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Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms
Inverse problems for accelerated MRI typically incorporate domain-specific knowledge about the forward encoding operator in a regularized reconstruction framework. Recently physics-driven deep learning (DL) methods have been proposed to use neural networks for data-driven regularization. These metho...
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| Publicat a: | IEEE J Sel Top Signal Process |
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| Autors principals: | , , , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
2020
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7978039/ https://ncbi.nlm.nih.gov/pubmed/33747334 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/jstsp.2020.3003170 |
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