The exponential growth in the number of dictionary entries with increasing dictionary dimensions places a practical limit on the number of tissue parameters that may be simultaneously reconstructed. While a sparse sampling of some dimensions can mitigate the problem it also introduces significant errors into the reconstruction. In this work we demonstrate that Deep Learning methods can be used to train a compact neural network with sparse dictionaries without penalty on the reconstruction accuracy.
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