A Convex Compressibility-Inspired Unsupervised Loss Function for Physics-Driven Deep Learning Reconstruction
Author:
Affiliation:
1. University of Minnesota,Electrical and Computer Engineering,Minneapolis,Minnesota,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635138.pdf?arnumber=10635138
Reference19 articles.
1. Learning a variational network for reconstruction of accelerated MRI data
2. Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction
3. MoDL: Model-Based Deep Learning Architecture for Inverse Problems
4. Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging
5. Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms
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