MRI data
2. A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
3. MoDL: Model-Based Deep Learning Architecture for Inverse Problems
4. Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues
5. Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms
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