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J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction
Modern MRI schemes, which rely on compressed sensing or deep learning algorithms to recover MRI data from undersampled multichannel Fourier measurements, are widely used to reduce the scan time. The image quality of these approaches is heavily dependent on the sampling pattern. We introduce a contin...
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| Gepubliceerd in: | IEEE J Sel Top Signal Process |
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| Hoofdauteurs: | , |
| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
2020
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| Onderwerpen: | |
| Online toegang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7893809/ https://ncbi.nlm.nih.gov/pubmed/33613806 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/jstsp.2020.3004094 |
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