MR Fingerprinting offers the ability to obtain simultaneous tissue (T1,T2…) and hardware (B1, B0…) parameter maps in a fast acquisition time but is limited by the size of the reconstruction dictionary. In previous work we demonstrated that these issues can be overcome by reconstructing the data using a properly trained neural network. Here we characterize the accuracy of a neural network trained on sparse dictionaries for reconstruction of noisy data.
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