In this work, we design a deep learning approach to MRF using a fully connected network (FCN). Neural networks (NNs) have been proposed as a feasible alternative, but this approach is still in its infancy. A typical drawback of dictionary-based MRF is an explosion of the dictionary size as a function of the number of reconstructed parameters, according to the "curse of dimensionality", which determines an explosion of resource requirements. Magnetic resonance fingerprinting (MRF) is a rapidly developing approach for fast quantitative MRI.
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