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    "https://doi.org/10.48550/arXiv.2011.02322",
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    "https://doi.org/10.48550/arXiv.2011.02322",
            
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      "name": "magnetic-resonance-imaging",
            
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      "title": "Magnetic Resonance Imaging"
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      "title": "Magnetic Resonance Imaging"
            
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      "name": "medical-imaging",
            
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  "name": "fastmri",
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  "name": "fastmri",
            
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  "notes": "Fast and accurate reconstruction of magnetic resonance 
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  "notes": "Fast and accurate reconstruction of magnetic resonance 
            
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(MR) images from under-sampled data is important in many clinical 
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(MR) images from under-sampled data is important in many clinical 
            
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applications. In recent years, deep learning-based methods have been 
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applications. In recent years, deep learning-based methods have been 
            
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shown to produce superior performance on MR image reconstruction. 
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shown to produce superior performance on MR image reconstruction. 
            
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However, these methods require large amounts of data which is 
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However, these methods require large amounts of data which is 
            
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difficult to collect and share due to the high cost of acquisition and 
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difficult to collect and share due to the high cost of acquisition and 
            
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medical data privacy regulations.",
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medical data privacy regulations.",
            
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233
    }
            
193
  ],
234
  ],
            
194
  "title": "FastMRI",
235
  "title": "FastMRI",
            
195
  "type": "dataset",
236
  "type": "dataset",
            
196
  "version": ""
237
  "version": ""
            
197
}
238
}